DPS921/OpenACC vs OpenMP Comparison
Project Overview
The idea of this project is to introduce OpenACC as a parallel processing library, compare how parallelization is done in different libraries, and identify benefits of using each of the libraries. According to description of both libraries, OpenACC does parallelization more automatically whereas OpenMP allows developers to manually set regions to parallelize and assign to threads. The deliverable of this project would be a introduction to OpenACC along with performance comparison between OpenMP and OpenACC, and a discussion on usage of each one.
Group Members
1. Ruiqi Yu
2. Hanlin Li
3. Le Minh Pham
Progress
- Nov 9, 2020: Added project description - Nov 13, 2020: Determine content sections to be discussed - Nov 18, 2020: Successful installation of required compiler and compilation of OpenACC code - Nov 19, 2020: Adding MPI into discussion
OpenACC
GPU parallelization vs CPU
The GPU (Graphics Processing Unit) often consists of thousands of cores, whereas a typical modern multiple core CPU has somewhere between 2 - 128 cores. Even though the GPU cores are smaller than the CPU cores and are much slower at processing serial codes, the high number of cores makes GPUs perform better with parallelized codes in many cases.
An example
If we want to render a 4k image which has 8.2 million pixels (3,840 x 2,160), we have to do the same rendering algorithm on 8.2 million pixels. The GPU can accomplish this task much more efficient since it has a much higher number of processors that can execute the algorithm at the same time.
What is OpenACC
OpenACC (Open Accelerators) is a programming standard for parallel computing on accelerators such as GPUs, which mainly targets Nvidia GPUs. OpenACC is designed to simplify GPU programming, unlike CUDA and OpenCL where you need to write your programs in a different way to achieve GPU acceleration, OpenACC takes a similar approach as OpenMP, which is inserting directives into the code to offload computation onto GPUs and parallelize the code at CUDA core level. It is possible for programmers to create efficient parallel OpenACC code with only minor changes to a serial CPU code.
Example
#pragma acc kernels
{
for (int i = 0; i < N; i++) {
y[i] = a * x[i] + y[i];
}
}GPU offloading
[image]
Installation
Originally, OpenACC compilation is supported by the PGI compiler which requires an expensive subscription, there has been new options in recent years.
Nvidia HPC SDK[1]
Evolved from PGI Compiler community edition. Installation guide are provided in the official website. Currently only supports Linux systems, but Windows support will come soon.
wget https://developer.download.nvidia.com/hpc-sdk/20.9/nvhpc-20-9_20.9_amd64.deb \
https://developer.download.nvidia.com/hpc-sdk/20.9/nvhpc-2020_20.9_amd64.deb
sudo apt-get install ./nvhpc-20-9_20.9_amd64.deb ./nvhpc-2020_20.9_amd64.debAfter installation, the compilers can be found under /opt/nvidia/hpc_sdk/Linux_x86_64/20.9/compilers/bin, and OpenACC code can be compiled with nvc -acc -gpu=manage demo.c, where -acc indicates that the code will include OpenACC directives, and -gpu=manage indicates how should memory be managed. nvc is used here because source code is C code, there is nvc++ for compiling C++ code.
The compiler can also tell how the parallel regions are generalized if you pass in a -Minfo option like
$ nvc -acc -gpu=managed -Minfo demo.c
main:
79, Generating implicit copyin(A[:256][:256]) [if not already present]
Generating implicit copy(_error) [if not already present]
Generating implicit copyout(Anew[1:254][1:254]) [if not already present]
83, Loop is parallelizable
85, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
83, #pragma acc loop gang, vector(4) /* blockIdx.y threadIdx.y */
Generating implicit reduction(max:_error)
85, #pragma acc loop gang, vector(32) /* blockIdx.x threadIdx.x */
91, Generating implicit copyout(A[1:254][1:254]) [if not already present]
Generating implicit copyin(Anew[1:254][1:254]) [if not already present]
95, Loop is parallelizable
97, Loop is parallelizable
Accelerator kernel generated
Generating Tesla code
95, #pragma acc loop gang, vector(4) /* blockIdx.y threadIdx.y */
97, #pragma acc loop gang, vector(32) /* blockIdx.x threadIdx.x */This tells which loops are parallelized with line numbers for reference.
For Windows users that would like to try this SDK, WSL2 is one option. WSL2 does not fully support this SDK at this moment, due to the fact that most virtualization technologies cannot let virtualized systems use the graphic card directly. Nvidia had released a preview driver[2] that allows the Linux subsystem to recognize graphic cards installed on the machine, it allows WSL2 users to compile programs with CUDA toolkits but not with the HPC SDK yet.
Nvidia CUDA WSL2
We are not going to go over how to deal with CUDA on WSL2. We included the installation guide for using CUDA on WSL2 here for anyone's interest [3]. Note that you need to have registered in the Windows Insider Program to get one of the preview Win10 versions.
GCC[4]
Latest GCC version, GCC 10 has support to OpenACC 2.6
OpenMP vs OpenACC
We are comparing with OpenMP for two reasons. First, OpenMP is also based on directives to parallelize code; second, OpenMP started support of offloading to accelerators starting OpenMP 4.0 using target constructs. OpenACC uses directives to tell the compiler where to parallelize loops, and how to manage data between host and accelerator memories. OpenMP takes a more generic approach, it allows programmers to explicitly spread the execution of loops, code regions and tasks across teams of threads.
OpenMP's directives tell the compiler to generate parallel code in that specific way, leaving little room to the discretion of the compiler and the optimizer. The compiler must do as instructed. It is up to the programmer to guarantee that generated code is correct, parallelization and scheduling are also responsibility of the programmer, not the compiler at runtime.
OpenACC's parallel directives tells the compiler that the loop is a parallel loop. It is up to the compiler to decide how to parallelize the loop. For example the compiler can generate code to run the iterations across threads, or run the iterations across SIMD lanes. The compiler gets to decide method of parallelization based on the underlying hardware architecture, or use a mixture of different methods.
So the real difference between the two is how much freedom is given to the compilers.
Code comparison
Explicit conversions
OpenACC OpenMP
#pragma acc kernels #pragma omp target
{ {
#pragma acc loop worker #pragma omp parallel for private(tmp)
for(int i = 0; i < N; i++){ for(int i = 0; i < N; i++){
tmp = …; tmp = …;
array[i] = tmp * …; array[i] = tmp * …;
} }
#pragma acc loop vector #pragma omp simd
for(int i = 0; i < N; i++) for(int i = 0; i < N; i++)
array2[i] = …; array2[i] = …;
} }ACC parallel
OpenACC OpenMP
#pragma acc parallel #pragma omp target
{ #pragma omp parallel
#pragma acc loop {
for(int i = 0; i < N; i++){ #pragma omp for private(tmp) nowait
tmp = …; for(int i = 0; i < N; i++){
array[i] = tmp * …; tmp = …;
} array[i] = tmp * …;
#pragma acc loop }
for(int i = 0; i < N; i++) #pragma omp for simd
array2[i] = …; for(int i = 0; i < N; i++)
} array2[i] = …;
}ACC Kernels
OpenACC OpenMP
#pragma acc kernels #pragma omp target
{ #pragma omp parallel
for(int i = 0; i < N; i++){ {
tmp = …; #pragma omp for private(tmp)
array[i] = tmp * …; for(int i = 0; i < N; i++){
} tmp = …;
for(int i = 0; i < N; i++) array[i] = tmp * …;
array2[i] = … }
} #pragma omp for simd
for(int i = 0; i < N; i++)
array2[i] = …
}Copy vs. PCopy
OpenACC OpenMP
int x[10],y[10]; int x[10],y[10];
#pragma acc data copy(x) pcopy(y) #pragma omp target data map(x,y)
{ {
... ...
#pragma acc kernels copy(x) pcopy(y) #pragma omp target update to(x)
{ #pragma omp target map(y)
// Accelerator Code {
... // Accelerator Code
} ...
... }
} }Performance Comparison
Jacobi Iteration
Collaboration
OpenACC with OpenMP
OpenMP and OpenACC can be used together
OpenACC with MPI
As we learned that MPI is used to allow communication and data transfer between threads during parallel execution. In the case of multiple accelerators, one of the ways we can use the two together is to use MPI to communicate between different accelerators.
