1. Examples
The hello.cu contains the CUDA implementation of HelloWorld.
- Login to HPC
- Setup MPI toolchain:
- Compile
nvcc -O3 -arch=sm_20 hello.cu
- Run
The option -t specifies the limit of run time. Setting it as a small number will get your program scheduled earlier. For more information on srun options, you can use man srun to find out.
- Profile (optional)
srun -n1 –gres=gpu:p100:1 –partition=debug nvprof ./a.out
- Allocate a machine
module purge
module load gcc /8.3.0 cuda /10.1.243
srun -n1 –gres=gpu:1 -t1 ./a.out
salloc -n1 –gres=gpu:1 –mem=16G -t10
// After the allocation, you will log on the machine and have 10 minutes to perform multiple operations
./a.out
// edit , compile , and run again without waiting for a new allocation
./a.out ./a.out
2. (100 points) 1024 × 1024 matrix multiplication using these two approaches. • Approach 1 (unoptimized implementation using global memory only) :
– Name this program as ‘mm1.cu’
– The value of each element of A is 1
– The value of each element of B is 2
– Thread block configuration: 16 × 16
– Grid configuration: 64 × 64
– After computation, print the value of C[451][451]
• Approach 2 (block matrix multiplication using shared memory) :
- – Name this program as ‘mm2.cu’
- – The value of each element of A is 1
- – The value of each element of B is 2
- – Thread block configuration: 32 × 32
- – Grid configuration: 32 × 32
- – More details of this algorithm can be found in the paper ‘Matrix Multiplication with CUDA’.
- – After computation, print the value of C[451][451]
Measure the execution time of the kernel of Approach 1 and Approach 2, respectively.Submission Instructions: Submit your code, screenshots, and a performance report as described above.
