1 OpenCL Work-Item Private Memory Workgroup Local Memory Compute Device Global/Constant Memory Host Host Memory OpenCL CUDA CUDA Compute Unit MP Proce

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1 GPGPU (VI) GPGPU 1 GPGPU CUDA CUDA GPGPU GPGPU CUDA GPGPU ( ) CUDA GPGPU 2 OpenCL OpenCL GPGPU Apple Khronos Group OpenCL Working Group [1] CUDA GPU NVIDIA GPU *1 OpenCL NVIDIA AMD GPU CPU DSP(Digital Signal Processor) NVIDIA GPU AMD GPU OpenCL OpenCL OpenCL CUDA CUDA OpenCL 2.1 OpenCL OpenCL 1 OpenCL Host( CPU ) ( )Compute Device( GPU ) Compute Device ( )Compute Unit( ) Compute Unit ( )Processing Element( ) OpenCL 2 OpenCL CUDA MP Block SP(CUDA Core) Thread Processing Element Work-Item( ) Compute Unit Workgroup( ) OpenCL *1 1

2 1 OpenCL Work-Item Private Memory Workgroup Local Memory Compute Device Global/Constant Memory Host Host Memory OpenCL CUDA CUDA Compute Unit MP Processing Element SP Private Memory Register Local Memory Shared Memory Global/Constant Memory Global Memory Host Memory Host Memory CUDA OpenCL OpenCL CUDA OpenCL GPU ( ) OpenCL 0 OpenCL Embedded Profile 2.2 OpenCL OpenCL OpenCL C/C++ OpenCL API OpenCL ( ) Compute Unit Processing Element (API) 2

3 2 OpenCL OpenCL C/C++ C/C++ GPU( ) kernel Global Memory global OpenCL CUDA OpenCL ( ) CUDA OpenCL OpenCL ( ) 3

4 2.3 OpenCL OpenCL CentOS 5.5 x86 64 CUDA 3.2RC TeslaC2050 PC AMD GPU ( ) 1 OpenCL ( ) (CPU ) : GPU : : : CPU/GPU CPU GPU : GPU GPU : GPU CPU : : 1 arraytest.cpp 1 #include <oclutils.h> 2 3 #define DATA LENGTH 16 4 cl context cxgpucontext; 5 cl kernel kernel; 6 cl command queue commandqueue; 7 8 #define CHK DO(name,o) cierrnum=o;if(cierrnum!=cl SUCCESS){printf(name);printf(" failed\n" );return( 1);} 9 #define CHK ERR(name) if(cierrnum!=cl SUCCESS){printf(name);printf(" failed\n");return ( 1);} int main(int argc, char argv){ 12 cl platform id cpplatform = NULL; 13 cl uint cidevicecount = 0; 14 cl device id cddevices = NULL; 15 cl int cierrnum = CL SUCCESS; 16 // get platform 17 CHK DO("oclGetPlatformID", oclgetplatformid(&cpplatform)); 18 // get devices 19 CHK DO("clGetDeviceIDs1", clgetdeviceids(cpplatform, CL DEVICE TYPE GPU, 0, NULL, & cidevicecount)); 20 cddevices = (cl device id )malloc(cidevicecount sizeof(cl device id)); 21 CHK DO("clGetDeviceIDs2", clgetdeviceids(cpplatform, CL DEVICE TYPE GPU, cidevicecount, cddevices, NULL)); 22 // get context 23 cxgpucontext = clcreatecontext(0, cidevicecount, cddevices, NULL, NULL, &cierrnum); 24 CHK ERR("clCreateContext"); 25 // create command queue 26 cl device id device = oclgetdev(cxgpucontext, 0); 27 commandqueue = clcreatecommandqueue(cxgpucontext, device, CL QUEUE PROFILING ENABLE, &cierrnum); 28 CHK ERR("clCreateCommandQueue"); 4

5 29 // program setup 30 size t program length; 31 const char source path = "gpu.cl"; 32 char source = oclloadprogsource(source path, "", &program length); 33 if(!source){printf("oclloadprogsource failed(%s)\n", source path);return 2000;} 34 // create the program 35 cl program cpprogram = clcreateprogramwithsource(cxgpucontext, 1, (const char )&source, & program length, &cierrnum); 36 CHK ERR("clCreateProgramWithSource"); 37 free(source); 38 // build the program 39 CHK DO("clBuildProgram", clbuildprogram(cpprogram, 0, NULL, "-cl-fast-relaxed-math", NULL, NULL)); 40 // Create Kernel 41 kernel = clcreatekernel(cpprogram, "arraytest", &cierrnum); 42 CHK ERR("clCreateKernel"); 43 // setup data 44 cl mem d A; 45 cl mem d R; 46 float h A data = (float )malloc(sizeof(float) DATA LENGTH); 47 for(int i=0; i<data LENGTH; i++)h A data[i] = (float)(i+1); 48 float h R data = (float )malloc(sizeof(float) DATA LENGTH); 49 d A = clcreatebuffer(cxgpucontext, CL MEM READ ONLY CL MEM COPY HOST PTR, sizeof( float) DATA LENGTH, h A data, NULL); 50 d R = clcreatebuffer(cxgpucontext, CL MEM WRITE ONLY, sizeof(float) DATA LENGTH, NULL, NULL); 51 float value = 2.0f; 52 // set args 53 clsetkernelarg(kernel, 0, sizeof(cl mem), (void )&d R); 54 clsetkernelarg(kernel, 1, sizeof(cl mem), (void )&d A); 55 clsetkernelarg(kernel, 2, sizeof(cl float), (void )&value); 56 // run kernel 57 cl event GPUExecution; 58 size t localworksize[] = {4}; 59 size t globalworksize[] = {DATA LENGTH}; 60 clenqueuendrangekernel(commandqueue, kernel, 1, 0, globalworksize, localworksize, 0, NULL, & GPUExecution); 61 clflush(commandqueue); 62 // sync 63 clfinish(commandqueue); 64 // bloking readback 65 clenqueuereadbuffer(commandqueue, d R, CL TRUE, 0, sizeof(float) DATA LENGTH, h R data, 0, NULL, NULL); 66 // check result 67 printf("before: "); for(int i=0; i<data LENGTH; i++){printf(" %.2f", h A data[i]);}printf("\n"); 68 printf("after : "); for(int i=0; i<data LENGTH; i++){printf(" %.2f", h R data[i]);}printf("\n"); 69 // release mem and event 70 clreleasememobject(d A); 71 clreleasememobject(d R); 72 clreleaseevent(gpuexecution); 73 // cleanup 74 cierrnum = clreleasekernel(kernel); 75 cierrnum = clreleasecommandqueue(commandqueue); 76 cierrnum = clreleaseprogram(cpprogram); 77 cierrnum = clreleasecontext(cxgpucontext); 78 CHK ERR("release"); 79 free(h A data); 80 free(h R data); 81 return 0; 82 } GPU PlatformID Device DeviceID Context 2 GPU 5

6 2 arraytest.cpp( ) 1 // get context create command queue 2 size t ndevicebytes; 3 CHK DO("clGetContextInfo", clgetcontextinfo(cxgpucontext, CL CONTEXT DEVICES, 0, NULL, &ndevicebytes)); 4 cidevicecount = (cl uint)ndevicebytes/sizeof(cl device id); 5 if(cidevicecount == 0){printf("no devices (return code %i)\n", cierrnum);return 1;} 6 // check all devices (get device and print the device name) 7 for(unsigned int i = 0; i < cidevicecount; ++i){ 8 // device GPU 9 cl device id device = oclgetdev(cxgpucontext, i); 10 printf("device %d: ", i); 11 oclprintdevname(logboth, device); // GPU 12 printf("\n"); 13 } clsetkernelarg API GPU API ( ) clenqueuendrangekernel 5 6 (localworksize globalworksize) CUDA Thread Block Thread Block OpenCL Work-Item CUDA Thread Block Thread CUDA ( ) WorkSize 2 CUDA (CUDA C) CUDA C CUDA Driver API CUDA CUDA OpenCL API OpenCL CUDA CUDA Stream( ) Stream GPU CPU-GPU Stream CUDA C 6

7 1. ( ) oclloadprogsource 2. clcreateprogramwithsource 3. clbuildprogram 4. clcreatekernel 3 CUDA ( ) Work-Item ID CUDA ID get global id Work-Item 1 3 gpu.cl 1 #define DATA LENGTH 16 2 kernel void arraytest( global float R, global float A, float value){ 3 int i; 4 i = get global id(0); 5 R[i] = A[i] value; 6 } CUDA CUDA CUDA CUDA ( ) OpenCL OpenCL CUDA nvcc OpenCL 3 gcc(g++) OpenCL OpenCL CUDA CUDA(CUDA C) CUDA Driver API CUDA C OpenGL GLUT ( ) OpenCL 7

8 $ ls arraytest.cpp gpu.cl $ g++ -O3 -m64 -o arraytest arraytest.cpp -lopencl \ -I/path_to_cudasdk_3.1/OpenCL/common/inc -I/path_to_cudasdk_3.1/shared/inc \ -L/path_to_cudasdk_3.1/OpenCL/common/lib -L/path_to_cudasdk_3.1/shared/lib \ -loclutil_x86_64 -lshrutil_x86_64 $ ls arraytest arraytest.cpp gpu.cl $./arraytest before: ( ) after : ( ) 3 OpenCL 3 GPGPU 3.1 GPGPU GPGPU C/C++ pthread OpenMP MPI GPGPU GPGPU OpenMP CUDA OMPCUDA[3] OMPCUDA OpenMP parallel for (for ) for GPU GPU CPU-GPU OpenMP OMPCUDA GPU SP ( 4) OMPCUDA OpenMP OMNI OpenMP compiler( OMNI)[2] OMNI OMPCUDA 5 OMNI OMPCUDA OMPCUDA CUDA GPU OMPCUDA OpenMP C/C++ Fortran OMPCUDA 8

9 4 OpenMP OMPCUDA 5 OMNI OMPCUDA 9

10 OMPCUDA Lee [5] OpenMP CUDA OpenMP GPGPU PGI [4] CAPS HMPP[6] 3.2 GPGPU CUDA OpenCL C/C++ C/C++ HPC C/C++ Fortran Java, Perl, Python, Ruby,.NET ( ) CUDA OpenCL GPU PyCUDA[7], PyOpenCL[8] PyCUDA PyOpenCL Python CUDA OpenCL Python CUDA OpenCL CPU-GPU GPU Python C++ Ruby-OpenCL[9] Ruby-OpenCL PyOpenCL Ruby OpenCL GPU Python Ruby JCuda[10] JCuda Java CUDA Python/Ruby GPU (PTX) CUDA (CUBLAS, CUFFT ) API CUDA Fortran[11] CUDA Fortran Fortran CUDA PGI CUDA Fortran C/C++ Fortran ( ) CUDA C Fortran CUDA OpenCL GPU 10

11 GPU GPU 3.3 GPGPU / GPU GPU CUDA GPU GPU ( / ) CUDA 2005 GPU BrookGPU[12] RapidMind[14] BrookGPU Stanford University 6 BrookGPU (kfunc) (streamread,streawrite) 2 GPU BrookGPU DirectX, OpenGL, CPU, CTM(Close to the Metal AMD ) AMD GPU BrookGPU Brook+[13] RapidMind University of Waterloo Sh[15] RAPIDMIND 7 RapidMind RapidMind BrookGPU RapidMind PetaFLOPS ExaFLOPS GPGPU ( ) CUDA GPGPU CUDA 11

12 // kernel void kfunc (float x<>, float y<>, out float z<>) { z = x + y; } int main() { float a<100>; float b<100>; float c <100>; // streamread(a, data1); streamread(b, data2); // kfunc(a, b); // streamwrite(c, result); return 0; } int main() { // Program kfunc=begin { In<Value3f>x, y; Out<Value3f>z; z = x + y; } END; // Array<1, Value3f> a(512); Array<1, Value3f> b(512); Array<1, Value3f> c(512); // c = kfunc(a, b); return 0; } 6 BrookGPU 7 RapidMind OpenCL NVIDIA GPU GPU C/C++ GPGPU GPGPU GPGPU Tianhe-1A ( TOP500 RadeonHD 5 ) 14,336 CPU 7,168 TeslaM2050 LINPACK 2PFLOPS TOP500( ) TSUBAME 4,224 TeslaM2050 TSUBAME 2.0 TOP500 Green500 GPGPU GPU GPU GPU GPU GPU GPGPU [1] OpenCL - The open standard for parallel programming of heterogeneous systems, http: // [2] M.Sato, S.Satoh, K.Kusano, and Y.Tanaka. Design of OpenMP Compiler for an SMP Cluster. 12

13 In EWOMP 99, pp , [3],,. OMPCUDA : GPU OpenMP. HPCS , pp , [4] PGI. PGI Accelerator Compilers, [5] Seyong Lee, Seung-Jai Min, Rudolf Eigenmann. OpenMP to GPGPU: a compiler framework for automatic translation and optimization. Proceedings of the 14th ACM SIGPLAN symposium on Principles and practice of parallel programming, pp , [6] CAPS. HMPP Workbench, 49\&p\ p=36 [7] PyCUDA, [8] PyOpenCL, [9] Ruby-OpenCL, [10] jcuda, [11] CUDA Fortran, [12] Ian Buck, Tim Foley, Daniel Horn, Jeremy Sugerman, Kayvon Fatahalian, Mike Houston, and Pat Hanrahan. Brook for GPUs: Stream Computing on Graphics Hardware. SIGGRAPH 2004, [13] AMD. Brook+. SC07 BOF Session presentation, November [14] Michael D. McCool. Data-Parallel Programming on the Cell BE and the GPU using the RapidMind Development Platform. In GSPx Multicore Applications Conference, [15] Michael McCool and Stefanus Du Toit. Metaprogramming GPUs with Sh. A K Peters Ltd,

1 OpenCL OpenCL 1 OpenCL GPU ( ) 1 OpenCL Compute Units Elements OpenCL OpenCL SPMD (Single-Program, Multiple-Data) SPMD OpenCL work-item work-group N

1 OpenCL OpenCL 1 OpenCL GPU ( ) 1 OpenCL Compute Units Elements OpenCL OpenCL SPMD (Single-Program, Multiple-Data) SPMD OpenCL work-item work-group N GPU 1 1 2 1, 3 2, 3 (Graphics Unit: GPU) GPU GPU GPU Evaluation of GPU Computing Based on An Automatic Program Generation Technology Makoto Sugawara, 1 Katsuto Sato, 1 Kazuhiko Komatsu, 2 Hiroyuki Takizawa