Matrix Addition and Matrix Multiplication
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Matrix Addition and Matrix Multiplication: Project # 1 – Matrix Addition and Matrix Multiplication
Task 1: Basic Matrix Addition
For this task, you will develop a complete CUDA program for integer matrix addition. You will add two two-dimensional matrices A, B on the device GPU in parallel. After the device matrix addition kernel function is invoked, and the addition result will be transferred back to the CPU. Your program will also compute the sum matrix of matrices A and B using the CPU. Your program should compare the device-computed result with the CPU-computed result. If it matches, then it will print out “Test PASSED” to the screen before exiting.
The pseudo code for matrix addition on the CPU is as follows:
void add_matrix_cpu(int *a, int *b, int *c, int N)
{
int i, j, index;
for (i=0;i<N;i++) {
for (j=0;j<N;j++) {
index =i*N+j;
c[index]=a[index]+b[index];
}
}
}
void main() {
…..
add_matrix(a,b,c,N);
}
The pseudo code for matrix addition on the GPU device is as follows:
CUDA C program
__global__ void add_matrix_gpu(int *a, int *b, int *c, intN)
{
int col =blockIdx.x*blockDim.x+threadIdx.x;
int row=blockIdx.y*blockDim.y+threadIdx.y;
int index =row*N+col;
if( row<N && col <N)
c[index]=a[index]+b[index];
}
void main() {
dim3 dimBlock(blocksize, blocksize,1);
dim3 dimGrid( ceiling (double (N) /dimBlock.x), ceiling (double (N) /dimBlock.y), 1 );
add_matrix_gpu<<<dimGrid, dimBlock>>>(a,b,c,N);
}
Use the following pseudo code for matrix initialization.
int *a, *b, *c;
A=malloc(sizeof(int)*N*N; //N is the size
//then malloc for b and c
Int init =1325;
For (i=0;i<N;i++){
For (j=0;j<N;j++){
Init=3125*init%65536;
A[i][j]=(init-32768)/6553;
B[i][j]=Init%1000;
}
}
Use the following matrix size and thread block size (the number of threads in each block) to test your cuda program.
| Matrix Size |
Size of Thread block |
| 8*8 |
4*4 (For debugging purpose) |
| 128*128 |
16*16 |
| 500*500 |
16*16 |
| 1000*1000 |
16*16 |
Task 2: Matrix Multiplication
For this task, you will develop a complete CUDA program for matrix multiplication. You will multiply two two-dimensional matrices A,B on the device GPU in paralell. After the device matrix multiplication kernel function is invoked, and the multiplication result will be transferred back to the CPU. Your program will also compute the product matrix of matrices A and B using the CPU. Your program should compare the device-computed result with the CPU-computed result. If it matches, then it will print out “Test PASSED” to the screen before exiting.
The pseudo code for matrix multiplication on the CPU is as follows:
void MatrixMulOnHost(int* M, int* N, int* P, int Width)
{
for (int i = 0; i < Width; ++i)
for (int j = 0; j < Width; ++j) {
int sum = 0;
for (int k = 0; k < Width; ++k) {
int a = M[i * Width + k];
int b = N[k * Width + j];
sum += a * b;
}
P[i * Width + j] = sum;
}
}
void main() {
…..
add_matrix(a,b,c,N);
}
The pseudo code for matrix addition on the GPU device is as follows:
CUDA C program
__global__ void MatrixMulKernel(int* M, int* N, int * P, int Width)
{
int Row = blockIdx.y*blockDim.y+threadIdx.y;
int Col = blockIdx.x*blockDim.x+threadIdx.x;
if ((Row < Width) && (Col < Width)) {
int Pvalue = 0;
for (int k = 0; k < Width; ++k)
Pvalue += M[Row*Width+k] * N[k*Width+Col];
d_P[Row*Width+Col] = Pvalue;
}
}
void main() {
dim3 dimBlock(blocksize,blocksize,1);
dim3 dimGrid( ceiling (double (N) /dimBlock.x), ceiling (double (N) /dimBlock.y), 1 );
add_matrix_gpu<<<dimGrid,dimBlock>>>(a,b,c,N);
}
Use the following pseudo code for matrix initialization.
int *a, *b, *c;
A=malloc(sizeof(int)*N*N; //N is the size
//then malloc for b and c
Int init =1325;
For (i=0;i<N;i++){
For (j=0;j<N;j++){
Init=3125*init%65536;
A[i,j]=(init-32768)/6553;
B[i,j]=Init%1000;
}
}
Use the following matrix size and thread block size (the number of threads in each block).
| Matrix Size |
Size of Thread block |
| 8*8 |
4*4 (For debugging purpose) |
| 128*128 |
16*16 |
| 500*500 |
16*16 |
| 1024*1024 |
16*16 |
Requirements:
1. In order to use the cuda compiler environment installed under the cs unix server, fry.cs.wright.edu, you need to connect to this unix server remotely using a secure shell client, such as putty. You can remotely connect to this unix server, fry.cs.wright.edu, on campus from a Wright State computer or use your own laptop connecting to the WSU wifi network named “WSU-Secure”. Note that you cannot remotely connect to this computer using ssh using computers outside Wright State University without installing VPN or use the campus “WSU_EZ_CONNECT” wifi network. If you want to connect to this server remotely off campus, you need to install VPN on your computer first. If you want to edit your cuda source programs under windows, download notepad++. Then edit your source programs using notepad++. After you finish editing the cuda source programs, using the secure file transfer client (WinSCP, you can download it online, and install it on your personal computer) to transfer your cuda source programs to fry.cs.wright.edu.
2. You must submit an ELECTRONIC COPY of your source program through Pilot before the due date. If for some reason Pilot is unavailable, submit your source code by email to meilin.liu@wright.edu.
3. Submit all your source codes, a README file, a report, and any other required files. It is required that you explain how to compile and run your programs clearly in the README file. In the report, please report whether your programs have all the functionalities required in the project description. In your report, please state clearly any functionalities not implemented in your program. If your program works correctly, please include screenshots in your report. Your submitted file name should use your last name as part of the file name, for example, Liu_Project1.cpp, Liu_Project1_Report, Liu_Project1_ReadMe, etc. All the submitted project files should have: Course Number / Course Title, your name, group member’s name, prof.’s name, date, and the project name. If you did not include these required contents in your submitted files, then 5 points will be deducted.
4. The grader or the instructor will test your programs under CUDA environment, on the linux server, fry.cs.wright.edu. Before you submit your program, please connect to this server using your campus ID to test your program (I have demoed how to compile and execute a cuda program on this server. If you have questions, let me know).
5. The programming assignment is individual. You must finish the project by yourself. If you allow others to copy your programs or answers, you will get the same punishment as those who copy yours.
How to use CUDA on fry.cs.wright.edu
First using putty or other secure shell clients to connect to fry.cs.wright.edu using your campus id (for example, w123abc), then run the following command:
srun -p a100 –gres=gpu:1 –pty bash
This command will request access to a gpu node and launch a bash shell on it.
Then you can compile a cuda program vectadd.cu using the following command under the directory where your source cuda program is located.
nvcc vectadd.cu -o vectadd
Then you can execute vectadd using the following command under the directory where the generated executable file (of your cuda source program), vectadd, is located.
./vectadd
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