1. 1 ITCS 6/8010 CUDA Programming, UNC-Charlotte, B. Wilkinson, Feb 14, 2011 Streams.pptx CUDA Streams These notes will introduce the use of multiple CUDA streams to overlap memory transfers with kernel computations. Also introduced is paged-locked memory These materials come from Chapter 10 of “CUDA by Example” by Jason Sanders and Edwards Kandrot.

2. 2 Page-locked host memory (also called pinned host memory) Page-locked memory is not paged in and out main memory by the OS through paging but will remain resident. Allows: Concurrent host/device memory transfers with kernel operations (Compute capability 2.x) – see next Host memory can be mapped to device address space (Compute capability > 1.0) Memory bandwidth is higher Uses real addresses rather than virtual addresses Does not need to intermediate copy buffering

3. 3 Note on using page-locked memory Using page-locked memory will reduce memory available to the OS for paging and so need to be careful in allocating it

4. 4 Allocating page locked memory cudaMallocHost ( void ** ptr, size_t size ) Allocates page-locked host memory that is accessible to device cudaHostAlloc ( void ** ptr, size_t size, unsigned int flags) Allocates page-locked host memory that is accessible to device – seems to have more options

5. 5 //Pinned memory test written by Barry Wilkinson, UNC-Charlotte. Feb 10, 2011. #include #define SIZE (10*1024*1024) // number of bytes in arrays 10 MBytes int main(int argc, char *argv[]) { int i; // loop counter int *a; int *dev_a; cudaEvent_t start, stop; // using cuda events to measure time cudaEventCreate(&start); // create events cudaEventCreate(&stop); float elapsed_time_ms1, elapsed_time_ms3; /* --------------------ENTER INPUT PARAMETERS AND DATA -----------------------*/ cudaMalloc((void**)&dev_a, SIZE);// allocate memory on device /* ---------------- COPY USING PINNED MEMORY -------------------- */ cudaHostAlloc((void**)&a, SIZE,cudaHostAllocDefault);// allocate page-locked memory on host cudaEventRecord(start, 0); for(i = 0; i < 100; i++) { cudaMemcpy(dev_a, a, SIZE,cudaMemcpyHostToDevice);//copy to device cudaMemcpy(a,dev_a, SIZE,cudaMemcpyDeviceToHost);//copy back to host } cudaEventRecord(stop, 0); // instrument code to measue end time cudaEventSynchronize(stop); cudaEventElapsedTime(&elapsed_time_ms1, start, stop ); printf("Time to copy %d bytes of data 100 times on GPU, pinned memory: %f ms\n", SIZE, elapsed_time_ms1); // exec. time Test of Pinned Memory

6. 6 /* ---------------- COPY USING REGULAR MEMORY-------------------- */ a = (int*) malloc(SIZE);// allocate regular memory on host cudaEventRecord(start, 0); for(i = 0; i < 100; i++) { cudaMemcpy(dev_a, a, SIZE,cudaMemcpyHostToDevice);//copy to device cudaMemcpy(a,dev_a, SIZE,cudaMemcpyDeviceToHost);//copy back to host } cudaEventRecord(stop, 0); // instrument code to measue end time cudaEventSynchronize(stop); cudaEventElapsedTime(&elapsed_time_ms3, start, stop ); printf("Time to copy %d bytes of data 100 times on GPU: %f ms\n", SIZE, elapsed_time_ms3); // exec. time /*--------------------------SPEEDUP ---------------------------------*/ printf("Speedup of using pinned memory = %f\n", (float) elapsed_time_ms3 / (float) elapsed_time_ms1); /* -------------- clean up ---------------------------------------*/ free(a); cudaFree(dev_a); cudaEventDestroy(start); cudaEventDestroy(stop); return 0; }

7. 7 My code

8. 8./bandwidthTest Starting... Running on... Device 0: Tesla C2050 Quick Mode Host to Device Bandwidth, 1 Device(s), Paged memory Transfer Size (Bytes) Bandwidth(MB/s) 33554432 1026.7 Device to Host Bandwidth, 1 Device(s), Paged memory Transfer Size (Bytes) Bandwidth(MB/s) 33554432 1108.1 Device to Device Bandwidth, 1 Device(s) Transfer Size (Bytes) Bandwidth(MB/s) 33554432 84097.6 [bandwidthTest] - Test results: PASSED Press to Quit... ----------------------------------------------------------- Using NVIDIA sample code for bandwidth on coit-grid06

9. 9 CUDA Streams A CUDA Stream is a sequence of operations (commands) that are executed in order. CUDA streams can be created and executed together and interleaved although the “program order” is always maintained within each stream. Streams proved a mechanism to overlap memory transfer and computations operations in different stream for increased performance if sufficient resources are available.

10. 10 Creating a stream Done by creating a stream object and associated it with a series of CUDA commands that then becomes the stream. CUDA commands have a stream pointer as an argument: cudaStream_t stream1; cudaStreamCreate(&stream1); cudaMemcpyAsync(…, stream1); MyKernel >>(…); cudaMemcpyAsync(…, stream1); Cannot use regular cudaMemcpy with streams, need asynchronous commands for concurrent operation see next Stream

11. 11 cudaMemcpyAsync( …, stream) Asynchronous version of cudaMemcpy that copies date to/from host and the device May return before copy complete A stream argument specified. Needs “page-locked” memory

12. 12 #define SIZE (N*20) … int main(void) { int *a, *b, *c; int *dev_a, *dev_b, *dev_c; cudaMalloc( (void**)&dev_a, N * sizeof(int) ); cudaMalloc( (void**)&dev_b, N * sizeof(int) ); cudaMalloc( (void**)&dev_c, N * sizeof(int) ); cudaHostAlloc((void**)&a,SIZE*sizeof(int),cudaHostAllocDefault); // paged-locked cudaHostAlloc((void**)&b,SIZE*sizeof(int),cudaHostAllocDefault); cudaHostAlloc((void**)&c,SIZE*sizeof(int),cudaHostAllocDefault); for(int i=0;i<SIZE;i++) { // load data a[i] = rand(); b[i] = rand(); } for(int i=0;I < SIZE;i+= N { // loop over data in chunks cudaMemcpyAsync(dev_a,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream); cudaMemcpyAsync(dev_b,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream); kernel >>(dev_a,dev-b,dev_c); cudaMemcpyAsync(c+1,dev_c,N*sizeof(int),cudaMemcpyDeviceToHost,stream); } cudaStreamSynchronise(stream); // wait for stream to finish return 0; } Code Example Page 194-95 CUDA by Example, without error detection macros One stream

13. 13 Multiple streams Assuming device can support it (can check in code if needed), create two streams with: cudaStream_t stream1, stream2; cudaStreamCreate(&stream1); cudaStreamCreate(&stream2); and then duplicate stream code for each stream

14. 14 int *dev_a1, *dev_b1, *dev_c1; // stream 1 mem ptrs int *dev_a2, *dev_b2, *dev_c2; // stream 2 mem ptrs //stream 1 cudaMalloc( (void**)&dev_a1, N * sizeof(int) ); cudaMalloc( (void**)&dev_b1, N * sizeof(int) ); cudaMalloc( (void**)&dev_c1, N * sizeof(int) ); //stream 2 cudaMalloc( (void**)&dev_a2, N * sizeof(int) ); cudaMalloc( (void**)&dev_b2, N * sizeof(int) ); cudaMalloc( (void**)&dev_c2, N * sizeof(int) ); … for(int i=0;I < SIZE;i+= N*2 { // loop over data in chunks // stream 1 cudaMemcpyAsync(dev_a1,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream1); cudaMemcpyAsync(dev_b1,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream1); kernel >>(dev_a,dev-b,dev_c); cudaMemcpyAsync(c+1,dev_c1,N*sizeof(int),cudaMemcpyDeviceToHost,stream1); //stream 2 cudaMemcpyAsync(dev_a2,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream2); cudaMemcpyAsync(dev_b2,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream2); kernel >>(dev_a,dev-b,dev_c); cudaMemcpyAsync(c+1,dev_c2,N*sizeof(int),cudaMemcpyDeviceToHost,stream2); } cudaStreamSynchronise(stream1); // wait for stream to finish cudaStreamSynchronise(stream2); // wait for stream to finish First attempt described in book concatenate statements of each stream

15. 15 Simply concatenating statements does not work well because of the way the GPU schedules work Page 206 CUDA by Example,

16. 16 Page 207 CUDA by Example,

17. 17 Page 208 CUDA by Example

18. 18 for(int i=0;I < SIZE;i+= N*2 { // loop over data in chunks // interleave stream 1 and stream 2 cudaMemcpyAsync(dev_a1,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream1); cudaMemcpyAsync(dev_a2,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream2); cudaMemcpyAsync(dev_b1,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream1); cudaMemcpyAsync(dev_b2,a+i,N*sizeof(int),cudaMemcpyHostToDevice,stream2); kernel >>(dev_a,dev-b,dev_c); cudaMemcpyAsync(c+1,dev_c1,N*sizeof(int),cudaMemcpyDeviceToHost,stream1); cudaMemcpyAsync(c+1,dev_c2,N*sizeof(int),cudaMemcpyDeviceToHost,stream2); } Second attempt described in book Interleave statements of each stream

19. 19 Page 210 CUDA by Example

20. Questions