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→Kernel Version 2
{{GPU610/DPS915 Index | 20191}}
= Project Name Goes here Algo Holics =
== Team Members ==
# [mailto:ssdhillon20@myseneca.ca?subject=GPU610 Sukhbeer Dhillon], Simple Neural Network
====Sudoku Puzzle Solver by - Gurpreet Singh====
Is it a program that solves Sudoku puzzles(9X9) using Bruteforce algorithm. The user can either pass a Sudoku files as an input or enter the values manually. Moreover, the file or the manual entry must strictly have 9 rows and 9 columns in them. Last but not the least, all the cells must be separated by a space and the cells that needs to be solved must have 0 in them as their value.
{| class="wikitable mw-collapsible mw-collapsed"
! Flat profileCall Graph
|-
|
{| class="wikitable mw-collapsible mw-collapsed"
! Flat profileCall Graph
|-
|
----
==== Simple Artificial Neural Network by - SukhbeerSingh====
=====Introduction=====
Loss 0.184251
--------------------------------------------End of Epoch :(------------------------------------------------
=====Profiling=====
The total execution time of the program is around 3 minutes. The profiling results spot displayPrediction as the function with maximum execution time. However, thats because it displays a matrix using the naive O(n2) for-loop. train() is the next function with the maximum time. This is the hotspot for the program. If this function is made to be the kernel and the functions that it calls as device functions, the program would fasten by a good proportion.
----
==== Sorting Algorithms - Merge Sort - Edgar Giang====
=====Intro=====
=====How to run the program=====
The following command was tested in matrix:
=====Running the program=====
This would be the following output:
As you can see the input used was 200,000. The time indicated in the output is in milliseconds so it took 166100 milliseconds to complete. A much larger input would result in the program running for a very long time.
=====Profiling=====
{| class="wikitable mw-collapsible mw-collapsed"
=====Analysis=====
21.90 0.07 0.07 600021 0.00 0.00 int* std::__copy_move<false, true, std::random_access_iterator_tag>::__copy_m<int>(int const*, int const*, int*)
From the flat profile,the hotspots for this program would be the function merge function whih was called 200,006 times and thee int* std::__copy__move. The merge function would take up 52.3% of the time
----
=== Assignment 2 ===
Our initial idea was to use the neural network code for our assignment 2. But since the algorithm itself was not very accurate (2/10 correct predictions even after 10,000 training iterations), we decided to paralellize merge sort. Soon we realized that since its Big O classification was n log n, offloading computations to GPU would not be that effective. So, we settled with the cosine transform library, as described below.
====Cosine Tranformation====
The Cosine_Transform is a simple C++ library which demonstrates properties of the Discrete cosine Transform for real data. The Discrete Cosine Transform or DCT is used to create jpeg (compressed images).
The formula used here is:
| (√1/n) , if u=0; 0≤v≤n-1
C(u,v) =
| (√2/n) * cos[((2*v+1)π*u)/2n], if 1≤u≤n-1; 0≤v≤n-1
Where, u is the row index, v is the column index and n is the total number of elements in a row/column in the computational matrix.
This [https://www.youtube.com/watch?v=tW3Hc0Wrgl0 Link] can be used for better understanding of the above formula. Here is the [https://people.sc.fsu.edu/~jburkardt/cpp_src/cosine_transform/cosine_transform.html source code] used.
=====Profiling=====
The flat profile for the above serial code looks like:
{| class="wikitable mw-collapsible mw-collapsed"
! Flat Profile
|-
|
1
2
3
4 granularity: each sample hit covers 2 byte(s) for 0.68% of 1.47 seconds
5
6 index % time self children called name
7 <spontaneous>
8 [1] 100.0 0.00 1.47 main [1]
9 0.00 1.47 1/1 cosine_transform_test01(int) [3]
10 -----------------------------------------------
11 1.47 0.00 1/1 cosine_transform_test01(int) [3]
12 [2] 100.0 1.47 0.00 1 cosine_transform_data(int, double*) [2]
13 -----------------------------------------------
14 0.00 1.47 1/1 main [1]
15 [3] 100.0 0.00 1.47 1 cosine_transform_test01(int) [3]
16 1.47 0.00 1/1 cosine_transform_data(int, double*) [2]
17 0.00 0.00 1/1 r8vec_uniform_01_new(int, int&) [14]
18 0.00 0.00 1/1 reportTime(char const*, std::chrono::duration<long, std::ratio<1l, 1000000000l> >) [13]
19 0.00 0.00 1/1 std::common_type<std::chrono::duration<long, std::ratio<1l, 1000000000l> >, std::chrono::duration<long, std::ratio<1l, 1000000000l> > >::type std::chrono::operator-<std::chrono::_V2::s teady_clock, std::chrono::duration<long, std::ratio<1l, 1000000000l> >, std::chrono::duration<long, std::ratio<1l, 1000000000l> > >(std::chrono::time_point<std::chrono::_V2::steady_clock, std::chrono::duration<long, std::ratio<1l, 10 00000000l> > > const&, std::chrono::time_point<std::chrono::_V2::steady_clock, std::chrono::duration<long, std::ratio<1l, 1000000000l> > > const&) [21]
20 -----------------------------------------------
21 0.00 0.00 1/3 std::chrono::duration<long, std::ratio<1l, 1000l> > std::chrono::__duration_cast_impl<std::chrono::duration<long, std::ratio<1l, 1000l> >, std::ratio<1l, 1000000l>, long, true, false>: :__cast<long, std::ratio<1l, 1000000000l> >(std::chrono::duration<long, std::ratio<1l, 1000000000l> > const&) [18]
22 0.00 0.00 2/3 std::common_type<std::chrono::duration<long, std::ratio<1l, 1000000000l> >, std::chrono::duration<long, std::ratio<1l, 1000000000l> > >::type std::chrono::operator-<long, std::ratio<1l , 1000000000l>, long, std::ratio<1l, 1000000000l> >(std::chrono::duration<long, std::ratio<1l, 1000000000l> > const&, std::chrono::duration<long, std::ratio<1l, 1000000000l> > const&) [22]
23 [10] 0.0 0.00 0.00 3 std::chrono::duration<long, std::ratio<1l, 1000000000l> >::count() const [10]
24 -----------------------------------------------
25 0.00 0.00 2/2 std::common_type<std::chrono::duration<long, std::ratio<1l, 1000000000l> >, std::chrono::duration<long, std::ratio<1l, 1000000000l> > >::type std::chrono::operator-<std::chrono::_V2::s teady_clock, std::chrono::duration<long, std::ratio<1l, 1000000000l> >, std::chrono::duration<long, std::ratio<1l, 1000000000l> > >(std::chrono::time_point<std::chrono::_V2::steady_clock, std::chrono::duration<long, std::ratio<1l, 10 00000000l> > > const&, std::chrono::time_point<std::chrono::_V2::steady_clock, std::chrono::duration<long, std::ratio<1l, 1000000000l> > > const&) [21]
26 [11] 0.0 0.00 0.00 2 std::chrono::time_point<std::chrono::_V2::steady_clock, std::chrono::duration<long, std::ratio<1l, 1000000000l> > >::time_since_epoch() const [11]
27 -----------------------------------------------
28 0.00 0.00 1/1 __libc_csu_init [28]
29 [12] 0.0 0.00 0.00 1 _GLOBAL__sub_I__Z20r8vec_uniform_01_newiRi [12]
30 0.00 0.00 1/1 __static_initialization_and_destruction_0(int, int) [15]
31 -----------------------------------------------
32 0.00 0.00 1/1 cosine_transform_test01(int) [3]
33 [13] 0.0 0.00 0.00 1 reportTime(char const*, std::chrono::duration<long, std::ratio<1l, 1000000000l> >) [13]
34 0.00 0.00 1/1 std::enable_if<std::chrono::__is_duration<std::chrono::duration<long, std::ratio<1l, 1000l> > >::value, std::chrono::duration<long, std::ratio<1l, 1000l> > >::type std::chrono::duratio n_cast<std::chrono::duration<long, std::ratio<1l, 1000l> >, long, std::ratio<1l, 1000000000l> >(std::chrono::duration<long, std::ratio<1l, 1000000000l> > const&) [17]
35 0.00 0.00 1/1 std::chrono::duration<long, std::ratio<1l, 1000l> >::count() const [16]
36 -----------------------------------------------
37 0.00 0.00 1/1 cosine_transform_test01(int) [3]
38 [14] 0.0 0.00 0.00 1 r8vec_uniform_01_new(int, int&) [14]
39 -----------------------------------------------
40 0.00 0.00 1/1 _GLOBAL__sub_I__Z20r8vec_uniform_01_newiRi [12]
41 [15] 0.0 0.00 0.00 1 __static_initialization_and_destruction_0(int, int) [15]
42 -----------------------------------------------
43 0.00 0.00 1/1 reportTime(char const*, std::chrono::duration<long, std::ratio<1l, 1000000000l> >) [13]
44 [16] 0.0 0.00 0.00 1 std::chrono::duration<long, std::ratio<1l, 1000l> >::count() const [16]
45 -----------------------------------------------
46 0.00 0.00 1/1 reportTime(char const*, std::chrono::duration<long, std::ratio<1l, 1000000000l> >) [13]
47 [17] 0.0 0.00 0.00 1 std::enable_if<std::chrono::__is_duration<std::chrono::duration<long, std::ratio<1l, 1000l> > >::value, std::chrono::duration<long, std::ratio<1l, 1000l> > >::type std::chrono::duration_ca st<std::chrono::duration<long, std::ratio<1l, 1000l> >, long, std::ratio<1l, 1000000000l> >(std::chrono::duration<long, std::ratio<1l, 1000000000l> > const&) [17]
48 0.00 0.00 1/1 std::chrono::duration<long, std::ratio<1l, 1000l> > std::chrono::__duration_cast_impl<std::chrono::duration<long, std::ratio<1l, 1000l> >, std::ratio<1l, 1000000l>, long, true, false>: :__cast<long, std::ratio<1l, 1000000000l> >(std::chrono::duration<long, std::ratio<1l, 1000000000l> > const&) [18]
49 -----------------------------------------------
50 0.00 0.00 1/1 std::enable_if<std::chrono::__is_duration<std::chrono::duration<long, std::ratio<1l, 1000l> > >::value, std::chrono::duration<long, std::ratio<1l, 1000l> > >::type std::chrono::duratio n_cast<std::chrono::duration<long, std::ratio<1l, 1000l> >, long, std::ratio<1l, 1000000000l> >(std::chrono::duration<long, std::ratio<1l, 1000000000l> > const&) [17]
51 [18] 0.0 0.00 0.00 1 std::chrono::duration<long, std::ratio<1l, 1000l> > std::chrono::__duration_cast_impl<std::chrono::duration<long, std::ratio<1l, 1000l> >, std::ratio<1l, 1000000l>, long, true, false>::__c ast<long, std::ratio<1l, 1000000000l> >(std::chrono::duration<long, std::ratio<1l, 1000000000l> > const&) [18]
52 0.00 0.00 1/3 std::chrono::duration<long, std::ratio<1l, 1000000000l> >::count() const [10]
|}
As is evident, the algorithm is O(n2) currently. Using thread indices on the GPU to replace the for loops could potentially improve performance.
To increase the efficiency of the program we transformed the '''cosine_transform_data''' function into a kernel named '''cosTransformKernel''' which offloads the compute intense calculation of the program to the GPU.
=====Kernel Version 1=====
{| class="wikitable mw-collapsible mw-collapsed"
! Modified Code
|-
|
# include <iostream>
# include <iomanip>
# include <ctime>
# include <chrono>
# include <cstdlib>
# include <cmath>
#include <cuda_runtime.h>
using namespace std;
using namespace std::chrono;
const double pi = 3.141592653589793;
const int ntpb = 1024;
void cosine_transform_test01 ( int size );
double *r8vec_uniform_01_new ( int n, int &seed ){
int i;
const int i4_huge = 2147483647;
int k;
double *r;
if ( seed == 0 ){
cerr << "\n";
cerr << "R8VEC_UNIFORM_01_NEW - Fatal error!\n";
cerr << " Input value of SEED = 0.\n";
exit ( 1 );
}
r = new double[n];
for ( i = 0; i < n; i++ ){
k = seed / 127773;
seed = 16807 * ( seed - k * 127773 ) - k * 2836;
if ( seed < 0 ){
seed = seed + i4_huge;
}
r[i] = ( double ) ( seed ) * 4.656612875E-10;
}
return r;
}
double *cosine_transform_data ( int n, double d[] ){
double angle;
double *c;
int i;
int j;
c = new double[n];
for ( i = 0; i < n; i++ ){
c[i] = 0.0;
for ( j = 0; j < n; j++ ){
angle = pi * ( double ) ( i * ( 2 * j + 1 ) ) / ( double ) ( 2 * n );
c[i] = c[i] + cos ( angle ) * d[j];
}
c[i] = c[i] * sqrt ( 2.0 / ( double ) ( n ) );
}
return c;
}
void reportTime(const char* msg, steady_clock::duration span) {
auto ms = duration_cast<milliseconds>(span);
std::cout << msg << " - took - " <<
ms.count() << " millisecs" << std::endl;
}
__global__ void cosTransformKernel(double *a, double *b, int n){
double angle;
const double pi = 3.141592653589793;
int i = blockIdx.x * blockDim.x + threadIdx.x;
for(int j=0; j<n; j++){
angle = pi * (double) (i*(2*j+1)) / (double)(2*n);
b[i] += cos ( angle ) * a[j];
}
b[i] *= sqrt( 2.0 / (double) n );
}
int main (int argc, char* argv[] ){
if (argc != 2) {
std::cerr << argv[0] << ": invalid number of arguments\n";
std::cerr << "Usage: " << argv[0] << " size_of_vector\n";
return 1;
}
int n = std::atoi(argv[1]);
cosine_transform_test01 (n);
return 0;
}
void cosine_transform_test01 ( int size){
int n = size;
int seed;
double *r;
double *hs;
double *s = new double[n];
double *d_a;
double *d_b;
//allocate memory on the device for the randomly generated array and for the array in which transform values will be stored
cudaMalloc((void**)&d_a,sizeof(double) * n);
cudaMalloc((void**)&d_b,sizeof(double) * n);
seed = 123456789;
r = r8vec_uniform_01_new ( n, seed );
//copy randomly generated values from host to device
cudaMemcpy(d_a,r,sizeof(double)*n,cudaMemcpyHostToDevice);
int nblks = (n + ntpb - 1) / ntpb;
steady_clock::time_point ts, te;
ts = steady_clock::now();
cosTransformKernel<<<nblks,ntpb>>>(d_a,d_b,size);
cudaDeviceSynchronize();
te = steady_clock::now();
reportTime("Cosine Transform on device",te-ts);
cudaMemcpy(s,d_b,sizeof(double)*n,cudaMemcpyDeviceToHost);
ts = steady_clock::now();
hs = cosine_transform_data ( n, r );
te = steady_clock::now();
reportTime("Cosine Transform on host",te-ts);
cudaFree(d_a);
cudaFree(d_b);
delete [] r;
delete [] s;
delete [] hs;
}
[[File:kernel1.png]]
Even though the kernel includes a for-loop the execution time has decreased drastically. Thats because each thread is now responsible for one calculating one element of the final Cos transformed matrix(unit vector).
=== Assignment 3 ===
For optimizing the code better, we thought of removing the iterative loop from the kernel by using threadIdx.y to control calculation of each element's cosine for that position in the supposed matrix. The problem in this was that each thread was in a racing condition to write to the same memory location, to sum up the cosine transformations for all elements of that row. We solved this by using the atomic function. Its prototype is as follows.
double atomicAdd(double* address, double value)
=====Kernel Version 2=====
{| class="wikitable mw-collapsible mw-collapsed"
! Kernel 2
|-
|
# include <cmath>
# include <cstdlib>
# include <iostream>
# include <iomanip>
# include <ctime>
# include <chrono>
# include <cstdlib>
# include <cmath>
#include <limits>
#include <cuda_runtime.h>
#include <cuda.h>
using namespace std;
using namespace std::chrono;
const double pi = 3.141592653589793;
const unsigned ntpb = 32;
void cosine_transform_test01 ( int size );
double *r8vec_uniform_01_new ( int n, int &seed ){
int i;
const int i4_huge = 2147483647;
int k;
double *r;
if ( seed == 0 ){
cerr << "\n";
cerr << "R8VEC_UNIFORM_01_NEW - Fatal error!\n";
cerr << " Input value of SEED = 0.\n";
exit ( 1 );
}
r = new double[n];
for ( i = 0; i < n; i++ ){
k = seed / 127773;
seed = 16807 * ( seed - k * 127773 ) - k * 2836;
if ( seed < 0 ){
seed = seed + i4_huge;
}
r[i] = ( double ) ( seed ) * 4.656612875E-10;
}
return r;
}
double *cosine_transform_data ( int n, double d[] ){
double angle;
double *c;
int i;
int j;
c = new double[n];
for ( i = 0; i < n; i++ ){
c[i] = 0.0;
for ( j = 0; j < n; j++ ){
angle = pi * ( double ) ( i * ( 2 * j + 1 ) ) / ( double ) ( 2 * n );
c[i] = c[i] + cos ( angle ) * d[j];
}
c[i] = c[i] * sqrt ( 2.0 / ( double ) ( n ) );
}
return c;
}
void reportTime(const char* msg, steady_clock::duration span) {
auto ms = duration_cast<milliseconds>(span);
std::cout << msg << " - took - " <<
ms.count() << " millisecs" << std::endl;
}
__global__ void cosTransformKernel(double *a, double *b, const int n){
double angle;
const double pi = 3.141592653589793;
int j = blockIdx.x * blockDim.x + threadIdx.x;
int i = blockIdx.y * blockDim.y + threadIdx.y;
if(i<n && j<n){
angle = pi * ( double ) ( i * ( 2 * j + 1 ) ) / ( double ) ( 2 * n );
double value = cos ( angle ) * a[j];
b[i] = atomicAdd(&b[i], value);
}
//square root of the whole cos transformed row term
if(j==n-1 && i<n){
b[i] *= sqrt ( 2.0 / ( double ) ( n ) );
}
}
int main (int argc, char* argv[] ){
if (argc != 2) {
std::cerr << argv[0] << ": invalid number of arguments\n";
std::cerr << "Usage: " << argv[0] << " size_of_vector\n";
return 1;
}
int n = std::atoi(argv[1]);
cosine_transform_test01 (n);
return 0;
}
void cosine_transform_test01 ( int size){
int n = size;
int seed;
double *r;
double *hs; //host side pointer to store the array returned from host side cosine_transform_data, for comparison purposes
double *s = new double[n];
//double *t;
double *d_a;
double *d_b;
//allocate memory on the device for the randomly generated array and for the array in which transform values will be stored
cudaMalloc((void**)&d_a,sizeof(double) * n);
cudaMalloc((void**)&d_b,sizeof(double) * n);
seed = 123456789;
r = r8vec_uniform_01_new ( n, seed );
//copy randomly generated values from host to device
for(int i=0; i<n; i++)
s[i]=0.0;
cudaMemcpy(d_a,r,sizeof(double)*n,cudaMemcpyHostToDevice);
cudaMemcpy(d_b,s,sizeof(double)*n,cudaMemcpyHostToDevice);
int nblks = (n + ntpb - 1) / ntpb;
dim3 grid(nblks,nblks,1);
dim3 block(ntpb,ntpb,1);
steady_clock::time_point ts, te;
ts = steady_clock::now();
cosTransformKernel<<<grid,block>>>(d_a,d_b,size);
cudaDeviceSynchronize();
te = steady_clock::now();
reportTime("Cosine Transform on device",te-ts);
cudaMemcpy(s,d_b,sizeof(double)*n,cudaMemcpyDeviceToHost);
ts = steady_clock::now();
hs = cosine_transform_data ( n, r );
te = steady_clock::now();
reportTime("Cosine Transform on host",te-ts);
cudaFree(d_a);
cudaFree(d_b);
delete [] r;
delete [] s;
delete [] hs;
//delete [] t;
return;
}
|}
Here is a comparison between the naive and optimized kernel
[[File:kernel2.jpg]]
Evidently, there is some performance boost for the new version. However, each call to atomicAdd by a thread locks the global memory until the old value is read and added to the passed value. This deters faster execution as might be expected.
