Difference between revisions of "Ghost Cells"

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==== Tony ====
 
==== Tony ====
 
Subject: Jacobi's method for Poisson's equation
 
Subject: Jacobi's method for Poisson's equation
 +
===== Source Code =====
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! poissan.h
 +
|-
 +
|
 +
<source>
 +
#ifndef POISSON_H
 +
#define POISSON_H
 +
#include <fstream>
 +
 +
namespace DPS{
 +
    class Poisson {
 +
        size_t nRowsTotal;
 +
        size_t nColumns;
 +
        float* data;
 +
        int bufferSide;
 +
 +
        void update (size_t startRow, size_t endRow, const float wx, const float wy);
 +
        void bufferSwitch(){ bufferSide = 1 - bufferSide; };
 +
 +
        public:
 +
        Poisson(std::ifstream& ifs);
 +
        Poisson(const size_t r, const size_t c, float* d);
 +
        ~Poisson(){ delete[] data; };
 +
        float* operator()(const size_t iteration, const float wx, const float wy);
 +
        float* operator()(const size_t iteration){
 +
            return operator()(iteration,0.1,0.1);
 +
        }
 +
        void show(std::ostream& ofs) const;
 +
    };
 +
}
 +
#endif
 +
 +
</source>
 +
|}
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! poissan.cpp
 +
|-
 +
|
 +
<source>
 +
#include <cstring>
 +
#include <cstdlib>
 +
#include <iomanip>
 +
#include <iostream>
 +
#include <string>
 +
#include "poisson.h"
 +
 +
namespace DPS{
 +
    Poisson::Poisson(std::ifstream& ifs){
 +
        std::string line;
 +
        bufferSide = 0;
 +
 +
        /* find number of columns */
 +
        std::getline(ifs,line);
 +
        for (size_t i = 0 ; i < line.size() ; i++){
 +
            if(line[i]==' ') nColumns++;
 +
        }
 +
        nColumns++;
 +
 +
        /* find number of rows */
 +
        nRowsTotal++; /* already fetched one */
 +
        while(std::getline(ifs,line))
 +
            nRowsTotal++;
 +
        ifs.clear();
 +
 +
        try{
 +
            data = new float[nColumns * nRowsTotal * 2];
 +
        }
 +
        catch (...){
 +
            throw std::runtime_error("Failed to Allocate Memory");
 +
        }
 +
 +
        /* readin data */
 +
        ifs.seekg(0,ifs.beg);
 +
        std::cout << ifs.tellg() << std::endl;
 +
        for (size_t i = 0 ; i < nRowsTotal * nColumns ; i++) {
 +
            ifs >> data[i];
 +
        }
 +
 +
        std::memset(data+nRowsTotal*nColumns,0,nRowsTotal*nColumns*sizeof(float));
 +
    }
 +
 +
    Poisson::Poisson(const size_t r, const size_t c, float* d){
 +
        bufferSide = 0;
 +
        nRowsTotal = r;
 +
        nColumns = c;
 +
        try{
 +
            data = new float[r*c*2];
 +
        }
 +
        catch (...){
 +
            throw std::runtime_error("Failed to Allocate Memory");
 +
        }
 +
        std::memcpy(data,d,r*c*sizeof(float));
 +
        std::memset(data+r*c,0,r*c*sizeof(float));
 +
    }
 +
 +
    void Poisson::update (size_t startRow, size_t endRow, const float wx, const float wy){
 +
        float* x_new = data + (1-bufferSide)*nRowsTotal*nColumns;
 +
        float* x_old = data + bufferSide*nRowsTotal*nColumns;
 +
        for (size_t i = startRow; i <= endRow; i++)
 +
            for (size_t j = 1; j < nColumns - 1; j++)
 +
                x_new[i * nColumns + j] = x_old[i * nColumns + j]
 +
                    + wx * (x_old[(i + 1) * nColumns + j] + x_old[(i - 1) * nColumns + j]
 +
                            - 2.0f * x_old[i * nColumns + j])
 +
                    + wy * (x_old[i * nColumns + j + 1] + x_old[i * nColumns + j - 1]
 +
                            - 2.0f * x_old[i * nColumns + j]);
 +
    }
 +
 +
    float* Poisson::operator()(const size_t nIterations, const float wx, const float wy){
 +
        for (size_t i = 0; i < nIterations; i++) {
 +
            update(0, nRowsTotal-1, wx, wy);
 +
            bufferSwitch();
 +
        }
 +
        return data;
 +
    }
 +
 +
    void Poisson::show(std::ostream& ofs) const{
 +
        ofs << std::fixed << std::setprecision(1);
 +
        for (size_t j = 0; j < nColumns ; j++) {
 +
            for (size_t i = 0 ; i < nRowsTotal ; i++)
 +
                ofs << std::setw(8) << data[ bufferSide*nColumns*nRowsTotal + i * nColumns + j];
 +
            ofs << std::endl;
 +
        }
 +
    }
 +
}
 +
 +
</source>
 +
|}
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! main.cpp
 +
|-
 +
|
 +
<source>
 +
// based on code from LLNL tutorial mpi_heat2d.c
 +
// Master-Worker Programming Model
 +
// Chris Szalwinski - 2018/11/13
 +
// Adopted by Tony Sim - 2019/02/16
 +
#include <iostream>
 +
#include <fstream>
 +
#include <iomanip>
 +
#include <cstdlib>
 +
#include <stdexcept>
 +
 +
#include "poisson.h"
 +
 +
// solution constants
 +
const size_t NONE = 0;
 +
const size_t MINPARTITIONS = 1;
 +
const size_t MAXPARTITIONS = 7;
 +
// weights
 +
const float wx = 0.1f;
 +
const float wy = 0.1f;
 +
 +
int main(int argc, char** argv) {
 +
    if (argc != 4) {
 +
        std::cerr << "*** Incorrect number of arguments ***\n";
 +
        std::cerr << "Usage: " << argv[0]
 +
            << " input_file output_file no_of_iterations\n";
 +
        return 1;
 +
    }
 +
 +
    std::ifstream input(argv[1]);
 +
    std::ofstream output(argv[2]);
 +
    std::ofstream temp("init.csv");
 +
 +
    if(!input.is_open()){
 +
        std::cerr << "Invalid Input File" << std::endl;
 +
        return 2;
 +
    }
 +
    if(!output.is_open()){
 +
        std::cerr << "Invalid Output File" << std::endl;
 +
        return 2;
 +
    }
 +
 +
    DPS::Poisson* p = nullptr;
 +
    try{
 +
        p = new DPS::Poisson(input);
 +
    }
 +
    catch(std::exception& e){
 +
        std::cerr << "Error: " << e.what() << std::endl;
 +
    }
 +
 +
    p->show(temp);
 +
 +
    size_t nIterations = std::atoi(argv[3]);
 +
 +
    (*p)(nIterations);
 +
 +
    // write results to file
 +
    p->show(output);
 +
 +
    delete p;
 +
 +
}
 +
 +
</source>
 +
|}
 +
===== Introduction =====
 +
The presented code simulates heat map using Jacobi's method for Poisson's equation. It is represented in a 2D array, and each element updates its value based on the adjacent elements at a given moment.  Each iteration represent one instance in time.  By repeating the calculation over the entire array through multiple iterations, we can estimate the state of the heat transfer after a given time interval.
 +
 +
===== Profiling =====
 +
The profiling was conducted using a data set of 79 rows and 205 columns over 150000 iterations.
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! Flat profile
 +
|-
 +
|
 +
 +
Flat profile:
 +
 +
Each sample counts as 0.01 seconds.
 +
  %  cumulative  self              self    total         
 +
time  seconds  seconds    calls  us/call  us/call  name   
 +
98.57      2.75    2.75  150000    18.33    18.33  DPS::Poisson::update(unsigned long, unsigned long, float, float)
 +
  0.00      2.75    0.00        1    0.00    0.00  _GLOBAL__sub_I__ZN3DPS7PoissonC2ERSt14basic_ifstreamIcSt11char_traitsIcEE
 +
  0.00      2.75    0.00        1    0.00    0.00  _GLOBAL__sub_I_main
 +
 +
 +
|}
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! Call graph
 +
|-
 +
|
 +
                        Call graph
 +
 +
 +
granularity: each sample hit covers 2 byte(s) for 0.36% of 2.75 seconds
 +
 +
index % time    self  children    called    name
 +
                2.75    0.00  150000/150000      DPS::Poisson::operator()(unsigned long, float, float) [2]
 +
[1]    100.0    2.75    0.00  150000        DPS::Poisson::update(unsigned long, unsigned long, float, float) [1]
 +
-----------------------------------------------
 +
                                                <spontaneous>
 +
[2]    100.0    0.00    2.75                DPS::Poisson::operator()(unsigned long, float, float) [2]
 +
                2.75    0.00  150000/150000      DPS::Poisson::update(unsigned long, unsigned long, float, float) [1]
 +
-----------------------------------------------
 +
                0.00    0.00      1/1          __libc_csu_init [21]
 +
[10]    0.0    0.00    0.00      1        _GLOBAL__sub_I__ZN3DPS7PoissonC2ERSt14basic_ifstreamIcSt11char_traitsIcEE [10]
 +
-----------------------------------------------
 +
                0.00    0.00      1/1          __libc_csu_init [21]
 +
[11]    0.0    0.00    0.00      1        _GLOBAL__sub_I_main [11]
 +
-----------------------------------------------
 +
 +
 +
Index by function name
 +
 +
  [10] _GLOBAL__sub_I__ZN3DPS7PoissonC2ERSt14basic_ifstreamIcSt11char_traitsIcEE (poisson.cpp) [11] _GLOBAL__sub_I_main (main.cpp) [1] DPS::Poisson::update(unsigned long, unsigned long, float, float)
 +
 +
 +
|}
 +
 +
=====Analysis=====
 +
given 98.57 percent of time is spent on the update() function, it is considered the hotspot.
 +
Total time taken was 2.75.
 +
 +
If we consider a GPU environment with 1000 cores, we can estimate the following speedup:
 +
S1000 = 1/(1-.9857 + .9857/1000) = 65.00
 +
In fact, the speed will decrease from 2.75 seconds to 0.0450 seconds.
 +
 +
As each iteration depends on the product of the previous iteration, there is a dependency resolution that might hamper the parallel process.
 +
Consideration may also be extended to resolving ghost cells across different SMX while using the device global memory as the transfer pipeline.
 +
 
==== Robert ====
 
==== Robert ====
−
Subject:
+
===== Multi Sampling Anti Aliasing =====
 +
====== Source Files ======
 +
 
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! main.cpp
 +
|-
 +
|
 +
<source>
 +
#include <cstdint>
 +
#include <iostream>
 +
#include <algorithm>
 +
 
 +
#define STB_IMAGE_IMPLEMENTATION
 +
#include "stb_image.h"
 +
#define STB_IMAGE_WRITE_IMPLEMENTATION
 +
#include "stb_image_write.h"
 +
 
 +
#include "vec3.h"
 +
 
 +
struct Point {
 +
int x;
 +
int y;
 +
};
 +
uint8_t* msaa(const uint8_t* input, uint8_t* output, int width, int height, int channels, int samples) {
 +
 
 +
// directions is (samples * 2 + 1) ^ 2
 +
int totalPoints = (samples * 2 + 1) * (samples * 2 + 1);
 +
Point* directions = new Point[totalPoints];
 +
size_t idx = 0;
 +
for (int i = -samples; i <= samples; i++) {
 +
for (int j = -samples; j <= samples; j++) {
 +
directions[idx].x = i;
 +
directions[idx].y = j;
 +
idx++;
 +
}
 +
}
 +
 
 +
int  x, y, cx, cy;
 +
Vec3<int> average;
 +
for (size_t i = 0; i < width*height; i++) {
 +
x = i % width * channels;
 +
y = i / width * channels;
 +
for (size_t j = 0; j < totalPoints; j++) {
 +
cx = x + directions[j].x * channels;
 +
cy = y + directions[j].y * channels;
 +
cx = std::clamp(cx, 0, width* channels);
 +
cy = std::clamp(cy, 0, height* channels);
 +
average.add(input[width * cy + cx], input[width * cy + cx + 1], input[width * cy + cx + 2]);
 +
}
 +
average.set(average.getX() / totalPoints, average.getY() / totalPoints, average.getZ() / totalPoints);
 +
output[(width * y + x)] = average.getX();
 +
output[(width * y + x) + 1] = average.getY();
 +
output[(width * y + x) + 2] = average.getZ();
 +
average.set(0, 0, 0);
 +
}
 +
delete[] directions;
 +
return output;
 +
}
 +
 
 +
int main(int argc, char* argv[]) {
 +
if (argc != 5) {
 +
std::cerr << argv[0] << ": invalid number of arguments\n";
 +
std::cerr << "Usage: " << argv[0] << "  input output sample_size passes \n";
 +
system("pause");
 +
return 1;
 +
}
 +
int width, height, channels;
 +
uint8_t* rgb_read = stbi_load(argv[1], &width, &height, &channels, STBI_rgb);
 +
if (channels != 3) {
 +
std::cout << "Incorrect channels" << std::endl;
 +
system("pause");
 +
return 2;
 +
}
 +
int samples = std::atoi(argv[3]);
 +
int passes = std::atoi(argv[4]);
 +
uint8_t* rgb_write = new uint8_t[width*height*channels];
 +
 
 +
rgb_write = msaa(rgb_read, rgb_write, width, height, channels, samples);
 +
for (int i = 1; i < passes; i++) {
 +
rgb_write = msaa(rgb_write, rgb_write, width, height, channels, samples);
 +
}
 +
stbi_write_png(argv[2], width, height, channels, rgb_write, width*channels);
 +
stbi_image_free(rgb_read);
 +
delete[] rgb_write;
 +
std::cout << "AA Done using " << samples << " sample size" << " over " << passes << " passes" << std::endl;
 +
system("pause");
 +
return 0;
 +
}
 +
</source>
 +
|}
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! vec3.h
 +
|-
 +
|
 +
<source>
 +
#ifndef VEC3_H
 +
#define VEC3_H
 +
#include <iostream>
 +
template <class T>
 +
class Vec3 {
 +
private:
 +
T x;
 +
T y;
 +
T z;
 +
public:
 +
Vec3() {
 +
x = 0;
 +
y = 0;
 +
z = 0;
 +
};
 +
Vec3(T x_, T y_, T z_) {
 +
x = x_;
 +
y = y_;
 +
z = z_;
 +
}
 +
void set(const T &x_, const T &y_, const T &z_) {
 +
x = x_;
 +
y = y_;
 +
z = z_;
 +
}
 +
void add(const T &x_, const T &y_, const T &z_) {
 +
x += x_;
 +
y += y_;
 +
z += z_;
 +
}
 +
 +
T getX() const { return x; }
 +
T getY() const { return y; }
 +
T getZ() const { return z; }
 +
 
 +
void setX(const T &x_) { x = x_; }
 +
void setY(const T &y_) { y = y_; }
 +
void setZ(const T &z_) { z = z_; }
 +
 
 +
static T dot(const Vec3& vec1, const Vec3& vec2) {
 +
return  vec1.x * vec2.x + vec1.y * vec2.y + vec1.z * vec2.z;
 +
}
 +
T dot(const Vec3 &vec) const {
 +
return x * vec.x + y * vec.y + z * vec.z;
 +
}
 +
void display(std::ostream& os) {
 +
os << "x: " << x << ", y: " << y << ", z: " << z << "\n";
 +
}
 +
 
 +
};
 +
 
 +
#endif // !VEC3_H
 +
 
 +
</source>
 +
|}
 +
[https://github.com/nothings/stb/blob/master/stb_image.h stb_image_write.h]
 +
[https://github.com/nothings/stb/blob/master/stb_image.h stb_image.h]
 +
 
 +
====== Introduction ======
 +
For my selection I chose to do Anti Aliasing since I see it a lot in video games but I never really knew how it worked. There are other anti aliasing methods like FXAA which is fast approximate anti aliasing but it seemed a lot more complicated than MSAA. The way I approached this problem is by getting the color of the pixels around a pixel. In you can specify the distance it will search in the application flags. In my implementation you specify an input file, output file, the radius of pixels to sample and how many passes to take on the image. In my tests the command line options I used was an image I made in paint with 4 sample size and 4 passes.
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! Before
 +
|-
 +
|
 +
[[File:MSAABefore.png]]
 +
|}
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! After
 +
|-
 +
|
 +
[[File:MSAAAfter.png]].
 +
|}
 +
====== Profiling ======
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! Profiling
 +
|-
 +
|
 +
<source>
 +
Flat profile:
 +
 
 +
Each sample counts as 0.01 seconds.
 +
  %  cumulative  self              self    total
 +
time  seconds  seconds    calls  ms/call  ms/call  name
 +
85.72      0.18    0.18                            msaa(unsigned char const*, unsigned char*, int, int, int, int)
 +
14.29      0.21    0.03        1    30.00    30.00  stbi_zlib_compress
 +
  0.00      0.21    0.00  127820    0.00    0.00  stbiw__zlib_flushf(unsigned char*, unsigned int*, int*)
 +
  0.00      0.21    0.00    96904    0.00    0.00  stbiw__zhash(unsigned char*)
 +
  0.00      0.21    0.00    5189    0.00    0.00  stbi__fill_bits(stbi__zbuf*)
 +
  0.00      0.21    0.00    2100    0.00    0.00  stbiw__encode_png_line(unsigned char*, int, int, int, int, int, int, signed char*)
 +
  0.00      0.21    0.00    2014    0.00    0.00  stbiw__sbgrowf(void**, int, int) [clone .constprop.58]
 +
  0.00      0.21    0.00      38    0.00    0.00  stbi__get16be(stbi__context*)
 +
  0.00      0.21    0.00      19    0.00    0.00  stbi__get32be(stbi__context*)
 +
  0.00      0.21    0.00        3    0.00    0.00  stbi__skip(stbi__context*, int)
 +
  0.00      0.21    0.00        3    0.00    0.00  stbiw__wpcrc(unsigned char**, int)
 +
  0.00      0.21    0.00        3    0.00    0.00  stbi__stdio_read(void*, char*, int)
 +
  0.00      0.21    0.00        3    0.00    0.00  stbi__zbuild_huffman(stbi__zhuffman*, unsigned char const*, int)
 +
  0.00      0.21    0.00        2    0.00    0.00  stbi__mad3sizes_valid(int, int, int, int)
 +
  0.00      0.21    0.00        1    0.00    0.00  _GLOBAL__sub_I_stbi_failure_reason
 +
  0.00      0.21    0.00        1    0.00    0.00  stbi__getn(stbi__context*, unsigned char*, int)
 +
  0.00      0.21    0.00        1    0.00    0.00  stbi__readval(stbi__context*, int, unsigned char*)
 +
  0.00      0.21    0.00        1    0.00    0.00  stbi__load_main(stbi__context*, int*, int*, int*, int, stbi__result_info*, int)
 +
  0.00      0.21    0.00        1    0.00    0.00  stbi__parse_zlib(stbi__zbuf*, int)
 +
  0.00      0.21    0.00        1    0.00    0.00  stbi__malloc_mad3(int, int, int, int)
 +
  0.00      0.21    0.00        1    0.00    0.00  stbi__parse_png_file(stbi__png*, int, int)
 +
  0.00      0.21    0.00        1    0.00    0.00  stbi__start_callbacks(stbi__context*, stbi_io_callbacks*, void*)
 +
  0.00      0.21    0.00        1    0.00    0.00  stbi__decode_jpeg_header(stbi__jpeg*, int)
 +
  0.00      0.21    0.00        1    0.00    0.00  stbi__compute_huffman_codes(stbi__zbuf*)
 +
  0.00      0.21    0.00        1    0.00    0.00  stbi__load_and_postprocess_8bit(stbi__context*, int*, int*, int*, int)
 +
  0.00      0.21    0.00        1    0.00    0.00  stbi_load_from_file
 +
  0.00      0.21    0.00        1    0.00    30.00  stbi_write_png_to_mem
 +
  0.00      0.21    0.00        1    0.00    0.00  stbi_zlib_decode_malloc_guesssize_headerflag
 +
</source>
 +
|}
 +
 
 +
====== Conclusion ======
 +
Since the <code>msaa</code> function I wrote is a hotspot of the program I would suggest offloading part of it to a GPU, more specifically the part that finds the average of colors of the nearby pixels. That part also does not depend on previous iterations to finish so it is a prime candidate for parallelization.
 +
 
 
==== Inna ====
 
==== Inna ====
  
Line 20: Line 492:
  
 
I tested the following source code for a compression and decompression of .txt files and a gif.
 
I tested the following source code for a compression and decompression of .txt files and a gif.
−
 
−
 
 
{| class="wikitable mw-collapsible mw-collapsed"
 
{| class="wikitable mw-collapsible mw-collapsed"
 
! lwz.cpp( ... )
 
! lwz.cpp( ... )
Line 290: Line 760:
 
===== Tested data =====  
 
===== Tested data =====  
  
−
1. book.txt- a 343 kilobyte text file.
+
1. book.txt - a 343 kilobyte text file.
  
−
2. words.txt- a 4.7 megabyte text file.
+
2. words.txt - a 4.7 megabyte text file.
  
−
3. fire.gif- a 309 kilobyte graphical image.
+
3. fire.gif - a 309 kilobyte graphical image.
  
 
===== Flat Profiles =====  
 
===== Flat Profiles =====  
  
−
Flat profile for book compression:
+
====== Book ======
 +
 
 +
Flat profile for compression:
 +
 
 
[[File:BookComp.jpg|1300px]]
 
[[File:BookComp.jpg|1300px]]
  
−
Flat profile for book decompression:
+
Flat profile for decompression:
  
 
[[File:BookDecomp.jpg|700px]]
 
[[File:BookDecomp.jpg|700px]]
  
−
Flat profile for text compression:
+
====== Text ======
 +
 
 +
Flat profile for compression:
 +
 
 
[[File:textCompression.jpg|1300px]]
 
[[File:textCompression.jpg|1300px]]
  
−
Flat profile for text decompression:
+
Flat profile for decompression:
  
 
[[File:textDecomp.jpg|1300px]]
 
[[File:textDecomp.jpg|1300px]]
 +
 +
====== GIF ======
 +
 +
Flat profile for compression:
 +
 +
[[File:FireComp.jpg|1300px]]
 +
 +
Flat profile for decompression:
 +
 +
[[File:FireDecomp.jpg|900px]]
  
 
=== Assignment 2 ===
 
=== Assignment 2 ===
 +
====== Source Files ======
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! poisson-pcie.cu
 +
|-
 +
|
 +
<source>
 +
/*
 +
* Poisson Method using two arrays. 
 +
* Non-Ghost Cells Method
 +
* Multiple PCIe Calls made, once per iteration
 +
* by Tony Sim
 +
*/
 +
#include <cstring>
 +
#include <cstdlib>
 +
#include <iomanip>
 +
#include <iostream>
 +
#include <string>
 +
#include <cuda_runtime.h>
 +
#include "poisson.cuh"
 +
 +
namespace DPS{
 +
 +
    Poisson::Poisson(std::ifstream& ifs) {
 +
        std::string line;
 +
        nColumns = 0;
 +
        bufferSide = 0;
 +
        nRowsTotal = 0;
 +
        /* find number of columns */
 +
        std::getline(ifs,line);
 +
        for (size_t i = 0 ; i < line.size() ; i++){
 +
            if(line[i]==' ') nColumns++;
 +
        }
 +
        nColumns++;
 +
 +
        /* find number of rows */
 +
        nRowsTotal++; /* already fetched one */
 +
        while(std::getline(ifs,line))
 +
            nRowsTotal++;
 +
        ifs.clear();
 +
 +
        try{
 +
            for (size_t i = 0 ; i < 2 ; i++)
 +
                h_data[i] = new float[ (nColumns+2) * (nRowsTotal+2)]; /* add edge buffers */
 +
        }
 +
        catch (...){
 +
            throw std::runtime_error("Failed to Allocate Memory");
 +
        }
 +
 +
        /* readin data */
 +
        std::cout <<"Reading in data"<<std::endl;
 +
        ifs.seekg(0,ifs.beg);
 +
        /* allocate memory to all but the edge buffer, index 0 and max for each row and column */
 +
        for (size_t i = 0 ; i < nRowsTotal+2 ; i++){
 +
            for (size_t j = 0 ; j < nColumns+2 ; j++){
 +
                float val = 0;
 +
                if(!(i == 0 || i == nRowsTotal + 1 || j == 0 || j == nColumns + 1))
 +
                    ifs >> val;
 +
                h_data[0][i*(nColumns+2)+j] = val;
 +
            }
 +
        }
 +
 +
        std::cout <<"Setting buffer"<<std::endl;
 +
        std::memset(h_data[1],0,(nRowsTotal+2)*(nColumns+2)*sizeof(float));
 +
        bool state = devMemSet();
 +
 +
    }
 +
 +
    Poisson::Poisson(const size_t r, const size_t c, float* d) {
 +
        bufferSide = 0;
 +
        nRowsTotal = r;
 +
        nColumns = c;
 +
        try{
 +
            h_data[0] = new float[(r+2)*(c+2)];
 +
            h_data[1] = new float[(r+2)*(c+2)];
 +
        }
 +
        catch (...){
 +
            throw std::runtime_error("Failed to Allocate Memory");
 +
        }
 +
        std::memcpy(h_data[0],d,(r+2)*(c+2)*sizeof(float));
 +
        std::memset(h_data[1],0,(r+2)*(c+2)*sizeof(float));
 +
        devMemSet();
 +
    }
 +
 +
    Poisson::~Poisson(){
 +
        for( size_t i = 0 ; i < 2 ; i++){
 +
            delete [] h_data[i];
 +
            cudaFree(d_data[i]);
 +
        }
 +
    }
 +
 +
    bool Poisson::devMemSet(){
 +
        for(size_t i = 0 ; i < 2 ; i++){
 +
            cudaMalloc(&d_data[i],(nColumns+2)*(nRowsTotal+2)*sizeof(float));
 +
            if(d_data[i] != nullptr){
 +
                cudaError_t state = cudaMemcpy((void*)d_data[i],(const void*)h_data[i],(nColumns+2)*(nRowsTotal+2)*sizeof(float),cudaMemcpyHostToDevice);
 +
                if(state != cudaSuccess)
 +
                        std::cerr << "ERROR on devMemSet for : " << i <<" with : " << cudaGetErrorString(state)<< std::endl;
 +
            }
 +
        }
 +
        return d_data[0]&&d_data[1];
 +
    }
 +
 +
 +
    float* Poisson::operator()(const size_t nIterations, const float wx, const float wy){
 +
 +
        /* calculate the grid, block, where block has 1024 threads total */
 +
        unsigned int blockx = 32;
 +
        unsigned int blocky = 32;
 +
        unsigned int gridx = ((nRowsTotal+2)+blockx-1)/blockx;
 +
        unsigned int gridy = ((nRowsTotal+2)+blocky-1)/blocky;
 +
 +
        /* create dim3 */
 +
        dim3 dBlock= {blockx,blocky};
 +
        dim3 dGrid = {gridx,gridy};
 +
 +
        /* run iterations */
 +
        for (size_t i = 0; i < nIterations; i++) {
 +
            update<<<dGrid,dBlock>>>(d_data[1-bufferSide],d_data[bufferSide],nColumns, nRowsTotal, wx, wy);
 +
            bufferSwitch();
 +
        }
 +
 +
        /* DEBUG */ h_data[bufferSide][1*(nColumns+2) + 1] = 100.0f;
 +
        /* output results from device to host */
 +
        cudaError_t state = cudaMemcpy(h_data[bufferSide],d_data[bufferSide],(nColumns+2)*(nRowsTotal+2)*sizeof(float),cudaMemcpyDeviceToHost);
 +
        if(state != cudaSuccess)
 +
            std::cout << "ERROR on () when copying data back to host" <<" with : " << cudaGetErrorString(state)<< std::endl;
 +
 +
        return h_data[bufferSide];
 +
    }
 +
 +
    void Poisson::show(std::ostream& ofs) const{
 +
        ofs << std::fixed << std::setprecision(1);
 +
        for (size_t j = 1; j <= nColumns ; j++) {
 +
            for (size_t i = 1 ; i <= nRowsTotal ; i++)
 +
                ofs << std::setw(8) << h_data[bufferSide][i * (nColumns+2) + j]<<",";
 +
            ofs << std::endl;
 +
        }
 +
    }
 +
    __global__ void update (float* newD, const float* currD, int nCol, int nRow, const float wx, const float wy){
 +
        size_t i = blockDim.x * blockIdx.x + threadIdx.x + 1; /* for x axis */
 +
        size_t j = blockDim.y * blockIdx.y + threadIdx.y + 1; /* for y axis */
 +
        newD[i*(nCol+2)+j] = currD[i * (nCol+2) +j] + wx*(currD[(i+1) * (nCol+2) +j] + currD[(i-1) * (nCol+2) +j] - 2.0f * currD[i * (nCol+2) +j] ) + wy*( currD[i * (nCol+2) +j+1] + currD[i * (nCol+2) +j-1] - 2.0f * currD[i * (nCol+2) +j]) ;
 +
        __syncthreads();
 +
    }
 +
}
 +
</source>
 +
|}
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! poisson-alt.cu
 +
|-
 +
|
 +
<source>
 +
/*
 +
* Poisson Method using two arrays. 
 +
* Non-Ghost Cells Method
 +
* One PCIe Call made, iterations done in kernel
 +
* by Tony Sim
 +
*/
 +
#include <cstring>
 +
#include <cstdlib>
 +
#include <iomanip>
 +
#include <iostream>
 +
#include <string>
 +
#include <cuda_runtime.h>
 +
#include "poisson-alt.cuh"
 +
 +
namespace DPS{
 +
 +
    Poisson::Poisson(std::ifstream& ifs) {
 +
        blockx = 32;
 +
        blocky = 32;
 +
 +
        std::string line;
 +
        nColumns = 0;
 +
        bufferSide = 0;
 +
        nRowsTotal = 0;
 +
        /* find number of columns */
 +
        std::getline(ifs,line);
 +
        for (size_t i = 0 ; i < line.size() ; i++){
 +
            if(line[i]==' ') nColumns++;
 +
        }
 +
        nColumns++;
 +
 +
        /* find number of rows */
 +
        nRowsTotal++; /* already fetched one */
 +
        while(std::getline(ifs,line))
 +
            nRowsTotal++;
 +
        ifs.clear();
 +
 +
        int sizeX = ((nColumns + 2 + blockx + 2 - 1)/(blockx+2))*(blockx+2);
 +
        int sizeY = ((nRowsTotal + 2 + blocky + 2 - 1)/(blocky+2))*(blocky+2);
 +
        bufferSize = sizeX * sizeY;
 +
        std::cout << "Allocate initial memory" << std::endl;
 +
        try{
 +
            h_data = new float[ bufferSize ]; /* add edge buffers */
 +
        }
 +
        catch (...){
 +
            throw std::runtime_error("Failed to Allocate Memory");
 +
        }
 +
 +
        /* readin data */
 +
        std::cout <<"Reading in data"<<std::endl;
 +
        ifs.seekg(0,ifs.beg);
 +
        /* allocate memory to all but the edge buffer, index 0 and max for each row and column */
 +
        std::memset(h_data,0,bufferSize);
 +
        for (size_t i = 0 ; i < nRowsTotal+2 ; i++){
 +
            for (size_t j = 0 ; j < nColumns+2 ; j++){
 +
                float val = 0;
 +
                if(!(i == 0 || i == nRowsTotal + 1 || j == 0 || j == nColumns + 1))
 +
                    ifs >> val;
 +
                h_data[i*(nColumns+2)+j] = val;
 +
            }
 +
        }
 +
 +
        std::cout <<"Setting buffer"<<std::endl;
 +
        bool state = devMemSet();
 +
 +
    }
 +
 +
    Poisson::Poisson(const size_t r, const size_t c, float* d) {
 +
        bufferSide = 0;
 +
        nRowsTotal = r;
 +
        nColumns = c;
 +
        try{
 +
            h_data = new float[(r+2)*(c+2)];
 +
        }
 +
        catch (...){
 +
            throw std::runtime_error("Failed to Allocate Memory");
 +
        }
 +
        std::memcpy(h_data,d,(r+2)*(c+2)*sizeof(float));
 +
        devMemSet();
 +
    }
 +
 +
    Poisson::~Poisson(){
 +
        delete [] h_data;
 +
        cudaFree(d_data);
 +
        cudaDeviceReset();
 +
    }
 +
 +
    bool Poisson::devMemSet(){
 +
 +
        /* create double buffer */
 +
        cudaMalloc(&d_data,2* bufferSize * sizeof(float));
 +
 +
        if(d_data != nullptr){
 +
            /* copy the initial information to the first buffer */
 +
            cudaError_t state = cudaMemcpy((void*)d_data,(const void*)h_data, bufferSize * sizeof(float),cudaMemcpyHostToDevice);
 +
            if(state != cudaSuccess)
 +
                std::cerr << "ERROR on devMemSet at cudaMemcpy : " << cudaGetErrorString(state)<< std::endl;
 +
 +
            /* set the second buffer to zero */
 +
            state = cudaMemset( d_data + bufferSize , 0, bufferSize * sizeof(float));
 +
            if(state != cudaSuccess)
 +
                std::cerr << "ERROR on devMemSet at cudaMemset : " << cudaGetErrorString(state)<< std::endl;
 +
 +
        }
 +
        return d_data;
 +
    }
 +
 +
    float* Poisson::operator()(const size_t nIterations, const float wx, const float wy){
 +
 +
        /* calculate the grid, block, where block has 1024 threads total */
 +
        unsigned int gridx = ((nRowsTotal+2)+blockx-1)/blockx;
 +
        unsigned int gridy = ((nRowsTotal+2)+blocky-1)/blocky;
 +
 +
        /* create dim3 */
 +
        dim3 dBlock= {blockx,blocky};
 +
        dim3 dGrid = {gridx,gridy};
 +
 +
        /* run iterations */
 +
        update<<<dGrid,dBlock>>>(d_data,nColumns, nRowsTotal, wx, wy,nIterations,bufferSize);
 +
 +
        /*DEBUG */ h_data[2*(nColumns+2)+2] = 100.0f;
 +
        /* output results from device to host */
 +
        cudaError_t state = cudaMemcpy(h_data,d_data,(nColumns+2)*(nRowsTotal+2)*sizeof(float),cudaMemcpyDeviceToHost);
 +
        if(state != cudaSuccess)
 +
            std::cout << "ERROR on () when copying data back to host with : " << cudaGetErrorString(state)<< std::endl;
 +
 +
        return h_data;
 +
    }
 +
 +
    void Poisson::show(std::ostream& ofs) const{
 +
        ofs << std::fixed << std::setprecision(1);
 +
        for (size_t j = 1; j <= nColumns ; j++) {
 +
            for (size_t i = 1 ; i <= nRowsTotal ; i++)
 +
                ofs << std::setw(8) << h_data[i * (nColumns+2) + j]<<",";
 +
            ofs << std::endl;
 +
        }
 +
    }
 +
    __global__ void update (float* data, int nCol, int nRow, const float wx, const float wy, unsigned int nIterations, unsigned int bufferSize){
 +
        size_t i = blockDim.x * blockIdx.x + threadIdx.x + 1; /* for x axis */
 +
        size_t j = blockDim.y * blockIdx.y + threadIdx.y + 1; /* for y axis */
 +
 +
        unsigned int buffer = 0;
 +
 +
        /* run iterations */
 +
        for (unsigned int n = 0 ; n < nIterations; n++){
 +
            /* Calculate and store into the other buffer */
 +
            data[(1-buffer)*bufferSize + i*(nCol+2)+j] = data[buffer*bufferSize + i * (nCol+2)+ j]
 +
                + wx * (data[buffer*bufferSize + (i+1) * (nCol+2) +j] + data[buffer*bufferSize + (i-1) * (nCol+2) + j] - 2.0f * data[buffer*bufferSize + i * (nCol+2)+ j])
 +
                + wy * (data[buffer*bufferSize + i * (nCol+2) + j + 1] + data[buffer*bufferSize +  i * (nCol+2) + j - 1] - 2.0f * data[buffer*bufferSize + i * (nCol+2)+ j]);
 +
            __syncthreads();
 +
 +
            /* flip buffer */
 +
            buffer = 1-buffer;
 +
        }
 +
 +
        /* copy the output back into global memory */
 +
        data[i*(nCol+2)+j] = data[buffer * bufferSize + i * (nCol+2) + j ];
 +
        __syncthreads();
 +
    }
 +
}
 +
</source>
 +
|}
 +
====== Profiles ======
 +
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! Poisson PCIe Profile
 +
|-
 +
|
 +
<source>
 +
Reading in data
 +
Setting buffer
 +
==6484== NVPROF is profiling process 6484, command: .\pcie.exe .\test3.csv .\output3.csv 1000
 +
==6484== Profiling application: .\pcie.exe .\test3.csv .\output3.csv 1000
 +
==6484== Warning: 43 API trace records have same start and end timestamps.
 +
This can happen because of short execution duration of CUDA APIs and low timer resolution on the underlying operating sy
 +
stem.
 +
==6484== Profiling result:
 +
            Type  Time(%)      Time    Calls      Avg      Min      Max  Name
 +
GPU activities:  99.86%  29.120ms      1000  29.119us  24.158us  30.589us  DPS::update(float*, float const *, int, int
 +
, float, float)
 +
                    0.09%  26.269us        2  13.134us  12.990us  13.279us  [CUDA memcpy HtoD]
 +
                    0.04%  13.119us        1  13.119us  13.119us  13.119us  [CUDA memcpy DtoH]
 +
      API calls:  71.59%  183.43ms        2  91.713ms  10.265us  183.42ms  cudaMalloc
 +
                  15.25%  39.069ms        1  39.069ms  39.069ms  39.069ms  cuDevicePrimaryCtxRelease
 +
                    8.04%  20.601ms        3  6.8671ms  81.478us  20.424ms  cudaMemcpy
 +
                    3.76%  9.6313ms      1000  9.6310us  6.7360us  335.53us  cudaLaunchKernel
 +
                    1.26%  3.2196ms        96  33.537us      0ns  1.6234ms  cuDeviceGetAttribute
 +
                    0.05%  127.03us        1  127.03us  127.03us  127.03us  cuModuleUnload
 +
                    0.04%  107.78us        2  53.890us  22.454us  85.327us  cudaFree
 +
                    0.00%  10.265us        1  10.265us  10.265us  10.265us  cuDeviceTotalMem
 +
                    0.00%  9.6230us        1  9.6230us  9.6230us  9.6230us  cuDeviceGetPCIBusId
 +
                    0.00%  1.2820us        2    641ns    320ns    962ns  cuDeviceGet
 +
                    0.00%    962ns        3    320ns      0ns    641ns  cuDeviceGetCount
 +
                    0.00%    962ns        1    962ns    962ns    962ns  cuDeviceGetName
 +
                    0.00%    321ns        1    321ns    321ns    321ns  cuDeviceGetUuid
 +
                    0.00%    321ns        1    321ns    321ns    321ns  cuDeviceGetLuid
 +
</source>
 +
|}
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! Poisson AltProfile
 +
|-
 +
|
 +
<source>
 +
Allocate initial memory
 +
Reading in data
 +
Setting buffer
 +
==2720== NVPROF is profiling process 2720, command: .\alt.exe .\test3.csv .\output3.csv 1000
 +
==2720== Profiling application: .\alt.exe .\test3.csv .\output3.csv 1000
 +
==2720== Warning: 50 API trace records have same start and end timestamps.
 +
This can happen because of short execution duration of CUDA APIs and low timer resolution on the underlying operating sy
 +
stem.
 +
==2720== Profiling result:
 +
            Type  Time(%)      Time    Calls      Avg      Min      Max  Name
 +
GPU activities:  99.88%  25.679ms        1  25.679ms  25.679ms  25.679ms  DPS::update(float*, int, int, float, float,
 +
unsigned int, unsigned int)
 +
                    0.06%  16.670us        1  16.670us  16.670us  16.670us  [CUDA memcpy HtoD]
 +
                    0.05%  12.575us        1  12.575us  12.575us  12.575us  [CUDA memcpy DtoH]
 +
                    0.00%    576ns        1    576ns    576ns    576ns  [CUDA memset]
 +
      API calls:  70.46%  158.87ms        1  158.87ms  158.87ms  158.87ms  cudaMalloc
 +
                  16.71%  37.678ms        1  37.678ms  37.678ms  37.678ms  cudaDeviceReset
 +
                  11.48%  25.877ms        2  12.938ms  60.947us  25.816ms  cudaMemcpy
 +
                    1.25%  2.8161ms        96  29.334us      0ns  1.3867ms  cuDeviceGetAttribute
 +
                    0.06%  133.12us        1  133.12us  133.12us  133.12us  cudaFree
 +
                    0.02%  47.475us        1  47.475us  47.475us  47.475us  cudaMemset
 +
                    0.01%  18.605us        1  18.605us  18.605us  18.605us  cudaLaunchKernel
 +
                    0.01%  11.548us        1  11.548us  11.548us  11.548us  cuDeviceTotalMem
 +
                    0.00%  9.9440us        1  9.9440us  9.9440us  9.9440us  cuDeviceGetPCIBusId
 +
                    0.00%  1.2830us        1  1.2830us  1.2830us  1.2830us  cuDeviceGetName
 +
                    0.00%    963ns        3    321ns      0ns    642ns  cuDeviceGetCount
 +
                    0.00%    642ns        1    642ns    642ns    642ns  cuDeviceGetLuid
 +
                    0.00%    641ns        2    320ns      0ns    641ns  cuDeviceGet
 +
                    0.00%      0ns        1      0ns      0ns      0ns  cuDeviceGetUuid
 +
</source>
 +
|}
 +
 +
====== GPU Offload Vs CPU ======
 +
[[File:Gc-spa.png | 800px]]
 +
 
=== Assignment 3 ===
 
=== Assignment 3 ===
 +
==== Source Codes ====
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! PCIe Optimization
 +
|-
 +
|
 +
<source>
 +
/*
 +
* Poisson Method using two arrays. 
 +
* Non-Ghost Cells Method
 +
* Multiple PCIe Calls made, once per iteration
 +
* by Tony Sim
 +
*/
 +
#include <cstring>
 +
#include <cstdlib>
 +
#include <iomanip>
 +
#include <iostream>
 +
#include <string>
 +
#include <cuda_runtime.h>
 +
#include "poisson.cuh"
 +
 +
namespace DPS{
 +
 +
    Poisson::Poisson(std::ifstream& ifs) {
 +
        std::string line;
 +
        nColumns = 0;
 +
        bufferSide = 0;
 +
        nRowsTotal = 0;
 +
        /* find number of columns */
 +
        std::getline(ifs,line);
 +
        for (size_t i = 0 ; i < line.size() ; i++){
 +
            if(line[i]==' ') nColumns++;
 +
        }
 +
        nColumns++;
 +
 +
        /* find number of rows */
 +
        nRowsTotal++; /* already fetched one */
 +
        while(std::getline(ifs,line))
 +
            nRowsTotal++;
 +
        ifs.clear();
 +
 +
        try{
 +
            for (size_t i = 0 ; i < 2 ; i++)
 +
                h_data[i] = new float[ (nColumns+2) * (nRowsTotal+2)]; /* add edge buffers */
 +
        }
 +
        catch (...){
 +
            throw std::runtime_error("Failed to Allocate Memory");
 +
        }
 +
 +
        /* readin data */
 +
        std::cout <<"Reading in data"<<std::endl;
 +
        ifs.seekg(0,ifs.beg);
 +
        /* allocate memory to all but the edge buffer, index 0 and max for each row and column */
 +
        for (size_t i = 0 ; i < nRowsTotal+2 ; i++){
 +
            for (size_t j = 0 ; j < nColumns+2 ; j++){
 +
                float val = 0;
 +
                if(!(i == 0 || i == nRowsTotal + 1 || j == 0 || j == nColumns + 1))
 +
                    ifs >> val;
 +
                h_data[0][i*(nColumns+2)+j] = val;
 +
            }
 +
        }
 +
 +
        std::cout <<"Setting buffer"<<std::endl;
 +
        std::memset(h_data[1],0,(nRowsTotal+2)*(nColumns+2)*sizeof(float));
 +
        bool state = devMemSet();
 +
        /* DEBUG */ std::cout << state << std::endl;
 +
 +
    }
 +
 +
    Poisson::Poisson(const size_t r, const size_t c, float* d) {
 +
        bufferSide = 0;
 +
        nRowsTotal = r;
 +
        nColumns = c;
 +
        try{
 +
            h_data[0] = new float[(r+2)*(c+2)];
 +
            h_data[1] = new float[(r+2)*(c+2)];
 +
        }
 +
        catch (...){
 +
            throw std::runtime_error("Failed to Allocate Memory");
 +
        }
 +
        std::memcpy(h_data[0],d,(r+2)*(c+2)*sizeof(float));
 +
        std::memset(h_data[1],0,(r+2)*(c+2)*sizeof(float));
 +
        devMemSet();
 +
    }
 +
 +
    Poisson::~Poisson(){
 +
        for( size_t i = 0 ; i < 2 ; i++){
 +
            delete [] h_data[i];
 +
            cudaFree(d_data[i]);
 +
        }
 +
    }
 +
 +
    bool Poisson::devMemSet(){
 +
        for(size_t i = 0 ; i < 2 ; i++){
 +
            cudaMalloc(&d_data[i],(nColumns+2)*(nRowsTotal+2)*sizeof(float));
 +
            if(d_data[i] != nullptr){
 +
                cudaError_t state = cudaMemcpy((void*)d_data[i],(const void*)h_data[i],(nColumns+2)*(nRowsTotal+2)*sizeof(float),cudaMemcpyHostToDevice);
 +
                if(state != cudaSuccess)
 +
                        std::cerr << "ERROR on devMemSet for : " << i <<" with : " << cudaGetErrorString(state)<< std::endl;
 +
            }
 +
        }
 +
        return d_data[0]&&d_data[1];
 +
    }
 +
 +
 +
    float* Poisson::operator()(const size_t nIterations, const float wx, const float wy){
 +
 +
        /* calculate the grid, block, where block has 1024 threads total */
 +
        unsigned int blockx = 32;
 +
        unsigned int blocky = 32;
 +
        unsigned int gridx = ((nRowsTotal+2)+blockx-1)/blockx;
 +
        unsigned int gridy = ((nRowsTotal+2)+blocky-1)/blocky;
 +
 +
        /* create dim3 */
 +
        dim3 dBlock= {blockx,blocky};
 +
        dim3 dGrid = {gridx,gridy};
 +
 +
        /* run iterations */
 +
        for (size_t i = 0; i < nIterations; i++) {
 +
            update<<<dGrid,dBlock>>>(d_data[1-bufferSide],d_data[bufferSide],nColumns, nRowsTotal, wx, wy);
 +
            bufferSwitch();
 +
        }
 +
 +
        /* DEBUG */ h_data[bufferSide][1*(nColumns+2) + 1] = 100.0f;
 +
        /* output results from device to host */
 +
        cudaError_t state = cudaMemcpy(h_data[bufferSide],d_data[bufferSide],(nColumns+2)*(nRowsTotal+2)*sizeof(float),cudaMemcpyDeviceToHost);
 +
        if(state != cudaSuccess)
 +
            std::cout << "ERROR on () when copying data back to host" <<" with : " << cudaGetErrorString(state)<< std::endl;
 +
 +
        return h_data[bufferSide];
 +
    }
 +
 +
    void Poisson::show(std::ostream& ofs) const{
 +
        ofs << std::fixed << std::setprecision(1);
 +
        for (size_t j = 1; j <= nColumns ; j++) {
 +
            for (size_t i = 1 ; i <= nRowsTotal ; i++)
 +
                ofs << std::setw(8) << h_data[bufferSide][i * (nColumns+2) + j]<<",";
 +
            ofs << std::endl;
 +
        }
 +
    }
 +
    __global__ void update (float* newD, const float* currD, int nCol, int nRow, const float wx, const float wy){
 +
        size_t j = blockDim.x * blockIdx.x + threadIdx.x + 1; /* for x axis */
 +
        size_t i = blockDim.y * blockIdx.y + threadIdx.y + 1; /* for y axis */
 +
        float curr = currD[i * (nCol+2)+ j];
 +
        float dir1 = currD[(i+1) * (nCol+2) +j];
 +
        float dir2 = currD[(i-1) * (nCol+2) +j];
 +
        float dir3 = currD[i * (nCol+2) +j+1];
 +
        float dir4 = currD[i * (nCol+2) +j-1];
 +
        newD[i*(nCol+2)+j] = curr + wx * (dir1+dir2-2.0f*curr) + wy * (dir3+dir4-2.0f*curr);
 +
        __syncthreads();
 +
    }
 +
}
 +
 +
</source>
 +
|}
 +
 +
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! For-loop Optimization
 +
|-
 +
|
 +
<source>
 +
/*
 +
* Poisson Method using two arrays. 
 +
* Non-Ghost Cells Method
 +
* Multiple PCIe Calls made, once per iteration
 +
* by Tony Sim
 +
*/
 +
#include <cstring>
 +
#include <cstdlib>
 +
#include <iomanip>
 +
#include <iostream>
 +
#include <string>
 +
#include <cuda_runtime.h>
 +
#include "poisson-alt-ghost2.cuh"
 +
 +
namespace DPS{
 +
 +
    Poisson::Poisson(std::ifstream& ifs) {
 +
        blockx = 32;
 +
        blocky = 32;
 +
 +
        std::string line;
 +
        nColumns = 0;
 +
        bufferSide = 0;
 +
        nRowsTotal = 0;
 +
        /* find number of columns */
 +
        std::getline(ifs,line);
 +
        for (size_t i = 0 ; i < line.size() ; i++){
 +
            if(line[i]==' ') nColumns++;
 +
        }
 +
        nColumns++;
 +
 +
        /* find number of rows */
 +
        nRowsTotal++; /* already fetched one */
 +
        while(std::getline(ifs,line))
 +
            nRowsTotal++;
 +
        ifs.clear();
 +
 +
        int sizeX = ((nColumns + 2 + blockx + 2 - 1)/(blockx+2))*(blockx+2);
 +
        int sizeY = ((nRowsTotal + 2 + blocky + 2 - 1)/(blocky+2))*(blocky+2);
 +
        bufferSize = sizeX * sizeY;
 +
        std::cout << "Allocate initial memory" << std::endl;
 +
        try{
 +
            h_data = new float[ bufferSize ]; /* add edge buffers */
 +
        }
 +
        catch (...){
 +
            throw std::runtime_error("Failed to Allocate Memory");
 +
        }
 +
 +
        /* readin data */
 +
        std::cout <<"Reading in data"<<std::endl;
 +
        ifs.seekg(0,ifs.beg);
 +
        /* allocate memory to all but the edge buffer, index 0 and max for each row and column */
 +
        std::memset(h_data,0,bufferSize);
 +
        for (size_t i = 0 ; i < nRowsTotal+2 ; i++){
 +
            for (size_t j = 0 ; j < nColumns+2 ; j++){
 +
                float val = 0;
 +
                if(!(i == 0 || i == nRowsTotal + 1 || j == 0 || j == nColumns + 1))
 +
                    ifs >> val;
 +
                h_data[i*(nColumns+2)+j] = val;
 +
            }
 +
        }
 +
 +
        std::cout <<"Setting buffer"<<std::endl;
 +
        bool state = devMemSet();
 +
 +
    }
 +
 +
    Poisson::Poisson(const size_t r, const size_t c, float* d) {
 +
        bufferSide = 0;
 +
        nRowsTotal = r;
 +
        nColumns = c;
 +
        try{
 +
            h_data = new float[(r+2)*(c+2)];
 +
        }
 +
        catch (...){
 +
            throw std::runtime_error("Failed to Allocate Memory");
 +
        }
 +
        std::memcpy(h_data,d,(r+2)*(c+2)*sizeof(float));
 +
        devMemSet();
 +
    }
 +
 +
    Poisson::~Poisson(){
 +
        delete [] h_data;
 +
        cudaFree(d_data);
 +
        cudaDeviceReset();
 +
    }
 +
 +
    bool Poisson::devMemSet(){
 +
 +
        /* create double buffer */
 +
        cudaMalloc(&d_data, bufferSize * sizeof(float));
 +
 +
        if(d_data != nullptr){
 +
            /* copy the initial information to the first buffer */
 +
            cudaError_t state = cudaMemcpy((void*)d_data,(const void*)h_data, bufferSize * sizeof(float),cudaMemcpyHostToDevice);
 +
            if(state != cudaSuccess)
 +
                std::cerr << "ERROR on devMemSet at cudaMemcpy : " << cudaGetErrorString(state)<< std::endl;
 +
        }
 +
        return d_data;
 +
    }
 +
 +
    float* Poisson::operator()(const size_t nIterations, const float wx, const float wy){
 +
 +
        /* calculate the grid, block, where block has 1024 threads total */
 +
        unsigned int gridx = ((nRowsTotal+2)+blockx-1)/blockx;
 +
        unsigned int gridy = ((nRowsTotal+2)+blocky-1)/blocky;
 +
 +
        /* create dim3 */
 +
        dim3 dBlock= {blockx,blocky};
 +
        dim3 dGrid = {gridx,gridy};
 +
 +
        /* generate shared memory map that will control ghost cell sharing */
 +
        char* hmap = new char[(blockx+2)*(blocky+2)*3];
 +
        int stride = 3;
 +
        for(int i = 0 ; i < (blockx+2);i++){
 +
            for(int j = 0 ; j < (blocky+2);j++){
 +
                char val = 0;
 +
                char x = 0;
 +
                char y = 0;
 +
                if(i==1){
 +
                    val = 1;
 +
                    x=-1;
 +
                    y=0;
 +
                }
 +
                if(j==1){
 +
                    val = 1;
 +
                    x=0;
 +
                    y=-1;
 +
                }
 +
                if(i==blockx) {
 +
                    val = 1;
 +
                    x=1;
 +
                    y=0;
 +
                }
 +
                if(j==blocky){
 +
                    val = 1;
 +
                    x=0;
 +
                    y=1;
 +
                }
 +
                if(i==2 || j==2 || i==31 || j==31)
 +
                    val = 2;
 +
                hmap[(i * (blockx+2) + j)*stride] = val;
 +
                hmap[(i * (blockx+2) + j)*stride+1] = x;
 +
                hmap[(i * (blockx+2) + j)*stride+2] = y;
 +
            }
 +
        }
 +
        /* transfer to device */
 +
        char* dmap = nullptr;
 +
        cudaMalloc(&dmap,(blockx+2)*(blocky+2)*sizeof(char)*3);
 +
        cudaMemcpy(dmap,hmap,(blockx+2)*(blocky+2)*sizeof(char)*3,cudaMemcpyHostToDevice);
 +
 +
        /* run iterations */
 +
        update<<<dGrid,dBlock>>>(d_data,dmap,nColumns, nRowsTotal, wx, wy,nIterations);
 +
 +
        /*DEBUG */ h_data[2*(nColumns+2)+2] = 100.0f;
 +
        /* output results from device to host */
 +
        cudaError_t state = cudaMemcpy(h_data,d_data,(nColumns+2)*(nRowsTotal+2)*sizeof(float),cudaMemcpyDeviceToHost);
 +
        if(state != cudaSuccess)
 +
            std::cout << "ERROR on () when copying data back to host with : " << cudaGetErrorString(state)<< std::endl;
 +
 +
        return h_data;
 +
    }
 +
 +
    void Poisson::show(std::ostream& ofs) const{
 +
        ofs << std::fixed << std::setprecision(1);
 +
        for (size_t j = 1; j <= nColumns ; j++) {
 +
            for (size_t i = 1 ; i <= nRowsTotal ; i++)
 +
                ofs << std::setw(8) << h_data[i * (nColumns+2) + j]<<",";
 +
            ofs << std::endl;
 +
        }
 +
    }
 +
    __global__ void update (float* data, char* dmap, int nCol, int nRow, const float wx, const float wy, unsigned int nIterations){
 +
        size_t j = blockDim.x * blockIdx.x + threadIdx.x + 1; /* for x axis */
 +
        size_t i = blockDim.y * blockIdx.y + threadIdx.y + 1; /* for y axis */
 +
        size_t y = threadIdx.x+1;
 +
        size_t x = threadIdx.y+1;
 +
 +
        const unsigned int bufferSize = (32+2)*(32+2);
 +
        __shared__ float localBuffer[ 2 * bufferSize ]; /* double local buffer with ghost cells */
 +
      // __shared__ char lmap[bufferSize];
 +
 +
        unsigned int buffer = 0;
 +
 +
        float prefetch = 0.0f;
 +
        /* copy information into first of the local buffer */
 +
        localBuffer[x*(32+2)+y] = data[i*(nCol+2)+j];
 +
        __syncthreads();
 +
 +
        const char lmap = dmap[(x*(32+2)+y)*3];
 +
        const char addx = dmap[(x*(32+2)+y)*3+1];
 +
        const char addy = dmap[(x*(32+2)+y)*3+2];
 +
 +
        /* prefetch */
 +
        if(lmap)
 +
            prefetch = data[(i+addx)*(nCol+2)+j+addy] ;
 +
 +
        /* run iterations */
 +
        for (unsigned int n = 0 ; n < nIterations; n++){
 +
            if(lmap)
 +
                localBuffer[buffer * bufferSize + (x+addx)*(32+2) + y+addy] = prefetch;
 +
            /* Calculate and store into the other buffer */
 +
            float curr = localBuffer[buffer*bufferSize + x * (32+2)+ y];
 +
            float dir1 = localBuffer[buffer*bufferSize + (x+1) * (32+2) +y];
 +
            float dir2 = localBuffer[buffer*bufferSize + (x-1) * (32+2) +y];
 +
            float dir3 = localBuffer[buffer*bufferSize + x * (32+2) + y + 1];
 +
            float dir4 = localBuffer[buffer*bufferSize + x * (32+2) + y - 1];
 +
            localBuffer[(1-buffer)*bufferSize + x*(32+2)+y] = curr + wx*(dir1+dir2-2.0f*curr) + wy*(dir3+dir4-2.0f*curr);
 +
            /* flip buffer */
 +
            buffer = 1-buffer;
 +
            /* for threads in charge of edges, share and obtain ghost cells */
 +
            if(lmap){
 +
                /* Copy over edges to global memory to be shared with neighboring blocks */
 +
                data[i*(nCol+2)+j] = localBuffer[buffer * bufferSize + x * (32+2) + y ];
 +
            }
 +
            __syncthreads();
 +
            if(lmap){
 +
                /* Copy back buffers from global memory */
 +
                prefetch = data[(i+addx)*(nCol+2)+j+addy] ;
 +
            }
 +
        }
 +
 +
        /* copy the output back into global memory */
 +
        data[i*(nCol+2)+j] = localBuffer[buffer * bufferSize + x * (32+2) + y ];
 +
        __syncthreads();
 +
    }
 +
}
 +
</source>
 +
|}
 +
{| class="wikitable mw-collapsible mw-collapsed"
 +
! For-loop Optimization - poissant-alt-ghost2.cuh
 +
|-
 +
|
 +
<source>
 +
/*
 +
* Poisson Method using two arrays. 
 +
* Non-Ghost Cells Method
 +
* Multiple PCIe Calls made, once per iteration
 +
* by Tony Sim
 +
*/
 +
#ifndef POISSON_H
 +
#define POISSON_H
 +
#include <fstream>
 +
#include <cuda_runtime.h>
 +
 +
namespace DPS{
 +
    class Poisson {
 +
        unsigned int blockx;
 +
        unsigned int blocky;
 +
        unsigned int nRowsTotal;
 +
        unsigned int nColumns;
 +
        unsigned int bufferSize;
 +
        float* h_data;
 +
        float* d_data;
 +
        int bufferSide;
 +
 +
        void bufferSwitch(){ bufferSide = 1 - bufferSide; };
 +
        bool devMemSet();
 +
 +
        public:
 +
        Poisson() = delete;
 +
        Poisson(std::ifstream& ifs);
 +
        Poisson(const size_t r, const size_t c, float* d);
 +
        ~Poisson();
 +
        float* operator()(const size_t iteration, const float wx, const float wy);
 +
        float* operator()(const size_t iteration){
 +
            return operator()(iteration,0.1,0.1);
 +
        }
 +
        void show(std::ostream& ofs) const;
 +
    };
 +
    __global__ void update (float* data, char* dmap, int nCol, int nRow, const float wx, const float wy, unsigned int nIterations);
 +
}
 +
#endif
 +
 +
</source>
 +
|}
 +
 +
==== Optimization Details ====
 +
===== PCIe Version =====
 +
* Coalesced Memory - Large performance boost.
 +
* Prefetch - this had minor to no effect on the performance.
 +
 +
===== For-loop Version =====
 +
* Shared Memory - Small boost. Used technique called Ghost Cells where updated information is shared over global memory as needed to perform the next iteration.
 +
* Prefetch - Small boost. Information are fetched first into register in the previous iteration to be copied in the current iteration prior to calculation.
 +
* Coalesed Memory - Large boost.
 +
* Logic change - To minimize the number of condition calls, a predefined map of instruction was created on the host based on the block dimension information.  Using this information, the if statement had been cut down to almost 1/4, showing noticeable performance increase.
 +
 +
==== Result ====
 +
 +
'''POST Presentation Results''' Contrary to the presentation's conclusion, the ghost-cell method proved to be more effective with some changes to the logic than simpler global-memory-based counterpart.  It does require some preparation in the host machine. The gain is small.
 +
 +
[[File:optimized.png|center|frame|GPU highlights.  para-ghost-pre-co2, which implements Ghost Cell + Prefetch + Coaleased memory + logic change, is slightly faster than simpler Prefetch+Coaleased memory that uses Global Memory. Both methods are superior than calling the conditional-less kernel 1000 times over PCIe.]]
 +
[[File:all.png|center|frame|UsinGPU highlights.  Ghost Cell + Prefetch + Coaleased memory + logic change is slightly faster than simpler Prefetch+Coaleased memory that uses Global Memoryg GPU significantly improved Calculation Time over the CPU counterparts.]]

Latest revision as of 01:14, 7 April 2019


GPU610/DPS915 | Student List | Group and Project Index | Student Resources | Glossary

Ghost Cells

Team Members

  1. Tony Sim, Issue Dumper
  2. Robert Dittrich, Issue Collector
  3. Inna Zhogova, Issue Resolver

Email All

Progress

Assignment 1

Tony

Subject: Jacobi's method for Poisson's equation

Source Code
poissan.h 
#ifndef POISSON_H
#define POISSON_H
#include <fstream>

namespace DPS{
    class Poisson {
        size_t nRowsTotal;
        size_t nColumns;
        float* data;
        int bufferSide;

        void update (size_t startRow, size_t endRow, const float wx, const float wy);
        void bufferSwitch(){ bufferSide = 1 - bufferSide; };

        public:
        Poisson(std::ifstream& ifs);
        Poisson(const size_t r, const size_t c, float* d);
        ~Poisson(){ delete[] data; };
        float* operator()(const size_t iteration, const float wx, const float wy);
        float* operator()(const size_t iteration){
            return operator()(iteration,0.1,0.1);
        }
        void show(std::ostream& ofs) const;
    };
}
#endif
poissan.cpp 
#include <cstring>
#include <cstdlib>
#include <iomanip>
#include <iostream>
#include <string>
#include "poisson.h"

namespace DPS{
    Poisson::Poisson(std::ifstream& ifs){
        std::string line;
        bufferSide = 0;

        /* find number of columns */
        std::getline(ifs,line);
        for (size_t i = 0 ; i < line.size() ; i++){
            if(line[i]==' ') nColumns++;
        }
        nColumns++;

        /* find number of rows */
        nRowsTotal++; /* already fetched one */
        while(std::getline(ifs,line))
            nRowsTotal++;
        ifs.clear();

        try{
            data = new float[nColumns * nRowsTotal * 2];
        }
        catch (...){
            throw std::runtime_error("Failed to Allocate Memory");
        }

        /* readin data */
        ifs.seekg(0,ifs.beg);
        std::cout << ifs.tellg() << std::endl;
        for (size_t i = 0 ; i < nRowsTotal * nColumns ; i++) {
            ifs >> data[i];
        }

        std::memset(data+nRowsTotal*nColumns,0,nRowsTotal*nColumns*sizeof(float));
    }

    Poisson::Poisson(const size_t r, const size_t c, float* d){
        bufferSide = 0;
        nRowsTotal = r;
        nColumns = c;
        try{
            data = new float[r*c*2];
        }
        catch (...){
            throw std::runtime_error("Failed to Allocate Memory");
        }
        std::memcpy(data,d,r*c*sizeof(float));
        std::memset(data+r*c,0,r*c*sizeof(float));
    }

    void Poisson::update (size_t startRow, size_t endRow, const float wx, const float wy){
        float* x_new = data + (1-bufferSide)*nRowsTotal*nColumns;
        float* x_old = data + bufferSide*nRowsTotal*nColumns;
        for (size_t i = startRow; i <= endRow; i++)
            for (size_t j = 1; j < nColumns - 1; j++)
                x_new[i * nColumns + j] = x_old[i * nColumns + j]
                    + wx * (x_old[(i + 1) * nColumns + j] + x_old[(i - 1) * nColumns + j]
                            - 2.0f * x_old[i * nColumns + j])
                    + wy * (x_old[i * nColumns + j + 1] + x_old[i * nColumns + j - 1]
                            - 2.0f * x_old[i * nColumns + j]);
    }

    float* Poisson::operator()(const size_t nIterations, const float wx, const float wy){
        for (size_t i = 0; i < nIterations; i++) {
            update(0, nRowsTotal-1, wx, wy);
            bufferSwitch();
        }
        return data;
    }

    void Poisson::show(std::ostream& ofs) const{
        ofs << std::fixed << std::setprecision(1);
        for (size_t j = 0; j < nColumns ; j++) {
            for (size_t i = 0 ; i < nRowsTotal ; i++)
                ofs << std::setw(8) << data[ bufferSide*nColumns*nRowsTotal + i * nColumns + j];
            ofs << std::endl;
        }
    }
}
main.cpp 
// based on code from LLNL tutorial mpi_heat2d.c
// Master-Worker Programming Model
// Chris Szalwinski - 2018/11/13
// Adopted by Tony Sim - 2019/02/16
#include <iostream>
#include <fstream>
#include <iomanip>
#include <cstdlib>
#include <stdexcept>

#include "poisson.h"

// solution constants
const size_t NONE = 0;
const size_t MINPARTITIONS = 1;
const size_t MAXPARTITIONS = 7;
// weights
const float wx = 0.1f;
const float wy = 0.1f;

int main(int argc, char** argv) {
    if (argc != 4) {
        std::cerr << "*** Incorrect number of arguments ***\n";
        std::cerr << "Usage: " << argv[0]
            << " input_file output_file no_of_iterations\n";
        return 1;
    }

    std::ifstream input(argv[1]);
    std::ofstream output(argv[2]);
    std::ofstream temp("init.csv");

    if(!input.is_open()){
        std::cerr << "Invalid Input File" << std::endl;
        return 2;
    }
    if(!output.is_open()){
        std::cerr << "Invalid Output File" << std::endl;
        return 2;
    }

    DPS::Poisson* p = nullptr;
    try{
        p = new DPS::Poisson(input);
    }
    catch(std::exception& e){
        std::cerr << "Error: " << e.what() << std::endl;
    }

    p->show(temp);

    size_t nIterations = std::atoi(argv[3]);

    (*p)(nIterations);

    // write results to file
    p->show(output);

    delete p;

}
Introduction

The presented code simulates heat map using Jacobi's method for Poisson's equation. It is represented in a 2D array, and each element updates its value based on the adjacent elements at a given moment. Each iteration represent one instance in time. By repeating the calculation over the entire array through multiple iterations, we can estimate the state of the heat transfer after a given time interval.

Profiling

The profiling was conducted using a data set of 79 rows and 205 columns over 150000 iterations.

Flat profile

Flat profile:

Each sample counts as 0.01 seconds. 

 %   cumulative   self              self     total           
time   seconds   seconds    calls  us/call  us/call  name    
98.57      2.75     2.75   150000    18.33    18.33  DPS::Poisson::update(unsigned long, unsigned long, float, float)
 0.00      2.75     0.00        1     0.00     0.00  _GLOBAL__sub_I__ZN3DPS7PoissonC2ERSt14basic_ifstreamIcSt11char_traitsIcEE
 0.00      2.75     0.00        1     0.00     0.00  _GLOBAL__sub_I_main


Call graph 
                       Call graph


granularity: each sample hit covers 2 byte(s) for 0.36% of 2.75 seconds

index % time self children called name 

               2.75    0.00  150000/150000      DPS::Poisson::operator()(unsigned long, float, float) [2]

[1] 100.0 2.75 0.00 150000 DPS::Poisson::update(unsigned long, unsigned long, float, float) [1] 

                                                <spontaneous>

[2] 100.0 0.00 2.75 DPS::Poisson::operator()(unsigned long, float, float) [2] 

               2.75    0.00  150000/150000      DPS::Poisson::update(unsigned long, unsigned long, float, float) [1]

               0.00    0.00       1/1           __libc_csu_init [21]

[10] 0.0 0.00 0.00 1 _GLOBAL__sub_I__ZN3DPS7PoissonC2ERSt14basic_ifstreamIcSt11char_traitsIcEE [10] 

               0.00    0.00       1/1           __libc_csu_init [21]

[11] 0.0 0.00 0.00 1 _GLOBAL__sub_I_main [11]


Index by function name 

 [10] _GLOBAL__sub_I__ZN3DPS7PoissonC2ERSt14basic_ifstreamIcSt11char_traitsIcEE (poisson.cpp) [11] _GLOBAL__sub_I_main (main.cpp) [1] DPS::Poisson::update(unsigned long, unsigned long, float, float)


Analysis

given 98.57 percent of time is spent on the update() function, it is considered the hotspot. Total time taken was 2.75.

If we consider a GPU environment with 1000 cores, we can estimate the following speedup: S1000 = 1/(1-.9857 + .9857/1000) = 65.00 In fact, the speed will decrease from 2.75 seconds to 0.0450 seconds.

As each iteration depends on the product of the previous iteration, there is a dependency resolution that might hamper the parallel process. Consideration may also be extended to resolving ghost cells across different SMX while using the device global memory as the transfer pipeline.

Robert

Multi Sampling Anti Aliasing
Source Files
main.cpp 
#include <cstdint>
#include <iostream>
#include <algorithm>

#define STB_IMAGE_IMPLEMENTATION
#include "stb_image.h"
#define STB_IMAGE_WRITE_IMPLEMENTATION
#include "stb_image_write.h"

#include "vec3.h"

struct Point {
	int x;
	int y;
};
uint8_t* msaa(const uint8_t* input, uint8_t* output, int width, int height, int channels, int samples) {

	// directions is (samples * 2 + 1) ^ 2
	int totalPoints = (samples * 2 + 1) * (samples * 2 + 1);
	Point* directions = new Point[totalPoints];
	size_t idx = 0;
	for (int i = -samples; i <= samples; i++) {
		for (int j = -samples; j <= samples; j++) {
			directions[idx].x = i;
			directions[idx].y = j;
			idx++;
		}
	}

	int  x, y, cx, cy;
	Vec3<int> average;
	for (size_t i = 0; i < width*height; i++) {
		x = i % width * channels;
		y = i / width * channels;
		for (size_t j = 0; j < totalPoints; j++) {
			cx = x + directions[j].x * channels;
			cy = y + directions[j].y * channels;
			cx = std::clamp(cx, 0, width* channels);
			cy = std::clamp(cy, 0, height* channels);
			average.add(input[width * cy + cx], input[width * cy + cx + 1], input[width * cy + cx + 2]);
		}
		average.set(average.getX() / totalPoints, average.getY() / totalPoints, average.getZ() / totalPoints);
		output[(width * y + x)] = average.getX();
		output[(width * y + x) + 1] = average.getY();
		output[(width * y + x) + 2] = average.getZ();
		average.set(0, 0, 0);
	}
	delete[] directions;
	return output;
}

int main(int argc, char* argv[]) {
	if (argc != 5) {
		std::cerr << argv[0] << ": invalid number of arguments\n";
		std::cerr << "Usage: " << argv[0] << "  input output sample_size passes \n";
		system("pause");
		return 1;
	}
	int width, height, channels;
	uint8_t* rgb_read = stbi_load(argv[1], &width, &height, &channels, STBI_rgb);
	if (channels != 3) {
		std::cout << "Incorrect channels" << std::endl;
		system("pause");
		return 2;
	}
	int samples = std::atoi(argv[3]);
	int passes = std::atoi(argv[4]);
	uint8_t* rgb_write = new uint8_t[width*height*channels];

	rgb_write = msaa(rgb_read, rgb_write, width, height, channels, samples);
	for (int i = 1; i < passes; i++) {
		rgb_write = msaa(rgb_write, rgb_write, width, height, channels, samples);
	}
	stbi_write_png(argv[2], width, height, channels, rgb_write, width*channels);
	stbi_image_free(rgb_read);
	delete[] rgb_write;
	std::cout << "AA Done using " << samples << " sample size" << " over " << passes << " passes" << std::endl;
	system("pause");
	return 0;
}
vec3.h 
#ifndef VEC3_H
#define VEC3_H
#include <iostream>
template <class T>
class Vec3 {
private:
	T x;
	T y;
	T z;
public:
	Vec3() {
		x = 0;
		y = 0;
		z = 0;
	};
	Vec3(T x_, T y_, T z_) {
		x = x_;
		y = y_;
		z = z_;
	}
	void set(const T &x_, const T &y_, const T &z_) {
		x = x_;
		y = y_;
		z = z_;
	}
	void add(const T &x_, const T &y_, const T &z_) {
		x += x_;
		y += y_;
		z += z_;
	}
	
	T getX() const { return x; }
	T getY() const { return y; }
	T getZ() const { return z; }

	void setX(const T &x_) { x = x_; }
	void setY(const T &y_) { y = y_; }
	void setZ(const T &z_) { z = z_; }

	static T dot(const Vec3& vec1, const Vec3& vec2) {
		return  vec1.x * vec2.x + vec1.y * vec2.y + vec1.z * vec2.z;
	}
	T dot(const Vec3 &vec) const {
		return x * vec.x + y * vec.y + z * vec.z;
	}
	void display(std::ostream& os) {
		os << "x: " << x << ", y: " << y << ", z: " << z << "\n";
	}

};

#endif // !VEC3_H

stb_image_write.h stb_image.h

Introduction

For my selection I chose to do Anti Aliasing since I see it a lot in video games but I never really knew how it worked. There are other anti aliasing methods like FXAA which is fast approximate anti aliasing but it seemed a lot more complicated than MSAA. The way I approached this problem is by getting the color of the pixels around a pixel. In you can specify the distance it will search in the application flags. In my implementation you specify an input file, output file, the radius of pixels to sample and how many passes to take on the image. In my tests the command line options I used was an image I made in paint with 4 sample size and 4 passes.

Before

MSAABefore.png

After

MSAAAfter.png.

Profiling
Profiling 
Flat profile:

Each sample counts as 0.01 seconds.
  %   cumulative   self              self     total
 time   seconds   seconds    calls  ms/call  ms/call  name
 85.72      0.18     0.18                             msaa(unsigned char const*, unsigned char*, int, int, int, int)
 14.29      0.21     0.03        1    30.00    30.00  stbi_zlib_compress
  0.00      0.21     0.00   127820     0.00     0.00  stbiw__zlib_flushf(unsigned char*, unsigned int*, int*)
  0.00      0.21     0.00    96904     0.00     0.00  stbiw__zhash(unsigned char*)
  0.00      0.21     0.00     5189     0.00     0.00  stbi__fill_bits(stbi__zbuf*)
  0.00      0.21     0.00     2100     0.00     0.00  stbiw__encode_png_line(unsigned char*, int, int, int, int, int, int, signed char*)
  0.00      0.21     0.00     2014     0.00     0.00  stbiw__sbgrowf(void**, int, int) [clone .constprop.58]
  0.00      0.21     0.00       38     0.00     0.00  stbi__get16be(stbi__context*)
  0.00      0.21     0.00       19     0.00     0.00  stbi__get32be(stbi__context*)
  0.00      0.21     0.00        3     0.00     0.00  stbi__skip(stbi__context*, int)
  0.00      0.21     0.00        3     0.00     0.00  stbiw__wpcrc(unsigned char**, int)
  0.00      0.21     0.00        3     0.00     0.00  stbi__stdio_read(void*, char*, int)
  0.00      0.21     0.00        3     0.00     0.00  stbi__zbuild_huffman(stbi__zhuffman*, unsigned char const*, int)
  0.00      0.21     0.00        2     0.00     0.00  stbi__mad3sizes_valid(int, int, int, int)
  0.00      0.21     0.00        1     0.00     0.00  _GLOBAL__sub_I_stbi_failure_reason
  0.00      0.21     0.00        1     0.00     0.00  stbi__getn(stbi__context*, unsigned char*, int)
  0.00      0.21     0.00        1     0.00     0.00  stbi__readval(stbi__context*, int, unsigned char*)
  0.00      0.21     0.00        1     0.00     0.00  stbi__load_main(stbi__context*, int*, int*, int*, int, stbi__result_info*, int)
  0.00      0.21     0.00        1     0.00     0.00  stbi__parse_zlib(stbi__zbuf*, int)
  0.00      0.21     0.00        1     0.00     0.00  stbi__malloc_mad3(int, int, int, int)
  0.00      0.21     0.00        1     0.00     0.00  stbi__parse_png_file(stbi__png*, int, int)
  0.00      0.21     0.00        1     0.00     0.00  stbi__start_callbacks(stbi__context*, stbi_io_callbacks*, void*)
  0.00      0.21     0.00        1     0.00     0.00  stbi__decode_jpeg_header(stbi__jpeg*, int)
  0.00      0.21     0.00        1     0.00     0.00  stbi__compute_huffman_codes(stbi__zbuf*)
  0.00      0.21     0.00        1     0.00     0.00  stbi__load_and_postprocess_8bit(stbi__context*, int*, int*, int*, int)
  0.00      0.21     0.00        1     0.00     0.00  stbi_load_from_file
  0.00      0.21     0.00        1     0.00    30.00  stbi_write_png_to_mem
  0.00      0.21     0.00        1     0.00     0.00  stbi_zlib_decode_malloc_guesssize_headerflag
Conclusion

Since the msaa function I wrote is a hotspot of the program I would suggest offloading part of it to a GPU, more specifically the part that finds the average of colors of the nearby pixels. That part also does not depend on previous iterations to finish so it is a prime candidate for parallelization.

Inna

Subject: Data compression - LWZ algorithm.

Source: http://www.cplusplus.com/articles/iL18T05o/#Version1

I tested the following source code for a compression and decompression of .txt files and a gif.

lwz.cpp( ... ) 
///
/// @file
/// @author Julius Pettersson
/// @copyright MIT/Expat License.
/// @brief LZW file compressor
/// @version 1
///
/// This is the C++11 implementation of a Lempel-Ziv-Welch single-file command-line compressor.
/// It uses the simpler fixed-width code compression method.
/// It was written with Doxygen comments.
///
/// @see http://en.wikipedia.org/wiki/Lempel%E2%80%93Ziv%E2%80%93Welch
/// @see http://marknelson.us/2011/11/08/lzw-revisited/
/// @see http://www.cs.duke.edu/csed/curious/compression/lzw.html
/// @see http://warp.povusers.org/EfficientLZW/index.html
/// @see http://en.cppreference.com/
/// @see http://www.doxygen.org/
///

#include <cstdint>
#include <cstdlib>
#include <exception>
#include <fstream>
#include <ios>
#include <iostream>
#include <istream>
#include <limits>
#include <map>
#include <ostream>
#include <stdexcept>
#include <string>
#include <vector>

/// Type used to store and retrieve codes.
using CodeType = std::uint16_t;

namespace globals {

/// Dictionary Maximum Size (when reached, the dictionary will be reset)
const CodeType dms {std::numeric_limits<CodeType>::max()};

} // namespace globals

///
/// @brief Helper operator intended to simplify code.
/// @param vc   original vector
/// @param c    element to be appended
/// @returns vector resulting from appending `c` to `vc`
///
std::vector<char> operator + (std::vector<char> vc, char c)
{
    vc.push_back(c);
    return vc;
}

///
/// @brief Compresses the contents of `is` and writes the result to `os`.
/// @param [in] is      input stream
/// @param [out] os     output stream
///
void compress(std::istream &is, std::ostream &os)
{
    std::map<std::vector<char>, CodeType> dictionary;

    // "named" lambda function, used to reset the dictionary to its initial contents
    const auto reset_dictionary = [&dictionary] {
        dictionary.clear();

        const long int minc = std::numeric_limits<char>::min();
        const long int maxc = std::numeric_limits<char>::max();

        for (long int c = minc; c <= maxc; ++c)
        {
            // to prevent Undefined Behavior, resulting from reading and modifying
            // the dictionary object at the same time
            const CodeType dictionary_size = dictionary.size();

            dictionary[{static_cast<char> (c)}] = dictionary_size;
        }
    };

    reset_dictionary();

    std::vector<char> s; // String
    char c;

    while (is.get(c))
    {
        // dictionary's maximum size was reached
        if (dictionary.size() == globals::dms)
            reset_dictionary();

        s.push_back(c);

        if (dictionary.count(s) == 0)
        {
            // to prevent Undefined Behavior, resulting from reading and modifying
            // the dictionary object at the same time
            const CodeType dictionary_size = dictionary.size();

            dictionary[s] = dictionary_size;
            s.pop_back();
            os.write(reinterpret_cast<const char *> (&dictionary.at(s)), sizeof (CodeType));
            s = {c};
        }
    }

    if (!s.empty())
        os.write(reinterpret_cast<const char *> (&dictionary.at(s)), sizeof (CodeType));
}

///
/// @brief Decompresses the contents of `is` and writes the result to `os`.
/// @param [in] is      input stream
/// @param [out] os     output stream
///
void decompress(std::istream &is, std::ostream &os)
{
    std::vector<std::vector<char>> dictionary;

    // "named" lambda function, used to reset the dictionary to its initial contents
    const auto reset_dictionary = [&dictionary] {
        dictionary.clear();
        dictionary.reserve(globals::dms);

        const long int minc = std::numeric_limits<char>::min();
        const long int maxc = std::numeric_limits<char>::max();

        for (long int c = minc; c <= maxc; ++c)
            dictionary.push_back({static_cast<char> (c)});
    };

    reset_dictionary();

    std::vector<char> s; // String
    CodeType k; // Key

    while (is.read(reinterpret_cast<char *> (&k), sizeof (CodeType)))
    {
        // dictionary's maximum size was reached
        if (dictionary.size() == globals::dms)
            reset_dictionary();

        if (k > dictionary.size())
            throw std::runtime_error("invalid compressed code");

        if (k == dictionary.size())
            dictionary.push_back(s + s.front());
        else
        if (!s.empty())
            dictionary.push_back(s + dictionary.at(k).front());

        os.write(&dictionary.at(k).front(), dictionary.at(k).size());
        s = dictionary.at(k);
    }

    if (!is.eof() || is.gcount() != 0)
        throw std::runtime_error("corrupted compressed file");
}

///
/// @brief Prints usage information and a custom error message.
/// @param s    custom error message to be printed
/// @param su   Show Usage information
///
void print_usage(const std::string &s = "", bool su = true)
{
    if (!s.empty())
        std::cerr << "\nERROR: " << s << '\n';

    if (su)
    {
        std::cerr << "\nUsage:\n";
        std::cerr << "\tprogram -flag input_file output_file\n\n";
        std::cerr << "Where `flag' is either `c' for compressing, or `d' for decompressing, and\n";
        std::cerr << "`input_file' and `output_file' are distinct files.\n\n";
        std::cerr << "Examples:\n";
        std::cerr << "\tlzw_v1.exe -c license.txt license.lzw\n";
        std::cerr << "\tlzw_v1.exe -d license.lzw new_license.txt\n";
    }

    std::cerr << std::endl;
}

///
/// @brief Actual program entry point.
/// @param argc             number of command line arguments
/// @param [in] argv        array of command line arguments
/// @retval EXIT_FAILURE    for failed operation
/// @retval EXIT_SUCCESS    for successful operation
///
int main(int argc, char *argv[])
{
    if (argc != 4)
    {
        print_usage("Wrong number of arguments.");
        return EXIT_FAILURE;
    }

    enum class Mode {
        Compress,
        Decompress
    };

    Mode m;

    if (std::string(argv[1]) == "-c")
        m = Mode::Compress;
    else
    if (std::string(argv[1]) == "-d")
        m = Mode::Decompress;
    else
    {
        print_usage(std::string("flag `") + argv[1] + "' is not recognized.");
        return EXIT_FAILURE;
    }

    std::ifstream input_file(argv[2], std::ios_base::binary);

    if (!input_file.is_open())
    {
        print_usage(std::string("input_file `") + argv[2] + "' could not be opened.");
        return EXIT_FAILURE;
    }

    std::ofstream output_file(argv[3], std::ios_base::binary);

    if (!output_file.is_open())
    {
        print_usage(std::string("output_file `") + argv[3] + "' could not be opened.");
        return EXIT_FAILURE;
    }

    try
    {
        input_file.exceptions(std::ios_base::badbit);
        output_file.exceptions(std::ios_base::badbit | std::ios_base::failbit);

        if (m == Mode::Compress)
            compress(input_file, output_file);
        else
        if (m == Mode::Decompress)
            decompress(input_file, output_file);
    }
    catch (const std::ios_base::failure &f)
    {
        print_usage(std::string("File input/output failure: ") + f.what() + '.', false);
        return EXIT_FAILURE;
    }
    catch (const std::exception &e)
    {
        print_usage(std::string("Caught exception: ") + e.what() + '.', false);
        return EXIT_FAILURE;
    }

    return EXIT_SUCCESS;
}
Tested data

1. book.txt - a 343 kilobyte text file.

2. words.txt - a 4.7 megabyte text file.

3. fire.gif - a 309 kilobyte graphical image.

Flat Profiles
Book

Flat profile for compression:

BookComp.jpg

Flat profile for decompression:

BookDecomp.jpg

Text

Flat profile for compression:

TextCompression.jpg

Flat profile for decompression:

TextDecomp.jpg

GIF

Flat profile for compression:

FireComp.jpg

Flat profile for decompression:

FireDecomp.jpg

Assignment 2

Source Files
poisson-pcie.cu 
/*
 * Poisson Method using two arrays.  
 * Non-Ghost Cells Method
 * Multiple PCIe Calls made, once per iteration
 * by Tony Sim
 */
#include <cstring>
#include <cstdlib>
#include <iomanip>
#include <iostream>
#include <string>
#include <cuda_runtime.h>
#include "poisson.cuh"

namespace DPS{

    Poisson::Poisson(std::ifstream& ifs) {
        std::string line;
        nColumns = 0;
        bufferSide = 0;
        nRowsTotal = 0;
        /* find number of columns */
        std::getline(ifs,line);
        for (size_t i = 0 ; i < line.size() ; i++){
            if(line[i]==' ') nColumns++;
        }
        nColumns++;

        /* find number of rows */
        nRowsTotal++; /* already fetched one */
        while(std::getline(ifs,line))
            nRowsTotal++;
        ifs.clear();

        try{
            for (size_t i = 0 ; i < 2 ; i++)
                h_data[i] = new float[ (nColumns+2) * (nRowsTotal+2)]; /* add edge buffers */
        }
        catch (...){
            throw std::runtime_error("Failed to Allocate Memory");
        }

        /* readin data */
        std::cout <<"Reading in data"<<std::endl;
        ifs.seekg(0,ifs.beg);
        /* allocate memory to all but the edge buffer, index 0 and max for each row and column */
        for (size_t i = 0 ; i < nRowsTotal+2 ; i++){
            for (size_t j = 0 ; j < nColumns+2 ; j++){
                float val = 0;
                if(!(i == 0 || i == nRowsTotal + 1 || j == 0 || j == nColumns + 1))
                    ifs >> val;
                h_data[0][i*(nColumns+2)+j] = val;
            }
        }

        std::cout <<"Setting buffer"<<std::endl;
        std::memset(h_data[1],0,(nRowsTotal+2)*(nColumns+2)*sizeof(float));
        bool state = devMemSet();

    }

    Poisson::Poisson(const size_t r, const size_t c, float* d) {
        bufferSide = 0;
        nRowsTotal = r;
        nColumns = c;
        try{
            h_data[0] = new float[(r+2)*(c+2)];
            h_data[1] = new float[(r+2)*(c+2)];
        }
        catch (...){
            throw std::runtime_error("Failed to Allocate Memory");
        }
        std::memcpy(h_data[0],d,(r+2)*(c+2)*sizeof(float));
        std::memset(h_data[1],0,(r+2)*(c+2)*sizeof(float));
        devMemSet();
    }

    Poisson::~Poisson(){
        for( size_t i = 0 ; i < 2 ; i++){
            delete [] h_data[i];
            cudaFree(d_data[i]);
        }
    }

    bool Poisson::devMemSet(){
        for(size_t i = 0 ; i < 2 ; i++){
            cudaMalloc(&d_data[i],(nColumns+2)*(nRowsTotal+2)*sizeof(float));
            if(d_data[i] != nullptr){
                cudaError_t state = cudaMemcpy((void*)d_data[i],(const void*)h_data[i],(nColumns+2)*(nRowsTotal+2)*sizeof(float),cudaMemcpyHostToDevice);
                if(state != cudaSuccess)
                        std::cerr << "ERROR on devMemSet for : " << i <<" with : " << cudaGetErrorString(state)<< std::endl;
            }
        }
        return d_data[0]&&d_data[1];
    }


    float* Poisson::operator()(const size_t nIterations, const float wx, const float wy){

        /* calculate the grid, block, where block has 1024 threads total */
        unsigned int blockx = 32;
        unsigned int blocky = 32;
        unsigned int gridx = ((nRowsTotal+2)+blockx-1)/blockx;
        unsigned int gridy = ((nRowsTotal+2)+blocky-1)/blocky;

        /* create dim3 */
        dim3 dBlock= {blockx,blocky};
        dim3 dGrid = {gridx,gridy};

        /* run iterations */
        for (size_t i = 0; i < nIterations; i++) {
            update<<<dGrid,dBlock>>>(d_data[1-bufferSide],d_data[bufferSide],nColumns, nRowsTotal, wx, wy);
            bufferSwitch();
        }

        /* DEBUG */ h_data[bufferSide][1*(nColumns+2) + 1] = 100.0f;
        /* output results from device to host */
        cudaError_t state = cudaMemcpy(h_data[bufferSide],d_data[bufferSide],(nColumns+2)*(nRowsTotal+2)*sizeof(float),cudaMemcpyDeviceToHost);
        if(state != cudaSuccess)
            std::cout << "ERROR on () when copying data back to host" <<" with : " << cudaGetErrorString(state)<< std::endl;

        return h_data[bufferSide];
    }

    void Poisson::show(std::ostream& ofs) const{
        ofs << std::fixed << std::setprecision(1);
        for (size_t j = 1; j <= nColumns ; j++) {
            for (size_t i = 1 ; i <= nRowsTotal ; i++)
                ofs << std::setw(8) << h_data[bufferSide][i * (nColumns+2) + j]<<",";
            ofs << std::endl;
        }
    }
    __global__ void update (float* newD, const float* currD, int nCol, int nRow, const float wx, const float wy){
        size_t i = blockDim.x * blockIdx.x + threadIdx.x + 1; /* for x axis */
        size_t j = blockDim.y * blockIdx.y + threadIdx.y + 1; /* for y axis */
        newD[i*(nCol+2)+j] = currD[i * (nCol+2) +j] + wx*(currD[(i+1) * (nCol+2) +j] + currD[(i-1) * (nCol+2) +j] - 2.0f * currD[i * (nCol+2) +j] ) + wy*( currD[i * (nCol+2) +j+1] + currD[i * (nCol+2) +j-1] - 2.0f * currD[i * (nCol+2) +j]) ;
        __syncthreads();
    }
}
poisson-alt.cu 
/*
 * Poisson Method using two arrays.  
 * Non-Ghost Cells Method
 * One PCIe Call made, iterations done in kernel
 * by Tony Sim
 */
#include <cstring>
#include <cstdlib>
#include <iomanip>
#include <iostream>
#include <string>
#include <cuda_runtime.h>
#include "poisson-alt.cuh"

namespace DPS{

    Poisson::Poisson(std::ifstream& ifs) {
        blockx = 32;
        blocky = 32;

        std::string line;
        nColumns = 0;
        bufferSide = 0;
        nRowsTotal = 0;
        /* find number of columns */
        std::getline(ifs,line);
        for (size_t i = 0 ; i < line.size() ; i++){
            if(line[i]==' ') nColumns++;
        }
        nColumns++;

        /* find number of rows */
        nRowsTotal++; /* already fetched one */
        while(std::getline(ifs,line))
            nRowsTotal++;
        ifs.clear();

        int sizeX = ((nColumns + 2 + blockx + 2 - 1)/(blockx+2))*(blockx+2);
        int sizeY = ((nRowsTotal + 2 + blocky + 2 - 1)/(blocky+2))*(blocky+2);
        bufferSize = sizeX * sizeY;
        std::cout << "Allocate initial memory" << std::endl;
        try{
            h_data = new float[ bufferSize ]; /* add edge buffers */
        }
        catch (...){
            throw std::runtime_error("Failed to Allocate Memory");
        }

        /* readin data */
        std::cout <<"Reading in data"<<std::endl;
        ifs.seekg(0,ifs.beg);
        /* allocate memory to all but the edge buffer, index 0 and max for each row and column */
        std::memset(h_data,0,bufferSize);
        for (size_t i = 0 ; i < nRowsTotal+2 ; i++){
            for (size_t j = 0 ; j < nColumns+2 ; j++){
                float val = 0;
                if(!(i == 0 || i == nRowsTotal + 1 || j == 0 || j == nColumns + 1))
                    ifs >> val;
                h_data[i*(nColumns+2)+j] = val;
            }
        }

        std::cout <<"Setting buffer"<<std::endl;
        bool state = devMemSet();

    }

    Poisson::Poisson(const size_t r, const size_t c, float* d) {
        bufferSide = 0;
        nRowsTotal = r;
        nColumns = c;
        try{
            h_data = new float[(r+2)*(c+2)];
        }
        catch (...){
            throw std::runtime_error("Failed to Allocate Memory");
        }
        std::memcpy(h_data,d,(r+2)*(c+2)*sizeof(float));
        devMemSet();
    }

    Poisson::~Poisson(){
        delete [] h_data;
        cudaFree(d_data);
        cudaDeviceReset();
    }

    bool Poisson::devMemSet(){

        /* create double buffer */
        cudaMalloc(&d_data,2* bufferSize * sizeof(float));

        if(d_data != nullptr){
            /* copy the initial information to the first buffer */
            cudaError_t state = cudaMemcpy((void*)d_data,(const void*)h_data, bufferSize * sizeof(float),cudaMemcpyHostToDevice);
            if(state != cudaSuccess)
                std::cerr << "ERROR on devMemSet at cudaMemcpy : " << cudaGetErrorString(state)<< std::endl;

            /* set the second buffer to zero */
            state = cudaMemset( d_data + bufferSize , 0, bufferSize * sizeof(float));
            if(state != cudaSuccess)
                std::cerr << "ERROR on devMemSet at cudaMemset : " << cudaGetErrorString(state)<< std::endl;

        }
        return d_data;
    }

    float* Poisson::operator()(const size_t nIterations, const float wx, const float wy){

        /* calculate the grid, block, where block has 1024 threads total */
        unsigned int gridx = ((nRowsTotal+2)+blockx-1)/blockx;
        unsigned int gridy = ((nRowsTotal+2)+blocky-1)/blocky;

        /* create dim3 */
        dim3 dBlock= {blockx,blocky};
        dim3 dGrid = {gridx,gridy};

        /* run iterations */
        update<<<dGrid,dBlock>>>(d_data,nColumns, nRowsTotal, wx, wy,nIterations,bufferSize);

        /*DEBUG */ h_data[2*(nColumns+2)+2] = 100.0f;
        /* output results from device to host */
        cudaError_t state = cudaMemcpy(h_data,d_data,(nColumns+2)*(nRowsTotal+2)*sizeof(float),cudaMemcpyDeviceToHost);
        if(state != cudaSuccess)
            std::cout << "ERROR on () when copying data back to host with : " << cudaGetErrorString(state)<< std::endl;

        return h_data;
    }

    void Poisson::show(std::ostream& ofs) const{
        ofs << std::fixed << std::setprecision(1);
        for (size_t j = 1; j <= nColumns ; j++) {
            for (size_t i = 1 ; i <= nRowsTotal ; i++)
                ofs << std::setw(8) << h_data[i * (nColumns+2) + j]<<",";
            ofs << std::endl;
        }
    }
    __global__ void update (float* data, int nCol, int nRow, const float wx, const float wy, unsigned int nIterations, unsigned int bufferSize){
        size_t i = blockDim.x * blockIdx.x + threadIdx.x + 1; /* for x axis */
        size_t j = blockDim.y * blockIdx.y + threadIdx.y + 1; /* for y axis */

        unsigned int buffer = 0;

        /* run iterations */
        for (unsigned int n = 0 ; n < nIterations; n++){
            /* Calculate and store into the other buffer */
            data[(1-buffer)*bufferSize + i*(nCol+2)+j] = data[buffer*bufferSize + i * (nCol+2)+ j]
                + wx * (data[buffer*bufferSize + (i+1) * (nCol+2) +j] + data[buffer*bufferSize + (i-1) * (nCol+2) + j] - 2.0f * data[buffer*bufferSize + i * (nCol+2)+ j])
                + wy * (data[buffer*bufferSize + i * (nCol+2) + j + 1] + data[buffer*bufferSize +  i * (nCol+2) + j - 1] - 2.0f * data[buffer*bufferSize + i * (nCol+2)+ j]);
            __syncthreads();

            /* flip buffer */
            buffer = 1-buffer;
        }

        /* copy the output back into global memory */
        data[i*(nCol+2)+j] = data[buffer * bufferSize + i * (nCol+2) + j ];
        __syncthreads();
    }
}
Profiles
Poisson PCIe Profile 
Reading in data
Setting buffer
==6484== NVPROF is profiling process 6484, command: .\pcie.exe .\test3.csv .\output3.csv 1000
==6484== Profiling application: .\pcie.exe .\test3.csv .\output3.csv 1000
==6484== Warning: 43 API trace records have same start and end timestamps.
This can happen because of short execution duration of CUDA APIs and low timer resolution on the underlying operating sy
stem.
==6484== Profiling result:
            Type  Time(%)      Time     Calls       Avg       Min       Max  Name
 GPU activities:   99.86%  29.120ms      1000  29.119us  24.158us  30.589us  DPS::update(float*, float const *, int, int
, float, float)
                    0.09%  26.269us         2  13.134us  12.990us  13.279us  [CUDA memcpy HtoD]
                    0.04%  13.119us         1  13.119us  13.119us  13.119us  [CUDA memcpy DtoH]
      API calls:   71.59%  183.43ms         2  91.713ms  10.265us  183.42ms  cudaMalloc
                   15.25%  39.069ms         1  39.069ms  39.069ms  39.069ms  cuDevicePrimaryCtxRelease
                    8.04%  20.601ms         3  6.8671ms  81.478us  20.424ms  cudaMemcpy
                    3.76%  9.6313ms      1000  9.6310us  6.7360us  335.53us  cudaLaunchKernel
                    1.26%  3.2196ms        96  33.537us       0ns  1.6234ms  cuDeviceGetAttribute
                    0.05%  127.03us         1  127.03us  127.03us  127.03us  cuModuleUnload
                    0.04%  107.78us         2  53.890us  22.454us  85.327us  cudaFree
                    0.00%  10.265us         1  10.265us  10.265us  10.265us  cuDeviceTotalMem
                    0.00%  9.6230us         1  9.6230us  9.6230us  9.6230us  cuDeviceGetPCIBusId
                    0.00%  1.2820us         2     641ns     320ns     962ns  cuDeviceGet
                    0.00%     962ns         3     320ns       0ns     641ns  cuDeviceGetCount
                    0.00%     962ns         1     962ns     962ns     962ns  cuDeviceGetName
                    0.00%     321ns         1     321ns     321ns     321ns  cuDeviceGetUuid
                    0.00%     321ns         1     321ns     321ns     321ns  cuDeviceGetLuid
Poisson AltProfile 
Allocate initial memory
Reading in data
Setting buffer
==2720== NVPROF is profiling process 2720, command: .\alt.exe .\test3.csv .\output3.csv 1000
==2720== Profiling application: .\alt.exe .\test3.csv .\output3.csv 1000
==2720== Warning: 50 API trace records have same start and end timestamps.
This can happen because of short execution duration of CUDA APIs and low timer resolution on the underlying operating sy
stem.
==2720== Profiling result:
            Type  Time(%)      Time     Calls       Avg       Min       Max  Name
 GPU activities:   99.88%  25.679ms         1  25.679ms  25.679ms  25.679ms  DPS::update(float*, int, int, float, float,
 unsigned int, unsigned int)
                    0.06%  16.670us         1  16.670us  16.670us  16.670us  [CUDA memcpy HtoD]
                    0.05%  12.575us         1  12.575us  12.575us  12.575us  [CUDA memcpy DtoH]
                    0.00%     576ns         1     576ns     576ns     576ns  [CUDA memset]
      API calls:   70.46%  158.87ms         1  158.87ms  158.87ms  158.87ms  cudaMalloc
                   16.71%  37.678ms         1  37.678ms  37.678ms  37.678ms  cudaDeviceReset
                   11.48%  25.877ms         2  12.938ms  60.947us  25.816ms  cudaMemcpy
                    1.25%  2.8161ms        96  29.334us       0ns  1.3867ms  cuDeviceGetAttribute
                    0.06%  133.12us         1  133.12us  133.12us  133.12us  cudaFree
                    0.02%  47.475us         1  47.475us  47.475us  47.475us  cudaMemset
                    0.01%  18.605us         1  18.605us  18.605us  18.605us  cudaLaunchKernel
                    0.01%  11.548us         1  11.548us  11.548us  11.548us  cuDeviceTotalMem
                    0.00%  9.9440us         1  9.9440us  9.9440us  9.9440us  cuDeviceGetPCIBusId
                    0.00%  1.2830us         1  1.2830us  1.2830us  1.2830us  cuDeviceGetName
                    0.00%     963ns         3     321ns       0ns     642ns  cuDeviceGetCount
                    0.00%     642ns         1     642ns     642ns     642ns  cuDeviceGetLuid
                    0.00%     641ns         2     320ns       0ns     641ns  cuDeviceGet
                    0.00%       0ns         1       0ns       0ns       0ns  cuDeviceGetUuid
GPU Offload Vs CPU

Gc-spa.png

Assignment 3

Source Codes

PCIe Optimization 
/*
 * Poisson Method using two arrays.  
 * Non-Ghost Cells Method
 * Multiple PCIe Calls made, once per iteration
 * by Tony Sim
 */
#include <cstring>
#include <cstdlib>
#include <iomanip>
#include <iostream>
#include <string>
#include <cuda_runtime.h>
#include "poisson.cuh"

namespace DPS{

    Poisson::Poisson(std::ifstream& ifs) {
        std::string line;
        nColumns = 0;
        bufferSide = 0;
        nRowsTotal = 0;
        /* find number of columns */
        std::getline(ifs,line);
        for (size_t i = 0 ; i < line.size() ; i++){
            if(line[i]==' ') nColumns++;
        }
        nColumns++;

        /* find number of rows */
        nRowsTotal++; /* already fetched one */
        while(std::getline(ifs,line))
            nRowsTotal++;
        ifs.clear();

        try{
            for (size_t i = 0 ; i < 2 ; i++)
                h_data[i] = new float[ (nColumns+2) * (nRowsTotal+2)]; /* add edge buffers */
        }
        catch (...){
            throw std::runtime_error("Failed to Allocate Memory");
        }

        /* readin data */
        std::cout <<"Reading in data"<<std::endl;
        ifs.seekg(0,ifs.beg);
        /* allocate memory to all but the edge buffer, index 0 and max for each row and column */
        for (size_t i = 0 ; i < nRowsTotal+2 ; i++){
            for (size_t j = 0 ; j < nColumns+2 ; j++){
                float val = 0;
                if(!(i == 0 || i == nRowsTotal + 1 || j == 0 || j == nColumns + 1))
                    ifs >> val;
                h_data[0][i*(nColumns+2)+j] = val;
            }
        }

        std::cout <<"Setting buffer"<<std::endl;
        std::memset(h_data[1],0,(nRowsTotal+2)*(nColumns+2)*sizeof(float));
        bool state = devMemSet();
        /* DEBUG */ std::cout << state << std::endl;

    }

    Poisson::Poisson(const size_t r, const size_t c, float* d) {
        bufferSide = 0;
        nRowsTotal = r;
        nColumns = c;
        try{
            h_data[0] = new float[(r+2)*(c+2)];
            h_data[1] = new float[(r+2)*(c+2)];
        }
        catch (...){
            throw std::runtime_error("Failed to Allocate Memory");
        }
        std::memcpy(h_data[0],d,(r+2)*(c+2)*sizeof(float));
        std::memset(h_data[1],0,(r+2)*(c+2)*sizeof(float));
        devMemSet();
    }

    Poisson::~Poisson(){
        for( size_t i = 0 ; i < 2 ; i++){
            delete [] h_data[i];
            cudaFree(d_data[i]);
        }
    }

    bool Poisson::devMemSet(){
        for(size_t i = 0 ; i < 2 ; i++){
            cudaMalloc(&d_data[i],(nColumns+2)*(nRowsTotal+2)*sizeof(float));
            if(d_data[i] != nullptr){
                cudaError_t state = cudaMemcpy((void*)d_data[i],(const void*)h_data[i],(nColumns+2)*(nRowsTotal+2)*sizeof(float),cudaMemcpyHostToDevice);
                if(state != cudaSuccess)
                        std::cerr << "ERROR on devMemSet for : " << i <<" with : " << cudaGetErrorString(state)<< std::endl;
            }
        }
        return d_data[0]&&d_data[1];
    }


    float* Poisson::operator()(const size_t nIterations, const float wx, const float wy){

        /* calculate the grid, block, where block has 1024 threads total */
        unsigned int blockx = 32;
        unsigned int blocky = 32;
        unsigned int gridx = ((nRowsTotal+2)+blockx-1)/blockx;
        unsigned int gridy = ((nRowsTotal+2)+blocky-1)/blocky;

        /* create dim3 */
        dim3 dBlock= {blockx,blocky};
        dim3 dGrid = {gridx,gridy};

        /* run iterations */
        for (size_t i = 0; i < nIterations; i++) {
            update<<<dGrid,dBlock>>>(d_data[1-bufferSide],d_data[bufferSide],nColumns, nRowsTotal, wx, wy);
            bufferSwitch();
        }

        /* DEBUG */ h_data[bufferSide][1*(nColumns+2) + 1] = 100.0f;
        /* output results from device to host */
        cudaError_t state = cudaMemcpy(h_data[bufferSide],d_data[bufferSide],(nColumns+2)*(nRowsTotal+2)*sizeof(float),cudaMemcpyDeviceToHost);
        if(state != cudaSuccess)
            std::cout << "ERROR on () when copying data back to host" <<" with : " << cudaGetErrorString(state)<< std::endl;

        return h_data[bufferSide];
    }

    void Poisson::show(std::ostream& ofs) const{
        ofs << std::fixed << std::setprecision(1);
        for (size_t j = 1; j <= nColumns ; j++) {
            for (size_t i = 1 ; i <= nRowsTotal ; i++)
                ofs << std::setw(8) << h_data[bufferSide][i * (nColumns+2) + j]<<",";
            ofs << std::endl;
        }
    }
    __global__ void update (float* newD, const float* currD, int nCol, int nRow, const float wx, const float wy){
        size_t j = blockDim.x * blockIdx.x + threadIdx.x + 1; /* for x axis */
        size_t i = blockDim.y * blockIdx.y + threadIdx.y + 1; /* for y axis */
        float curr = currD[i * (nCol+2)+ j];
        float dir1 = currD[(i+1) * (nCol+2) +j];
        float dir2 = currD[(i-1) * (nCol+2) +j];
        float dir3 = currD[i * (nCol+2) +j+1];
        float dir4 = currD[i * (nCol+2) +j-1];
        newD[i*(nCol+2)+j] = curr + wx * (dir1+dir2-2.0f*curr) + wy * (dir3+dir4-2.0f*curr);
        __syncthreads();
    }
}


For-loop Optimization 
/*
 * Poisson Method using two arrays.  
 * Non-Ghost Cells Method
 * Multiple PCIe Calls made, once per iteration
 * by Tony Sim
 */
#include <cstring>
#include <cstdlib>
#include <iomanip>
#include <iostream>
#include <string>
#include <cuda_runtime.h>
#include "poisson-alt-ghost2.cuh"

namespace DPS{

    Poisson::Poisson(std::ifstream& ifs) {
        blockx = 32;
        blocky = 32;

        std::string line;
        nColumns = 0;
        bufferSide = 0;
        nRowsTotal = 0;
        /* find number of columns */
        std::getline(ifs,line);
        for (size_t i = 0 ; i < line.size() ; i++){
            if(line[i]==' ') nColumns++;
        }
        nColumns++;

        /* find number of rows */
        nRowsTotal++; /* already fetched one */
        while(std::getline(ifs,line))
            nRowsTotal++;
        ifs.clear();

        int sizeX = ((nColumns + 2 + blockx + 2 - 1)/(blockx+2))*(blockx+2);
        int sizeY = ((nRowsTotal + 2 + blocky + 2 - 1)/(blocky+2))*(blocky+2);
        bufferSize = sizeX * sizeY;
        std::cout << "Allocate initial memory" << std::endl;
        try{
            h_data = new float[ bufferSize ]; /* add edge buffers */
        }
        catch (...){
            throw std::runtime_error("Failed to Allocate Memory");
        }

        /* readin data */
        std::cout <<"Reading in data"<<std::endl;
        ifs.seekg(0,ifs.beg);
        /* allocate memory to all but the edge buffer, index 0 and max for each row and column */
        std::memset(h_data,0,bufferSize);
        for (size_t i = 0 ; i < nRowsTotal+2 ; i++){
            for (size_t j = 0 ; j < nColumns+2 ; j++){
                float val = 0;
                if(!(i == 0 || i == nRowsTotal + 1 || j == 0 || j == nColumns + 1))
                    ifs >> val;
                h_data[i*(nColumns+2)+j] = val;
            }
        }

        std::cout <<"Setting buffer"<<std::endl;
        bool state = devMemSet();

    }

    Poisson::Poisson(const size_t r, const size_t c, float* d) {
        bufferSide = 0;
        nRowsTotal = r;
        nColumns = c;
        try{
            h_data = new float[(r+2)*(c+2)];
        }
        catch (...){
            throw std::runtime_error("Failed to Allocate Memory");
        }
        std::memcpy(h_data,d,(r+2)*(c+2)*sizeof(float));
        devMemSet();
    }

    Poisson::~Poisson(){
        delete [] h_data;
        cudaFree(d_data);
        cudaDeviceReset();
    }

    bool Poisson::devMemSet(){

        /* create double buffer */
        cudaMalloc(&d_data, bufferSize * sizeof(float));

        if(d_data != nullptr){
            /* copy the initial information to the first buffer */
            cudaError_t state = cudaMemcpy((void*)d_data,(const void*)h_data, bufferSize * sizeof(float),cudaMemcpyHostToDevice);
            if(state != cudaSuccess)
                std::cerr << "ERROR on devMemSet at cudaMemcpy : " << cudaGetErrorString(state)<< std::endl;
        }
        return d_data;
    }

    float* Poisson::operator()(const size_t nIterations, const float wx, const float wy){

        /* calculate the grid, block, where block has 1024 threads total */
        unsigned int gridx = ((nRowsTotal+2)+blockx-1)/blockx;
        unsigned int gridy = ((nRowsTotal+2)+blocky-1)/blocky;

        /* create dim3 */
        dim3 dBlock= {blockx,blocky};
        dim3 dGrid = {gridx,gridy};

        /* generate shared memory map that will control ghost cell sharing */
        char* hmap = new char[(blockx+2)*(blocky+2)*3];
        int stride = 3;
        for(int i = 0 ; i < (blockx+2);i++){
            for(int j = 0 ; j < (blocky+2);j++){
                char val = 0;
                char x = 0;
                char y = 0;
                if(i==1){
                    val = 1;
                    x=-1;
                    y=0;
                }
                if(j==1){
                    val = 1;
                    x=0;
                    y=-1;
                }
                if(i==blockx) {
                    val = 1;
                    x=1;
                    y=0;
                }
                if(j==blocky){
                    val = 1;
                    x=0;
                    y=1;
                }
                if(i==2 || j==2 || i==31 || j==31)
                    val = 2;
                hmap[(i * (blockx+2) + j)*stride] = val;
                hmap[(i * (blockx+2) + j)*stride+1] = x;
                hmap[(i * (blockx+2) + j)*stride+2] = y;
            }
        }
        /* transfer to device */
        char* dmap = nullptr;
        cudaMalloc(&dmap,(blockx+2)*(blocky+2)*sizeof(char)*3);
        cudaMemcpy(dmap,hmap,(blockx+2)*(blocky+2)*sizeof(char)*3,cudaMemcpyHostToDevice);

        /* run iterations */
        update<<<dGrid,dBlock>>>(d_data,dmap,nColumns, nRowsTotal, wx, wy,nIterations);

        /*DEBUG */ h_data[2*(nColumns+2)+2] = 100.0f;
        /* output results from device to host */
        cudaError_t state = cudaMemcpy(h_data,d_data,(nColumns+2)*(nRowsTotal+2)*sizeof(float),cudaMemcpyDeviceToHost);
        if(state != cudaSuccess)
            std::cout << "ERROR on () when copying data back to host with : " << cudaGetErrorString(state)<< std::endl;

        return h_data;
    }

    void Poisson::show(std::ostream& ofs) const{
        ofs << std::fixed << std::setprecision(1);
        for (size_t j = 1; j <= nColumns ; j++) {
            for (size_t i = 1 ; i <= nRowsTotal ; i++)
                ofs << std::setw(8) << h_data[i * (nColumns+2) + j]<<",";
            ofs << std::endl;
        }
    }
    __global__ void update (float* data, char* dmap, int nCol, int nRow, const float wx, const float wy, unsigned int nIterations){
        size_t j = blockDim.x * blockIdx.x + threadIdx.x + 1; /* for x axis */
        size_t i = blockDim.y * blockIdx.y + threadIdx.y + 1; /* for y axis */
        size_t y = threadIdx.x+1;
        size_t x = threadIdx.y+1;

        const unsigned int bufferSize = (32+2)*(32+2);
        __shared__ float localBuffer[ 2 * bufferSize ]; /* double local buffer with ghost cells */
       // __shared__ char lmap[bufferSize];

        unsigned int buffer = 0;

        float prefetch = 0.0f;
        /* copy information into first of the local buffer */
        localBuffer[x*(32+2)+y] = data[i*(nCol+2)+j];
        __syncthreads();

        const char lmap = dmap[(x*(32+2)+y)*3];
        const char addx = dmap[(x*(32+2)+y)*3+1];
        const char addy = dmap[(x*(32+2)+y)*3+2];

        /* prefetch */
        if(lmap)
            prefetch = data[(i+addx)*(nCol+2)+j+addy] ;

        /* run iterations */
        for (unsigned int n = 0 ; n < nIterations; n++){
            if(lmap)
                localBuffer[buffer * bufferSize + (x+addx)*(32+2) + y+addy] = prefetch;
            /* Calculate and store into the other buffer */
            float curr = localBuffer[buffer*bufferSize + x * (32+2)+ y];
            float dir1 = localBuffer[buffer*bufferSize + (x+1) * (32+2) +y];
            float dir2 = localBuffer[buffer*bufferSize + (x-1) * (32+2) +y];
            float dir3 = localBuffer[buffer*bufferSize + x * (32+2) + y + 1];
            float dir4 = localBuffer[buffer*bufferSize + x * (32+2) + y - 1];
            localBuffer[(1-buffer)*bufferSize + x*(32+2)+y] = curr + wx*(dir1+dir2-2.0f*curr) + wy*(dir3+dir4-2.0f*curr);
            /* flip buffer */
            buffer = 1-buffer;
            /* for threads in charge of edges, share and obtain ghost cells */
            if(lmap){ 
                /* Copy over edges to global memory to be shared with neighboring blocks */
                data[i*(nCol+2)+j] = localBuffer[buffer * bufferSize + x * (32+2) + y ];
            }
            __syncthreads();
            if(lmap){ 
                /* Copy back buffers from global memory */
                prefetch = data[(i+addx)*(nCol+2)+j+addy] ;
            }
        }

        /* copy the output back into global memory */
        data[i*(nCol+2)+j] = localBuffer[buffer * bufferSize + x * (32+2) + y ];
        __syncthreads();
    }
}
For-loop Optimization - poissant-alt-ghost2.cuh 
/*
 * Poisson Method using two arrays.  
 * Non-Ghost Cells Method
 * Multiple PCIe Calls made, once per iteration
 * by Tony Sim
 */
#ifndef POISSON_H
#define POISSON_H
#include <fstream>
#include <cuda_runtime.h>

namespace DPS{
    class Poisson {
        unsigned int blockx;
        unsigned int blocky;
        unsigned int nRowsTotal;
        unsigned int nColumns;
        unsigned int bufferSize;
        float* h_data;
        float* d_data;
        int bufferSide;

        void bufferSwitch(){ bufferSide = 1 - bufferSide; };
        bool devMemSet();

        public:
        Poisson() = delete;
        Poisson(std::ifstream& ifs);
        Poisson(const size_t r, const size_t c, float* d);
        ~Poisson();
        float* operator()(const size_t iteration, const float wx, const float wy);
        float* operator()(const size_t iteration){
            return operator()(iteration,0.1,0.1);
        }
        void show(std::ostream& ofs) const;
    };
    __global__ void update (float* data, char* dmap, int nCol, int nRow, const float wx, const float wy, unsigned int nIterations);
}
#endif

Optimization Details

PCIe Version
  • Coalesced Memory - Large performance boost.
  • Prefetch - this had minor to no effect on the performance.
For-loop Version
  • Shared Memory - Small boost. Used technique called Ghost Cells where updated information is shared over global memory as needed to perform the next iteration.
  • Prefetch - Small boost. Information are fetched first into register in the previous iteration to be copied in the current iteration prior to calculation.
  • Coalesed Memory - Large boost.
  • Logic change - To minimize the number of condition calls, a predefined map of instruction was created on the host based on the block dimension information. Using this information, the if statement had been cut down to almost 1/4, showing noticeable performance increase.

Result

POST Presentation Results Contrary to the presentation's conclusion, the ghost-cell method proved to be more effective with some changes to the logic than simpler global-memory-based counterpart. It does require some preparation in the host machine. The gain is small.

GPU highlights. para-ghost-pre-co2, which implements Ghost Cell + Prefetch + Coaleased memory + logic change, is slightly faster than simpler Prefetch+Coaleased memory that uses Global Memory. Both methods are superior than calling the conditional-less kernel 1000 times over PCIe.
UsinGPU highlights. Ghost Cell + Prefetch + Coaleased memory + logic change is slightly faster than simpler Prefetch+Coaleased memory that uses Global Memoryg GPU significantly improved Calculation Time over the CPU counterparts.
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