Portability with OpenCL

1. Portability with OpenCL

2. High Performance Landscape • High Performance computing is trending towards large number of cores and accelerators • It would be nice to write your code once and have it be portable across these platforms • The selection of a programming model can impact your portability

3. Platforms • Multi-core CPU • An 8-core Sandy Bridge with AVX instructions has 64 GPU core-equivalents (each core can issue 5 instructions per clock cycle and can store the state of two threads. The standard CPUs will execute at a higher clock rate than a standard GPU • The Intel Xeon Phi Coprocessor (IXPC) has up to 61 cores which perform 16 single precision operations in a single instruction or 976 GPU cores. • NVIDIA Kepler SMX has 2880 GPU cores • An AMD Radeon GPU has up to 32 compute units that can issue 4 instructions per cycle which could be treated as 128 cores

4. CPU+Accelerator architecture

5. Memory Management • CUDA C requires the programmer to allocate device memory and explicitly copy data between host and device. • OpenCL requires the programmer to allocate buffers and copy data to the buffers. It hides some important details, however, in that it doesn't expose exactly where the buffer lives at various points during the program execution. • OpenACC allow the programmer to rely entirely on the compiler for memory management to get started, but offer optional data constructs and clauses to control and optimize when data is allocated on the device and moved between host and device

6. Parallelism Scheduling • CUDA and OpenCL have thread, block, grid abstractions • OpenMP provides core control, but most of the process is automated. • OpenACC exposes the three levels of parallelism as gang, worker and vector parallelism

7. Multithreading • All three devices require oversubscription to keep the compute units busy; that is, the program must expose extra (slack) parallelism so a compute unit can swap in another active thread when a thread stalls on memory or other long latency operations • In CUDA and OpenCL, slack parallelism comes from creating blocks or workgroups larger than the number of cores. • OpenMP allocates multiple iterations per core. • OpenACC worker-level parallelism is intended to address this issue directly. On the GPUs, iterations of a worker-parallel loop will run on the same core

8. Caching and Scratchpad Memories • In CUDA and OpenCL, the programmer must manage the scratchpad memory explicitly, using CUDA __shared__ or OpenCL __local memory • OpenACC has a cache directive to allow the programmer to tell the implementation what data has enough reuse to cache locally

9. Portability • There are three levels of portability. • First is language portability, meaning a programmer can use the same language to write a program for different targets, even if the programs must be different. • Second is functional portability, meaning a programmer can write one program that will run on different targets, though not all targets will get the best performance. • Third is performance portability, meaning a programmer can write one program that gives good performance across many targets.

10. Portability • CUDA provides reasonable portability across NVIDIA devices but there is no pretense that these provide cross-vendor portability, or even performance portability of CUDA source code. • OpenCL is designed to provide language and functionality portability. Research has demonstrated that even across similar devices, like NVIDIA and AMD GPUs, retuning or rewriting a program can have a significant impact on performance. • OpenACC is also intended to provide performance portability across devices, and there is some initial evidence to support this claim.

11. OpenCL to CUDA Data Parallelism Model Mapping

12. Overview of OpenCL Execution Model

13. Mapping of OpenCL Dimensions and Indices to CUDA

14. Conceptual OpenCL Device Architecture 14

15. Mapping OpenCL Memory Types to CUDA

16. OpenCL Context for Device Management

17. Structure of OpenCL main program Get information about platform and devices available on system Select devices to use Create an OpenCL command queue Create memory buffers on device Transfer data from host to device memory buffers Create kernel program object Build (compile) kernel in-line (or load precompiled binary) Create OpenCL kernel object Set kernel arguments Execute kernel Read kernel memory and copy to host memory.

18. Platform "The host plus a collection of devices managed by the OpenCL framework that allow an application to share resources and execute kernels on devices in the platform." Platforms represented by a cl_platform object, initialized with clGetPlatformID() http://opencl.codeplex.com/wikipage?title=OpenCL%20Tutorials%20-%201

19. Simple code for identifying platform //Platform cl_platform_id platform; clGetPlatformIDs (1, &platform, NULL); Returns number of OpenCL platforms available. If NULL, ignored. Number of platform entries List of OpenCL platforms found. (Platform IDs) In our case just one platform, identified by &platform

20. Context “The environment within which the kernels execute and the domain in which synchronization and memory management is defined. The context includes a set of devices, the memory accessible to those devices, the corresponding memory properties and one or more command-queues used to schedule execution of a kernel(s) or operations on memory objects.” The OpenCL Specification version 1.1 http://www.khronos.org/registry/cl/specs/opencl-1.1.pdf

21. Code for context //Context cl_context_properties props[3]; props[0] = (cl_context_properties) CL_CONTEXT_PLATFORM; props[1] = (cl_context_properties) platform; props[2] = (cl_context_properties) 0; cl_context GPUContext = clCreateContextFromType(props,CL_DEVICE_TYPE_GPU,NULL,NULL,NULL); //Context info size_t ParmDataBytes; clGetContextInfo(GPUContext,CL_CONTEXT_DEVICES,0,NULL,&ParmDataBytes); cl_device_id* GPUDevices = (cl_device_id*)malloc(ParmDataBytes); clGetContextInfo(GPUContext,CL_CONTEXT_DEVICES,ParmDataBytes,GPUDevices,NULL);

22. Command Queue “An object that holds commands that will be executed on a specific device. The command-queue is created on a specific device in a context. Commands to a command-queue are queued in-order but may be executed in-order or out-of-order. ...” The OpenCL Specification version 1.1 http://www.khronos.org/registry/cl/specs/opencl-1.1.pdf

23. Simple code for creating a command queue // Create command-queue cl_command_queue GPUCommandQueue = clCreateCommandQueue(GPUContext,GPUDevices[0],0,NULL);

24. Allocating memory on device OpenCL context, from clCreateContextFromType() Use clCreatBuffer: cl_mem clCreateBuffer(cl_context context, cl_mem_flags flags, size_t size, void *host_ptr, cl_int *errcode_ret) Bit field to specify type of allocation/usage (CL_MEM_READ_WRITE,…) No of bytes in buffer memory object Ptr to buffer data (May be previously allocated.) Returns memory object Returns error code if an error

25. Sample code for allocating memory on device for source data // source data on host, two vectors int *A, *B; A = new int[N]; B = new int[N]; for(int i = 0; i < N; i++) { A[i] = rand()%1000; B[i] = rand()%1000; } … // Allocate GPU memory for source vectors cl_mem GPUVector1 = clCreateBuffer(GPUContext,CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,sizeof(int)*N, A, NULL); cl_mem GPUVector2 = clCreateBuffer(GPUContext,CL_MEM_READ_ONLY | CL_MEM_COPY_HOST_PTR,sizeof(int)*N, B, NULL);

26. Sample code for allocating memory on device for results on GPU // Allocate GPU memory for output vector cl_mem GPUOutputVector = clCreateBuffer(GPUContext,CL_MEM_WRITE_ONLY,sizeof(int)*N, NULL,NULL);

27. Kernel Program Simple programs might be in the same file as the host code as our CUDA examples. In that case need to formed into strings in a character array. If in a separate file, can read that file into host program as a character string

28. If in same program as host, kernel needs to be strings (I think it can be a single string) Kernel program OpenCL qualifier to indicate kernel code const char* OpenCLSource[] = { "__kernel void vectorAdd (const __global int* a,", " const __global int* b,", " __global int* c)", "{", " unsigned int gid = get_global_id(0);", " c[gid] = a[gid] + b[gid];", "}" }; … int main(int argc, char **argv){ … } OpenCL qualifier to indicate kernel memory (Memory objects allocated from global memory pool) Returns global work-item ID in given dimension (0 here) Double underscores optional in OpenCL qualifiers

29. Kernel in a separate file // Load the kernel source code into the array source_str FILE *fp; char *source_str; size_t source_size; fp = fopen("vector_add_kernel.cl", "r"); if (!fp) { fprintf(stderr, "Failed to load kernel.\n"); exit(1); } source_str = (char*)malloc(MAX_SOURCE_SIZE); source_size = fread( source_str, 1, MAX_SOURCE_SIZE, fp); fclose( fp ); http://mywiki-science.wikispaces.com/OpenCL

30. Create kernel program object const char* OpenCLSource[] = { … }; int main(int argc, char **argv) … // Create OpenCL program object cl_program OpenCLProgram = clCreateProgramWithSource(GPUContext,7,OpenCLSource,NULL,NULL); This example uses a single file for both host and kernel code. Can use clCreateprogramWithSource() with a separate kernel file read into host program Used to return error code if error Number of strings in kernel program array Used if strings not null-terminated to given length of strings

31. Build kernel program // Build the program (OpenCL JIT compilation) clBuildProgram(OpenCLProgram,0,NULL,NULL,NULL,NULL); Arguments for notification routine Build options Number of devices Program object from clCreateProgramwithSource Function ptr to notification routine called with build complete. Then clBuildProgram will return immediately, otherwise only when build complete List of devices, if more than one

32. Creating Kernel Objects // Create a handle to the compiled OpenCL function cl_kernel OpenCLVectorAdd = clCreateKernel(OpenCLProgram, "vectorAdd", NULL); Built prgram from clBuildProgram Function name with __kernel qualifier Return error code

33. Set Kernel Arguments // Set kernel arguments clSetKernelArg(OpenCLVectorAdd,0,sizeof(cl_mem), (void*)&GPUVector1); clSetKernelArg(OpenCLVectorAdd,1,sizeof(cl_mem), (void*)&GPUVector2); clSetKernelArg(OpenCLVectorAdd,2,sizeof(cl_mem), (void*)&GPUOutputVector); Which argument Size of argument Pointer to data for argument, from clCreateBuffer() Kernel object from clCreateKernel()

34. Enqueue a command to execute kernel on device // Launch the kernel size_t WorkSize[1] = {N}; // Total number of work items size_t localWorkSize[1]={256}; //No of work items in work group // Launch the kernel clEnqueueNDRangeKernel(GPUCommandQueue,OpenCLVectorAdd,1,NULL, WorkSize, localWorkSize, 0, NULL, NULL); Dimensions of work items Kernel object from clCreatKernel() Offset used with work item Number of events to complete before this commands Array describing no of global work items Array describing no of work items that make up a work group Event wait list Event

35. Function to copy from buffer object to host memory The following function enqueue commands to read from a buffer object to host memory: cl_int clEnqueueReadBuffer (cl_command_queue command_queue, cl_mem buffer, cl_bool blocking_read, size_t offset, size_t cb, void *ptr, cl_uint num_events_in_wait_list, const cl_event *event_wait_list, cl_event *event) The OpenCL Specification version 1.1 http://www.khronos.org/registry/cl/specs/opencl-1.1.pdf

36. Function to copy from host memory to buffer object The following function enqueue commands to write to a buffer object from host memory: cl_int clEnqueueWriteBuffer (cl_command_queue command_queue, cl_mem buffer, cl_bool blocking_write, size_t offset, size_t cb, const void *ptr, cl_uint num_events_in_wait_list, const cl_event *event_wait_list, cl_event *event) The OpenCL Specification version 1.1 http://www.khronos.org/registry/cl/specs/opencl-1.1.pdf

37. Copy data back from kernel // Copy the output back to CPU memory int *C; C = new int[N]; clEnqueueReadBuffer(GPUCommandQueue,GPUOutputVector, CL_TRUE, 0, N*sizeof(int), C, 0, NULL, NULL); Command queue from clCreateCommandQueue Device buffer from clCreateBuffer Number of events to complete before this commands Read is blocking Byte offset in buffer Pointer to buffer in host to write data Event wait list Event Size of data to read in bytes

38. Results from GPU cout << "C[“ << 0 << "]: " << A[0] <<"+"<< B[0] <<"=" << C[0] << "\n"; cout << "C[“ << N-1 << "]: “ << A[N-1] << "+“ << B[N-1] << "=" << C[N-1] << "\n"; C++ here

39. Clean-up // Cleanup free(GPUDevices); clReleaseKernel(OpenCLVectorAdd); clReleaseProgram(OpenCLProgram); clReleaseCommandQueue(GPUCommandQueue); clReleaseContext(GPUContext); clReleaseMemObject(GPUVector1); clReleaseMemObject(GPUVector2); clReleaseMemObject(GPUOutputVector);

40. Compiling Need OpenCL header: #include <CL/cl.h> (For mac: #include <OpenCL/opencl.h> ) and link to the OpenCL library. Compile OpenCL host program main.c using gcc, two phases: gcc -c -I /path-to-include-dir-with-cl.h/ main.c -o main.o gcc -L /path-to-lib-folder-with-OpenCL-libfile/ -l OpenCL main.o -o host Ref: http://www.thebigblob.com/getting-started-with-opencl-and-gpu-computing/