1. GPU PROGRAMMING David Gilbert California State University, Los Angeles

2. Outline CUDA CPU vs GPU Architecture Scalability Blocks Performance Speed Up Graphics Cards How It Works Program Flow When to Use the GPU Example: Matrix Row Sum References

3. CUDA Compute Unified Device Architecture (CUDA) High performance computing on your GPU CUDA is a proprietary architecture for GPU Computing, there is also OpenCL which runs on AMD/ATI

4. CPU vs GPU Architecture ALU does the computations

5. Scalability Code automatically scales upward GPUs with more cores will execute the same code in less time Can add additional graphics cards to your computer and gain exponential performance increases!

6. Blocks Essentially Groups Block Size and ThreadsPerBlock are defined before the memory is copied to the graphics card. To access a thread in a block i = blockIdx.x + threadIdx.x; j = blockIdx.y + threadIdx.y;

7. Performance Super computer performance is measured in Floating Point Operations Per Second (FLOPS) Megaflops = 10^6 Gigaflops = 10^9 Teraflops = 10^12 Petaflops = 10^15 Japan’s K Computer 10.51 Petaflops Nvidia GTX 480 ~1300 gigaflops Core i7 920 @3.4Ghz 69 gigaflops

8. Graphics Cards Consumer AMD 6950, $250 2.25 TFLOPs Single Precision compute power 562.5 GFLOPs Double Precision compute power 1408 Stream Processors Nvidia GTX 470, $150 1.09 TFLOPs Single Precision compute power 544.32 GFLOPs Double Precision compute power 448 Cuda Cores About $1 per TFLOP

9. Speed Up?

10. How it works Computer dumps the load onto the GPU GPU does the computing GPU returns the results to System Memory This transfer is the biggest bottleneck in the system CPUGPU Results Code

11. Program Flow 1. Allocate System Memory 2. Allocate Device Memory 3. Copy Memory from System to Device 4. Execute the Code 5. Copy Results back to the System from the Device 6. Free Device Memory 7. Process Results 8. Free System Memory Lines 3 and 5 create the bottleneck

12. When to Use the GPU Let dT = transfer time between device and system Let st = serial execution time Let pt = parallel execution time 2(dT) + pt < st

13. Example: Matrix Row Sum 0.50.25 0 00.5 0 000.750.25 0.5 0.25 0 0 0 0 Block size, 4X1 0.25 0.5 0 0.25 0.5 0.75

14. Example: Matrix Row Sum // Device code __global__ void RowSum(float* B, float* Sum, int N, int M) { int i = blockDim.x * blockIdx.x + threadIdx.x; int j = blockDim.y * blockIdx.y + threadIdx.y; if (i < N && j < M) C[j] += B[i][j]; } B is the matrix being summed Sum is the array storing the row sum N is # of rows M is # of cols

15. Example: Matrix Row Sum int main() { int M = 4, N = 4; // Allocate System Memory size_t size = N*M*sizeof(float); float * h_B = (float *)malloc(size); float * h_sum = (float *)malloc(size); // Allocate Device Memory float * d_B, * d_sum; cudaMalloc(&d_B, size); cudaMalloc(&d_sum, size); // Copy System Memory to Device cudaMemcpy(d_B, h_B, size, cudaMemcpyDeviceToHost); // Execute the code int threadsPerBlock = 4; int blocksPerGrid = 4; RowSum >>(d_B, d_sum, N, M); // Copy Results from Device Back to System Memory cudaMemcpy(h_sum, d_sum, size, cudaMemcpyDeviceToHost); // Free device Memory cudaFree(d_B); cudaFree(d_sum); // Process Results print results… // some method to display results // Free System Memory free(h_B); free(h_sum); return 0; }

16. Example: Matrix Row Sum Now, imagine a matrix of 1000 x 1000 I don’t guarantee that this code will run

17. References Newegg.com CUDA C Programming Guide http://developer.download.nvidia.com/compute/DevZone/docs/html/C /doc/CUDA_C_Programming_Guide.pdf AMD.com http://www.amd.com/us/products/desktop/graphics/amd-radeon-hd- 6000/hd-6950/Pages/amd-radeon-hd-6950-overview.aspx PCGameshardware.com http://www.pcgameshardware.com/aid,743498/Geforce-GTX-480- and-GTX-470-reviewed-Fermi-performance-benchmarks/Reviews/ Nvidia.com http://www.nvidia.com/object/product_geforce_gtx_470_us.html