| // |
| // Copyright (c) 2017 The Khronos Group Inc. |
| // |
| // Licensed under the Apache License, Version 2.0 (the "License"); |
| // you may not use this file except in compliance with the License. |
| // You may obtain a copy of the License at |
| // |
| // http://www.apache.org/licenses/LICENSE-2.0 |
| // |
| // Unless required by applicable law or agreed to in writing, software |
| // distributed under the License is distributed on an "AS IS" BASIS, |
| // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| // See the License for the specific language governing permissions and |
| // limitations under the License. |
| // |
| #include "Utility.h" |
| |
| #include <string.h> |
| #include "FunctionList.h" |
| |
| #if defined( __APPLE__ ) |
| #include <sys/time.h> |
| #endif |
| |
| int TestFunc_Float_Float(const Func *f, MTdata, bool relaxedMode); |
| int TestFunc_Double_Double(const Func *f, MTdata, bool relaxedMode); |
| |
| extern const vtbl _unary = { "unary", TestFunc_Float_Float, |
| TestFunc_Double_Double }; |
| |
| static int BuildKernel(const char *name, int vectorSize, cl_uint kernel_count, |
| cl_kernel *k, cl_program *p, bool relaxedMode); |
| static int BuildKernelDouble(const char *name, int vectorSize, |
| cl_uint kernel_count, cl_kernel *k, cl_program *p, |
| bool relaxedMode); |
| |
| static int BuildKernel(const char *name, int vectorSize, cl_uint kernel_count, |
| cl_kernel *k, cl_program *p, bool relaxedMode) |
| { |
| const char *c[] = { |
| "__kernel void math_kernel", sizeNames[vectorSize], "( __global float", sizeNames[vectorSize], "* out, __global float", sizeNames[vectorSize], "* in)\n" |
| "{\n" |
| " int i = get_global_id(0);\n" |
| " out[i] = ", name, "( in[i] );\n" |
| "}\n" |
| }; |
| const char *c3[] = { "__kernel void math_kernel", sizeNames[vectorSize], "( __global float* out, __global float* in)\n" |
| "{\n" |
| " size_t i = get_global_id(0);\n" |
| " if( i + 1 < get_global_size(0) )\n" |
| " {\n" |
| " float3 f0 = vload3( 0, in + 3 * i );\n" |
| " f0 = ", name, "( f0 );\n" |
| " vstore3( f0, 0, out + 3*i );\n" |
| " }\n" |
| " else\n" |
| " {\n" |
| " size_t parity = i & 1; // Figure out how many elements are left over after BUFFER_SIZE % (3*sizeof(float)). Assume power of two buffer size \n" |
| " float3 f0;\n" |
| " switch( parity )\n" |
| " {\n" |
| " case 1:\n" |
| " f0 = (float3)( in[3*i], NAN, NAN ); \n" |
| " break;\n" |
| " case 0:\n" |
| " f0 = (float3)( in[3*i], in[3*i+1], NAN ); \n" |
| " break;\n" |
| " }\n" |
| " f0 = ", name, "( f0 );\n" |
| " switch( parity )\n" |
| " {\n" |
| " case 0:\n" |
| " out[3*i+1] = f0.y; \n" |
| " // fall through\n" |
| " case 1:\n" |
| " out[3*i] = f0.x; \n" |
| " break;\n" |
| " }\n" |
| " }\n" |
| "}\n" |
| }; |
| |
| |
| const char **kern = c; |
| size_t kernSize = sizeof(c)/sizeof(c[0]); |
| |
| if( sizeValues[vectorSize] == 3 ) |
| { |
| kern = c3; |
| kernSize = sizeof(c3)/sizeof(c3[0]); |
| } |
| |
| char testName[32]; |
| snprintf( testName, sizeof( testName ) -1, "math_kernel%s", sizeNames[vectorSize] ); |
| |
| return MakeKernels(kern, (cl_uint)kernSize, testName, kernel_count, k, p, |
| relaxedMode); |
| } |
| |
| static int BuildKernelDouble(const char *name, int vectorSize, |
| cl_uint kernel_count, cl_kernel *k, cl_program *p, |
| bool relaxedMode) |
| { |
| const char *c[] = { "#pragma OPENCL EXTENSION cl_khr_fp64 : enable\n", |
| "__kernel void math_kernel", sizeNames[vectorSize], "( __global double", sizeNames[vectorSize], "* out, __global double", sizeNames[vectorSize], "* in)\n" |
| "{\n" |
| " int i = get_global_id(0);\n" |
| " out[i] = ", name, "( in[i] );\n" |
| "}\n" |
| }; |
| |
| const char *c3[] = { "#pragma OPENCL EXTENSION cl_khr_fp64 : enable\n", |
| "__kernel void math_kernel", sizeNames[vectorSize], "( __global double* out, __global double* in)\n" |
| "{\n" |
| " size_t i = get_global_id(0);\n" |
| " if( i + 1 < get_global_size(0) )\n" |
| " {\n" |
| " double3 f0 = vload3( 0, in + 3 * i );\n" |
| " f0 = ", name, "( f0 );\n" |
| " vstore3( f0, 0, out + 3*i );\n" |
| " }\n" |
| " else\n" |
| " {\n" |
| " size_t parity = i & 1; // Figure out how many elements are left over after BUFFER_SIZE % (3*sizeof(float)). Assume power of two buffer size \n" |
| " double3 f0;\n" |
| " switch( parity )\n" |
| " {\n" |
| " case 1:\n" |
| " f0 = (double3)( in[3*i], NAN, NAN ); \n" |
| " break;\n" |
| " case 0:\n" |
| " f0 = (double3)( in[3*i], in[3*i+1], NAN ); \n" |
| " break;\n" |
| " }\n" |
| " f0 = ", name, "( f0 );\n" |
| " switch( parity )\n" |
| " {\n" |
| " case 0:\n" |
| " out[3*i+1] = f0.y; \n" |
| " // fall through\n" |
| " case 1:\n" |
| " out[3*i] = f0.x; \n" |
| " break;\n" |
| " }\n" |
| " }\n" |
| "}\n" |
| }; |
| |
| const char **kern = c; |
| size_t kernSize = sizeof(c)/sizeof(c[0]); |
| |
| if( sizeValues[vectorSize] == 3 ) |
| { |
| kern = c3; |
| kernSize = sizeof(c3)/sizeof(c3[0]); |
| } |
| |
| |
| char testName[32]; |
| snprintf( testName, sizeof( testName ) -1, "math_kernel%s", sizeNames[vectorSize] ); |
| |
| return MakeKernels(kern, (cl_uint)kernSize, testName, kernel_count, k, p, |
| relaxedMode); |
| } |
| |
| typedef struct BuildKernelInfo |
| { |
| cl_uint offset; // the first vector size to build |
| cl_uint kernel_count; |
| cl_kernel **kernels; |
| cl_program *programs; |
| const char *nameInCode; |
| bool relaxedMode; // Whether to build with -cl-fast-relaxed-math. |
| }BuildKernelInfo; |
| |
| static cl_int BuildKernel_FloatFn( cl_uint job_id, cl_uint thread_id UNUSED, void *p ); |
| static cl_int BuildKernel_FloatFn( cl_uint job_id, cl_uint thread_id UNUSED, void *p ) |
| { |
| BuildKernelInfo *info = (BuildKernelInfo*) p; |
| cl_uint i = info->offset + job_id; |
| return BuildKernel(info->nameInCode, i, info->kernel_count, |
| info->kernels[i], info->programs + i, info->relaxedMode); |
| } |
| |
| static cl_int BuildKernel_DoubleFn( cl_uint job_id, cl_uint thread_id UNUSED, void *p ); |
| static cl_int BuildKernel_DoubleFn( cl_uint job_id, cl_uint thread_id UNUSED, void *p ) |
| { |
| BuildKernelInfo *info = (BuildKernelInfo*) p; |
| cl_uint i = info->offset + job_id; |
| return BuildKernelDouble(info->nameInCode, i, info->kernel_count, |
| info->kernels[i], info->programs + i, |
| info->relaxedMode); |
| } |
| |
| //Thread specific data for a worker thread |
| typedef struct ThreadInfo |
| { |
| cl_mem inBuf; // input buffer for the thread |
| cl_mem outBuf[ VECTOR_SIZE_COUNT ]; // output buffers for the thread |
| float maxError; // max error value. Init to 0. |
| double maxErrorValue; // position of the max error value. Init to 0. |
| cl_command_queue tQueue; // per thread command queue to improve performance |
| }ThreadInfo; |
| |
| typedef struct TestInfo |
| { |
| size_t subBufferSize; // Size of the sub-buffer in elements |
| const Func *f; // A pointer to the function info |
| cl_program programs[ VECTOR_SIZE_COUNT ]; // programs for various vector sizes |
| cl_kernel *k[VECTOR_SIZE_COUNT ]; // arrays of thread-specific kernels for each worker thread: k[vector_size][thread_id] |
| ThreadInfo *tinfo; // An array of thread specific information for each worker thread |
| cl_uint threadCount; // Number of worker threads |
| cl_uint jobCount; // Number of jobs |
| cl_uint step; // step between each chunk and the next. |
| cl_uint scale; // stride between individual test values |
| float ulps; // max_allowed ulps |
| int ftz; // non-zero if running in flush to zero mode |
| |
| int isRangeLimited; // 1 if the function is only to be evaluated over a range |
| float half_sin_cos_tan_limit; |
| bool relaxedMode; // True if test is to be run in relaxed mode, false |
| // otherwise. |
| }TestInfo; |
| |
| static cl_int TestFloat( cl_uint job_id, cl_uint thread_id, void *p ); |
| |
| int TestFunc_Float_Float(const Func *f, MTdata d, bool relaxedMode) |
| { |
| TestInfo test_info; |
| cl_int error; |
| size_t i, j; |
| float maxError = 0.0f; |
| double maxErrorVal = 0.0; |
| int skipTestingRelaxed = (relaxedMode && strcmp(f->name, "tan") == 0); |
| |
| logFunctionInfo(f->name, sizeof(cl_float), relaxedMode); |
| |
| // Init test_info |
| memset( &test_info, 0, sizeof( test_info ) ); |
| test_info.threadCount = GetThreadCount(); |
| |
| test_info.subBufferSize = BUFFER_SIZE / (sizeof( cl_float) * RoundUpToNextPowerOfTwo(test_info.threadCount)); |
| test_info.scale = getTestScale(sizeof(cl_float)); |
| if (gWimpyMode) |
| { |
| test_info.subBufferSize = gWimpyBufferSize / (sizeof( cl_float) * RoundUpToNextPowerOfTwo(test_info.threadCount)); |
| } |
| |
| test_info.step = (cl_uint) test_info.subBufferSize * test_info.scale; |
| if (test_info.step / test_info.subBufferSize != test_info.scale) |
| { |
| //there was overflow |
| test_info.jobCount = 1; |
| } |
| else |
| { |
| test_info.jobCount = (cl_uint)((1ULL << 32) / test_info.step); |
| } |
| |
| test_info.f = f; |
| test_info.ulps = gIsEmbedded ? f->float_embedded_ulps : f->float_ulps; |
| test_info.ftz = f->ftz || gForceFTZ || 0 == (CL_FP_DENORM & gFloatCapabilities); |
| test_info.relaxedMode = relaxedMode; |
| // cl_kernels aren't thread safe, so we make one for each vector size for every thread |
| for( i = gMinVectorSizeIndex; i < gMaxVectorSizeIndex; i++ ) |
| { |
| size_t array_size = test_info.threadCount * sizeof( cl_kernel ); |
| test_info.k[i] = (cl_kernel*)malloc( array_size ); |
| if( NULL == test_info.k[i] ) |
| { |
| vlog_error( "Error: Unable to allocate storage for kernels!\n" ); |
| error = CL_OUT_OF_HOST_MEMORY; |
| goto exit; |
| } |
| memset( test_info.k[i], 0, array_size ); |
| } |
| test_info.tinfo = (ThreadInfo*)malloc( test_info.threadCount * sizeof(*test_info.tinfo) ); |
| if( NULL == test_info.tinfo ) |
| { |
| vlog_error( "Error: Unable to allocate storage for thread specific data.\n" ); |
| error = CL_OUT_OF_HOST_MEMORY; |
| goto exit; |
| } |
| memset( test_info.tinfo, 0, test_info.threadCount * sizeof(*test_info.tinfo) ); |
| for( i = 0; i < test_info.threadCount; i++ ) |
| { |
| cl_buffer_region region = { i * test_info.subBufferSize * sizeof( cl_float), test_info.subBufferSize * sizeof( cl_float) }; |
| test_info.tinfo[i].inBuf = clCreateSubBuffer( gInBuffer, CL_MEM_READ_ONLY, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &error); |
| if( error || NULL == test_info.tinfo[i].inBuf) |
| { |
| vlog_error( "Error: Unable to create sub-buffer of gInBuffer for region {%zd, %zd}\n", region.origin, region.size ); |
| goto exit; |
| } |
| |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| { |
| test_info.tinfo[i].outBuf[j] = clCreateSubBuffer( gOutBuffer[j], CL_MEM_WRITE_ONLY, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &error); |
| if( error || NULL == test_info.tinfo[i].outBuf[j] ) |
| { |
| vlog_error( "Error: Unable to create sub-buffer of gInBuffer for region {%zd, %zd}\n", region.origin, region.size ); |
| goto exit; |
| } |
| } |
| test_info.tinfo[i].tQueue = clCreateCommandQueue(gContext, gDevice, 0, &error); |
| if( NULL == test_info.tinfo[i].tQueue || error ) |
| { |
| vlog_error( "clCreateCommandQueue failed. (%d)\n", error ); |
| goto exit; |
| } |
| |
| } |
| |
| // Check for special cases for unary float |
| test_info.isRangeLimited = 0; |
| test_info.half_sin_cos_tan_limit = 0; |
| if( 0 == strcmp( f->name, "half_sin") || 0 == strcmp( f->name, "half_cos") ) |
| { |
| test_info.isRangeLimited = 1; |
| test_info.half_sin_cos_tan_limit = 1.0f + test_info.ulps * (FLT_EPSILON/2.0f); // out of range results from finite inputs must be in [-1,1] |
| } |
| else if( 0 == strcmp( f->name, "half_tan")) |
| { |
| test_info.isRangeLimited = 1; |
| test_info.half_sin_cos_tan_limit = INFINITY; // out of range resut from finite inputs must be numeric |
| } |
| |
| // Init the kernels |
| { |
| BuildKernelInfo build_info = { |
| gMinVectorSizeIndex, test_info.threadCount, test_info.k, |
| test_info.programs, f->nameInCode, relaxedMode |
| }; |
| if( (error = ThreadPool_Do( BuildKernel_FloatFn, gMaxVectorSizeIndex - gMinVectorSizeIndex, &build_info ) )) |
| goto exit; |
| } |
| |
| if( !gSkipCorrectnessTesting || skipTestingRelaxed) |
| { |
| error = ThreadPool_Do( TestFloat, test_info.jobCount, &test_info ); |
| |
| // Accumulate the arithmetic errors |
| for( i = 0; i < test_info.threadCount; i++ ) |
| { |
| if( test_info.tinfo[i].maxError > maxError ) |
| { |
| maxError = test_info.tinfo[i].maxError; |
| maxErrorVal = test_info.tinfo[i].maxErrorValue; |
| } |
| } |
| |
| if( error ) |
| goto exit; |
| |
| if( gWimpyMode ) |
| vlog( "Wimp pass" ); |
| else |
| vlog( "passed" ); |
| |
| if( skipTestingRelaxed ) |
| { |
| vlog(" (rlx skip correctness testing)\n"); |
| goto exit; |
| } |
| } |
| |
| if( gMeasureTimes ) |
| { |
| //Init input array |
| uint32_t *p = (uint32_t *)gIn; |
| if( strstr( f->name, "exp" ) || strstr( f->name, "sin" ) || strstr( f->name, "cos" ) || strstr( f->name, "tan" ) ) |
| for( j = 0; j < BUFFER_SIZE / sizeof( float ); j++ ) |
| ((float*)p)[j] = (float) genrand_real1(d); |
| else if( strstr( f->name, "log" ) ) |
| for( j = 0; j < BUFFER_SIZE / sizeof( float ); j++ ) |
| p[j] = genrand_int32(d) & 0x7fffffff; |
| else |
| for( j = 0; j < BUFFER_SIZE / sizeof( float ); j++ ) |
| p[j] = genrand_int32(d); |
| if( (error = clEnqueueWriteBuffer(gQueue, gInBuffer, CL_FALSE, 0, BUFFER_SIZE, gIn, 0, NULL, NULL) )) |
| { |
| vlog_error( "\n*** Error %d in clEnqueueWriteBuffer ***\n", error ); |
| return error; |
| } |
| |
| |
| // Run the kernels |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| { |
| size_t vectorSize = sizeValues[j] * sizeof(cl_float); |
| size_t localCount = (BUFFER_SIZE + vectorSize - 1) / vectorSize; |
| if( ( error = clSetKernelArg( test_info.k[j][0], 0, sizeof( gOutBuffer[j] ), &gOutBuffer[j] ) )) { LogBuildError( test_info.programs[j]); goto exit; } |
| if( ( error = clSetKernelArg( test_info.k[j][0], 1, sizeof( gInBuffer ), &gInBuffer ) )) { LogBuildError(test_info.programs[j]); goto exit; } |
| |
| double sum = 0.0; |
| double bestTime = INFINITY; |
| for( i = 0; i < PERF_LOOP_COUNT; i++ ) |
| { |
| uint64_t startTime = GetTime(); |
| if( (error = clEnqueueNDRangeKernel(gQueue, test_info.k[j][0], 1, NULL, &localCount, NULL, 0, NULL, NULL)) ) |
| { |
| vlog_error( "FAILED -- could not execute kernel\n" ); |
| goto exit; |
| } |
| |
| // Make sure OpenCL is done |
| if( (error = clFinish(gQueue) ) ) |
| { |
| vlog_error( "Error %d at clFinish\n", error ); |
| goto exit; |
| } |
| |
| uint64_t endTime = GetTime(); |
| double current_time = SubtractTime( endTime, startTime ); |
| sum += current_time; |
| if( current_time < bestTime ) |
| bestTime = current_time; |
| } |
| |
| if( gReportAverageTimes ) |
| bestTime = sum / PERF_LOOP_COUNT; |
| double clocksPerOp = bestTime * (double) gDeviceFrequency * gComputeDevices * gSimdSize * 1e6 / (BUFFER_SIZE / sizeof( float ) ); |
| vlog_perf( clocksPerOp, LOWER_IS_BETTER, "clocks / element", "%sf%s", f->name, sizeNames[j] ); |
| } |
| } |
| |
| if( ! gSkipCorrectnessTesting ) |
| vlog( "\t%8.2f @ %a", maxError, maxErrorVal ); |
| vlog( "\n" ); |
| |
| exit: |
| for( i = gMinVectorSizeIndex; i < gMaxVectorSizeIndex; i++ ) |
| { |
| clReleaseProgram(test_info.programs[i]); |
| if( test_info.k[i] ) |
| { |
| for( j = 0; j < test_info.threadCount; j++ ) |
| clReleaseKernel(test_info.k[i][j]); |
| |
| free( test_info.k[i] ); |
| } |
| } |
| if( test_info.tinfo ) |
| { |
| for( i = 0; i < test_info.threadCount; i++ ) |
| { |
| clReleaseMemObject(test_info.tinfo[i].inBuf); |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| clReleaseMemObject(test_info.tinfo[i].outBuf[j]); |
| clReleaseCommandQueue(test_info.tinfo[i].tQueue); |
| } |
| |
| free( test_info.tinfo ); |
| } |
| |
| return error; |
| } |
| |
| static cl_int TestFloat( cl_uint job_id, cl_uint thread_id, void *data ) |
| { |
| const TestInfo *job = (const TestInfo *) data; |
| size_t buffer_elements = job->subBufferSize; |
| size_t buffer_size = buffer_elements * sizeof( cl_float ); |
| cl_uint scale = job->scale; |
| cl_uint base = job_id * (cl_uint) job->step; |
| ThreadInfo *tinfo = job->tinfo + thread_id; |
| fptr func = job->f->func; |
| const char * fname = job->f->name; |
| bool relaxedMode = job->relaxedMode; |
| float ulps = getAllowedUlpError(job->f, relaxedMode); |
| if (relaxedMode) |
| { |
| func = job->f->rfunc; |
| } |
| |
| cl_uint j, k; |
| cl_int error; |
| |
| int isRangeLimited = job->isRangeLimited; |
| float half_sin_cos_tan_limit = job->half_sin_cos_tan_limit; |
| int ftz = job->ftz; |
| |
| // start the map of the output arrays |
| cl_event e[ VECTOR_SIZE_COUNT ]; |
| cl_uint *out[ VECTOR_SIZE_COUNT ]; |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| { |
| out[j] = (uint32_t*) clEnqueueMapBuffer( tinfo->tQueue, tinfo->outBuf[j], CL_FALSE, CL_MAP_WRITE, 0, buffer_size, 0, NULL, e + j, &error); |
| if( error || NULL == out[j]) |
| { |
| vlog_error( "Error: clEnqueueMapBuffer %d failed! err: %d\n", j, error ); |
| return error; |
| } |
| } |
| |
| // Get that moving |
| if( (error = clFlush(tinfo->tQueue) )) |
| vlog( "clFlush failed\n" ); |
| |
| // Write the new values to the input array |
| cl_uint *p = (cl_uint*) gIn + thread_id * buffer_elements; |
| for( j = 0; j < buffer_elements; j++ ) |
| { |
| p[j] = base + j * scale; |
| if (relaxedMode) |
| { |
| float p_j = *(float *) &p[j]; |
| if ( strcmp(fname,"sin")==0 || strcmp(fname,"cos")==0 ) //the domain of the function is [-pi,pi] |
| { |
| if (fabs(p_j) > M_PI) ((float *)p)[j] = NAN; |
| } |
| |
| if ( strcmp( fname, "reciprocal" ) == 0 ) |
| { |
| const float l_limit = HEX_FLT(+, 1, 0, -, 126); |
| const float u_limit = HEX_FLT(+, 1, 0, +, 126); |
| |
| if (fabs(p_j) < l_limit |
| || fabs(p_j) |
| > u_limit) // the domain of the function is [2^-126,2^126] |
| ((float *)p)[j] = NAN; |
| } |
| } |
| } |
| |
| if( (error = clEnqueueWriteBuffer( tinfo->tQueue, tinfo->inBuf, CL_FALSE, 0, buffer_size, p, 0, NULL, NULL) )) |
| { |
| vlog_error( "Error: clEnqueueWriteBuffer failed! err: %d\n", error ); |
| return error; |
| } |
| |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| { |
| //Wait for the map to finish |
| if( (error = clWaitForEvents(1, e + j) )) |
| { |
| vlog_error( "Error: clWaitForEvents failed! err: %d\n", error ); |
| return error; |
| } |
| if( (error = clReleaseEvent( e[j] ) )) |
| { |
| vlog_error( "Error: clReleaseEvent failed! err: %d\n", error ); |
| return error; |
| } |
| |
| // Fill the result buffer with garbage, so that old results don't carry over |
| uint32_t pattern = 0xffffdead; |
| memset_pattern4(out[j], &pattern, buffer_size); |
| if( (error = clEnqueueUnmapMemObject( tinfo->tQueue, tinfo->outBuf[j], out[j], 0, NULL, NULL) )) |
| { |
| vlog_error( "Error: clEnqueueMapBuffer failed! err: %d\n", error ); |
| return error; |
| } |
| |
| // run the kernel |
| size_t vectorCount = (buffer_elements + sizeValues[j] - 1) / sizeValues[j]; |
| cl_kernel kernel = job->k[j][thread_id]; //each worker thread has its own copy of the cl_kernel |
| cl_program program = job->programs[j]; |
| |
| if( ( error = clSetKernelArg( kernel, 0, sizeof( tinfo->outBuf[j] ), &tinfo->outBuf[j] ))){ LogBuildError(program); return error; } |
| if( ( error = clSetKernelArg( kernel, 1, sizeof( tinfo->inBuf ), &tinfo->inBuf ) )) { LogBuildError(program); return error; } |
| |
| if( (error = clEnqueueNDRangeKernel(tinfo->tQueue, kernel, 1, NULL, &vectorCount, NULL, 0, NULL, NULL))) |
| { |
| vlog_error( "FAILED -- could not execute kernel\n" ); |
| return error; |
| } |
| } |
| |
| |
| // Get that moving |
| if( (error = clFlush(tinfo->tQueue) )) |
| vlog( "clFlush 2 failed\n" ); |
| |
| if( gSkipCorrectnessTesting ) |
| return CL_SUCCESS; |
| |
| //Calculate the correctly rounded reference result |
| float *r = (float *)gOut_Ref + thread_id * buffer_elements; |
| float *s = (float *)p; |
| for( j = 0; j < buffer_elements; j++ ) |
| r[j] = (float) func.f_f( s[j] ); |
| |
| // Read the data back -- no need to wait for the first N-1 buffers. This is an in order queue. |
| for( j = gMinVectorSizeIndex; j + 1 < gMaxVectorSizeIndex; j++ ) |
| { |
| out[j] = (uint32_t*) clEnqueueMapBuffer( tinfo->tQueue, tinfo->outBuf[j], CL_FALSE, CL_MAP_READ, 0, buffer_size, 0, NULL, NULL, &error); |
| if( error || NULL == out[j] ) |
| { |
| vlog_error( "Error: clEnqueueMapBuffer %d failed! err: %d\n", j, error ); |
| return error; |
| } |
| } |
| // Wait for the last buffer |
| out[j] = (uint32_t*) clEnqueueMapBuffer( tinfo->tQueue, tinfo->outBuf[j], CL_TRUE, CL_MAP_READ, 0, buffer_size, 0, NULL, NULL, &error); |
| if( error || NULL == out[j] ) |
| { |
| vlog_error( "Error: clEnqueueMapBuffer %d failed! err: %d\n", j, error ); |
| return error; |
| } |
| |
| //Verify data |
| uint32_t *t = (uint32_t *)r; |
| for( j = 0; j < buffer_elements; j++ ) |
| { |
| for( k = gMinVectorSizeIndex; k < gMaxVectorSizeIndex; k++ ) |
| { |
| uint32_t *q = out[k]; |
| |
| // If we aren't getting the correctly rounded result |
| if( t[j] != q[j] ) |
| { |
| float test = ((float*) q)[j]; |
| double correct = func.f_f( s[j] ); |
| float err = Ulp_Error( test, correct ); |
| float abs_error = Abs_Error( test, correct ); |
| int fail = 0; |
| int use_abs_error = 0; |
| |
| // it is possible for the output to not match the reference result but for Ulp_Error |
| // to be zero, for example -1.#QNAN vs. 1.#QNAN. In such cases there is no failure |
| if (err == 0.0f) |
| { |
| fail = 0; |
| } |
| else if (relaxedMode) |
| { |
| if ( strcmp(fname,"sin")==0 || strcmp(fname,"cos")==0 ) |
| { |
| fail = ! (fabsf(abs_error) <= ulps); |
| use_abs_error = 1; |
| } |
| if (strcmp(fname, "sinpi") == 0 |
| || strcmp(fname, "cospi") == 0) |
| { |
| if (s[j] >= -1.0 && s[j] <= 1.0) |
| { |
| fail = !(fabsf(abs_error) <= ulps); |
| use_abs_error = 1; |
| } |
| } |
| |
| if ( strcmp(fname, "reciprocal") == 0 ) |
| { |
| fail = ! (fabsf(err) <= ulps); |
| } |
| |
| if ( strcmp(fname, "exp") == 0 || strcmp(fname, "exp2") == 0 ) |
| { |
| float exp_error = ulps; |
| |
| if (!gIsEmbedded) |
| { |
| exp_error += floor(fabs(2 * s[j])); |
| } |
| |
| fail = ! (fabsf(err) <= exp_error); |
| ulps = exp_error; |
| } |
| if (strcmp(fname, "tan") == 0) { |
| |
| if( !gFastRelaxedDerived ) |
| { |
| fail = ! (fabsf(err) <= ulps); |
| } |
| // Else fast math derived implementation does not require ULP verification |
| } |
| if (strcmp(fname, "exp10") == 0) |
| { |
| if( !gFastRelaxedDerived ) |
| { |
| fail = ! (fabsf(err) <= ulps); |
| } |
| // Else fast math derived implementation does not require ULP verification |
| } |
| if (strcmp(fname, "log") == 0 || strcmp(fname, "log2") == 0 |
| || strcmp(fname, "log10") == 0) |
| { |
| if( s[j] >= 0.5 && s[j] <= 2 ) |
| { |
| fail = ! (fabsf(abs_error) <= ulps ); |
| } |
| else |
| { |
| ulps = gIsEmbedded ? job->f->float_embedded_ulps : job->f->float_ulps; |
| fail = ! (fabsf(err) <= ulps); |
| } |
| } |
| |
| |
| // fast-relaxed implies finite-only |
| if( IsFloatInfinity(correct) || IsFloatNaN(correct) || |
| IsFloatInfinity(s[j]) || IsFloatNaN(s[j]) ) { |
| fail = 0; |
| err = 0; |
| } |
| } |
| else |
| { |
| fail = ! (fabsf(err) <= ulps); |
| } |
| |
| // half_sin/cos/tan are only valid between +-2**16, Inf, NaN |
| if( isRangeLimited && fabsf(s[j]) > MAKE_HEX_FLOAT(0x1.0p16f, 0x1L, 16) && fabsf(s[j]) < INFINITY ) |
| { |
| if( fabsf( test ) <= half_sin_cos_tan_limit ) |
| { |
| err = 0; |
| fail = 0; |
| } |
| } |
| |
| if( fail ) |
| { |
| if( ftz ) |
| { |
| typedef int (*CheckForSubnormal) (double,float); // If we are in fast relaxed math, we have a different calculation for the subnormal threshold. |
| CheckForSubnormal isFloatResultSubnormalPtr; |
| |
| if (relaxedMode) |
| { |
| isFloatResultSubnormalPtr = &IsFloatResultSubnormalAbsError; |
| } |
| else |
| { |
| isFloatResultSubnormalPtr = &IsFloatResultSubnormal; |
| } |
| // retry per section 6.5.3.2 |
| if( (*isFloatResultSubnormalPtr)(correct, ulps) ) |
| { |
| fail = fail && ( test != 0.0f ); |
| if( ! fail ) |
| err = 0.0f; |
| } |
| |
| // retry per section 6.5.3.3 |
| if( IsFloatSubnormal( s[j] ) ) |
| { |
| double correct2 = func.f_f( 0.0 ); |
| double correct3 = func.f_f( -0.0 ); |
| float err2; |
| float err3; |
| if( use_abs_error ) |
| { |
| err2 = Abs_Error( test, correct2 ); |
| err3 = Abs_Error( test, correct3 ); |
| } |
| else |
| { |
| err2 = Ulp_Error( test, correct2 ); |
| err3 = Ulp_Error( test, correct3 ); |
| } |
| fail = fail && ((!(fabsf(err2) <= ulps)) && (!(fabsf(err3) <= ulps))); |
| if( fabsf( err2 ) < fabsf(err ) ) |
| err = err2; |
| if( fabsf( err3 ) < fabsf(err ) ) |
| err = err3; |
| |
| // retry per section 6.5.3.4 |
| if( (*isFloatResultSubnormalPtr)(correct2, ulps ) || (*isFloatResultSubnormalPtr)(correct3, ulps ) ) |
| { |
| fail = fail && ( test != 0.0f); |
| if( ! fail ) |
| err = 0.0f; |
| } |
| } |
| } |
| } |
| if( fabsf(err ) > tinfo->maxError ) |
| { |
| tinfo->maxError = fabsf(err); |
| tinfo->maxErrorValue = s[j]; |
| } |
| if( fail ) |
| { |
| vlog_error( "\nERROR: %s%s: %f ulp error at %a (0x%8.8x): *%a vs. %a\n", job->f->name, sizeNames[k], err, ((float*) s)[j], ((uint32_t*) s)[j], ((float*) t)[j], test); |
| return -1; |
| } |
| } |
| } |
| } |
| |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| { |
| if( (error = clEnqueueUnmapMemObject( tinfo->tQueue, tinfo->outBuf[j], out[j], 0, NULL, NULL)) ) |
| { |
| vlog_error( "Error: clEnqueueUnmapMemObject %d failed 2! err: %d\n", j, error ); |
| return error; |
| } |
| } |
| |
| if( (error = clFlush(tinfo->tQueue) )) |
| vlog( "clFlush 3 failed\n" ); |
| |
| |
| if( 0 == ( base & 0x0fffffff) ) |
| { |
| if (gVerboseBruteForce) |
| { |
| vlog("base:%14u step:%10u scale:%10u buf_elements:%10zd ulps:%5.3f ThreadCount:%2u\n", base, job->step, job->scale, buffer_elements, job->ulps, job->threadCount); |
| } else |
| { |
| vlog("." ); |
| } |
| fflush(stdout); |
| } |
| |
| return CL_SUCCESS; |
| } |
| |
| |
| |
| static cl_int TestDouble( cl_uint job_id, cl_uint thread_id, void *data ) |
| { |
| const TestInfo *job = (const TestInfo *) data; |
| size_t buffer_elements = job->subBufferSize; |
| size_t buffer_size = buffer_elements * sizeof( cl_double ); |
| cl_uint scale = job->scale; |
| cl_uint base = job_id * (cl_uint) job->step; |
| ThreadInfo *tinfo = job->tinfo + thread_id; |
| float ulps = job->ulps; |
| dptr func = job->f->dfunc; |
| cl_uint j, k; |
| cl_int error; |
| int ftz = job->ftz; |
| |
| Force64BitFPUPrecision(); |
| |
| // start the map of the output arrays |
| cl_event e[ VECTOR_SIZE_COUNT ]; |
| cl_ulong *out[ VECTOR_SIZE_COUNT ]; |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| { |
| out[j] = (cl_ulong*) clEnqueueMapBuffer( tinfo->tQueue, tinfo->outBuf[j], CL_FALSE, CL_MAP_WRITE, 0, buffer_size, 0, NULL, e + j, &error); |
| if( error || NULL == out[j]) |
| { |
| vlog_error( "Error: clEnqueueMapBuffer %d failed! err: %d\n", j, error ); |
| return error; |
| } |
| } |
| |
| // Get that moving |
| if( (error = clFlush(tinfo->tQueue) )) |
| vlog( "clFlush failed\n" ); |
| |
| // Write the new values to the input array |
| cl_double *p = (cl_double*) gIn + thread_id * buffer_elements; |
| for( j = 0; j < buffer_elements; j++ ) |
| p[j] = DoubleFromUInt32( base + j * scale); |
| |
| if( (error = clEnqueueWriteBuffer( tinfo->tQueue, tinfo->inBuf, CL_FALSE, 0, buffer_size, p, 0, NULL, NULL) )) |
| { |
| vlog_error( "Error: clEnqueueWriteBuffer failed! err: %d\n", error ); |
| return error; |
| } |
| |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| { |
| //Wait for the map to finish |
| if( (error = clWaitForEvents(1, e + j) )) |
| { |
| vlog_error( "Error: clWaitForEvents failed! err: %d\n", error ); |
| return error; |
| } |
| if( (error = clReleaseEvent( e[j] ) )) |
| { |
| vlog_error( "Error: clReleaseEvent failed! err: %d\n", error ); |
| return error; |
| } |
| |
| // Fill the result buffer with garbage, so that old results don't carry over |
| uint32_t pattern = 0xffffdead; |
| memset_pattern4(out[j], &pattern, buffer_size); |
| if( (error = clEnqueueUnmapMemObject( tinfo->tQueue, tinfo->outBuf[j], out[j], 0, NULL, NULL) )) |
| { |
| vlog_error( "Error: clEnqueueMapBuffer failed! err: %d\n", error ); |
| return error; |
| } |
| |
| // run the kernel |
| size_t vectorCount = (buffer_elements + sizeValues[j] - 1) / sizeValues[j]; |
| cl_kernel kernel = job->k[j][thread_id]; //each worker thread has its own copy of the cl_kernel |
| cl_program program = job->programs[j]; |
| |
| if( ( error = clSetKernelArg( kernel, 0, sizeof( tinfo->outBuf[j] ), &tinfo->outBuf[j] ))){ LogBuildError(program); return error; } |
| if( ( error = clSetKernelArg( kernel, 1, sizeof( tinfo->inBuf ), &tinfo->inBuf ) )) { LogBuildError(program); return error; } |
| |
| if( (error = clEnqueueNDRangeKernel(tinfo->tQueue, kernel, 1, NULL, &vectorCount, NULL, 0, NULL, NULL))) |
| { |
| vlog_error( "FAILED -- could not execute kernel\n" ); |
| return error; |
| } |
| } |
| |
| |
| // Get that moving |
| if( (error = clFlush(tinfo->tQueue) )) |
| vlog( "clFlush 2 failed\n" ); |
| |
| if( gSkipCorrectnessTesting ) |
| return CL_SUCCESS; |
| |
| //Calculate the correctly rounded reference result |
| cl_double *r = (cl_double *)gOut_Ref + thread_id * buffer_elements; |
| cl_double *s = (cl_double *)p; |
| for( j = 0; j < buffer_elements; j++ ) |
| r[j] = (cl_double) func.f_f( s[j] ); |
| |
| // Read the data back -- no need to wait for the first N-1 buffers. This is an in order queue. |
| for( j = gMinVectorSizeIndex; j + 1 < gMaxVectorSizeIndex; j++ ) |
| { |
| out[j] = (cl_ulong*) clEnqueueMapBuffer( tinfo->tQueue, tinfo->outBuf[j], CL_FALSE, CL_MAP_READ, 0, buffer_size, 0, NULL, NULL, &error); |
| if( error || NULL == out[j] ) |
| { |
| vlog_error( "Error: clEnqueueMapBuffer %d failed! err: %d\n", j, error ); |
| return error; |
| } |
| } |
| // Wait for the last buffer |
| out[j] = (cl_ulong*) clEnqueueMapBuffer( tinfo->tQueue, tinfo->outBuf[j], CL_TRUE, CL_MAP_READ, 0, buffer_size, 0, NULL, NULL, &error); |
| if( error || NULL == out[j] ) |
| { |
| vlog_error( "Error: clEnqueueMapBuffer %d failed! err: %d\n", j, error ); |
| return error; |
| } |
| |
| |
| //Verify data |
| cl_ulong *t = (cl_ulong *)r; |
| for( j = 0; j < buffer_elements; j++ ) |
| { |
| for( k = gMinVectorSizeIndex; k < gMaxVectorSizeIndex; k++ ) |
| { |
| cl_ulong *q = out[k]; |
| |
| // If we aren't getting the correctly rounded result |
| if( t[j] != q[j] ) |
| { |
| cl_double test = ((cl_double*) q)[j]; |
| long double correct = func.f_f( s[j] ); |
| float err = Bruteforce_Ulp_Error_Double( test, correct ); |
| int fail = ! (fabsf(err) <= ulps); |
| |
| if( fail ) |
| { |
| if( ftz ) |
| { |
| // retry per section 6.5.3.2 |
| if( IsDoubleResultSubnormal(correct, ulps) ) |
| { |
| fail = fail && ( test != 0.0f ); |
| if( ! fail ) |
| err = 0.0f; |
| } |
| |
| // retry per section 6.5.3.3 |
| if( IsDoubleSubnormal( s[j] ) ) |
| { |
| long double correct2 = func.f_f( 0.0L ); |
| long double correct3 = func.f_f( -0.0L ); |
| float err2 = Bruteforce_Ulp_Error_Double( test, correct2 ); |
| float err3 = Bruteforce_Ulp_Error_Double( test, correct3 ); |
| fail = fail && ((!(fabsf(err2) <= ulps)) && (!(fabsf(err3) <= ulps))); |
| if( fabsf( err2 ) < fabsf(err ) ) |
| err = err2; |
| if( fabsf( err3 ) < fabsf(err ) ) |
| err = err3; |
| |
| // retry per section 6.5.3.4 |
| if( IsDoubleResultSubnormal(correct2, ulps ) || IsDoubleResultSubnormal(correct3, ulps ) ) |
| { |
| fail = fail && ( test != 0.0f); |
| if( ! fail ) |
| err = 0.0f; |
| } |
| } |
| } |
| } |
| if( fabsf(err ) > tinfo->maxError ) |
| { |
| tinfo->maxError = fabsf(err); |
| tinfo->maxErrorValue = s[j]; |
| } |
| if( fail ) |
| { |
| vlog_error( "\nERROR: %s%s: %f ulp error at %.13la (0x%16.16llx): *%.13la vs. %.13la\n", job->f->name, sizeNames[k], err, ((cl_double*) gIn)[j], ((cl_ulong*) gIn)[j], ((cl_double*) gOut_Ref)[j], test ); |
| return -1; |
| } |
| } |
| } |
| } |
| |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| { |
| if( (error = clEnqueueUnmapMemObject( tinfo->tQueue, tinfo->outBuf[j], out[j], 0, NULL, NULL)) ) |
| { |
| vlog_error( "Error: clEnqueueUnmapMemObject %d failed 2! err: %d\n", j, error ); |
| return error; |
| } |
| } |
| |
| if( (error = clFlush(tinfo->tQueue) )) |
| vlog( "clFlush 3 failed\n" ); |
| |
| |
| if( 0 == ( base & 0x0fffffff) ) |
| { |
| if (gVerboseBruteForce) |
| { |
| vlog("base:%14u step:%10u scale:%10zd buf_elements:%10u ulps:%5.3f ThreadCount:%2u\n", base, job->step, buffer_elements, job->scale, job->ulps, job->threadCount); |
| } else |
| { |
| vlog("." ); |
| } |
| fflush(stdout); |
| } |
| |
| return CL_SUCCESS; |
| } |
| |
| int TestFunc_Double_Double(const Func *f, MTdata d, bool relaxedMode) |
| { |
| TestInfo test_info; |
| cl_int error; |
| size_t i, j; |
| float maxError = 0.0f; |
| double maxErrorVal = 0.0; |
| #if defined( __APPLE__ ) |
| struct timeval time_val; |
| gettimeofday( &time_val, NULL ); |
| double start_time = time_val.tv_sec + 1e-6 * time_val.tv_usec; |
| double end_time; |
| #endif |
| |
| logFunctionInfo(f->name, sizeof(cl_double), relaxedMode); |
| // Init test_info |
| memset( &test_info, 0, sizeof( test_info ) ); |
| test_info.threadCount = GetThreadCount(); |
| test_info.subBufferSize = BUFFER_SIZE / (sizeof( cl_double) * RoundUpToNextPowerOfTwo(test_info.threadCount)); |
| test_info.scale = getTestScale(sizeof(cl_double)); |
| if (gWimpyMode) |
| { |
| test_info.subBufferSize = gWimpyBufferSize / (sizeof( cl_double) * RoundUpToNextPowerOfTwo(test_info.threadCount)); |
| } |
| |
| test_info.step = (cl_uint) test_info.subBufferSize * test_info.scale; |
| if (test_info.step / test_info.subBufferSize != test_info.scale) |
| { |
| //there was overflow |
| test_info.jobCount = 1; |
| } |
| else |
| { |
| test_info.jobCount = (cl_uint)((1ULL << 32) / test_info.step); |
| } |
| |
| test_info.f = f; |
| test_info.ulps = f->double_ulps; |
| test_info.ftz = f->ftz || gForceFTZ; |
| test_info.relaxedMode = relaxedMode; |
| |
| // cl_kernels aren't thread safe, so we make one for each vector size for every thread |
| for( i = gMinVectorSizeIndex; i < gMaxVectorSizeIndex; i++ ) |
| { |
| size_t array_size = test_info.threadCount * sizeof( cl_kernel ); |
| test_info.k[i] = (cl_kernel*)malloc( array_size ); |
| if( NULL == test_info.k[i] ) |
| { |
| vlog_error( "Error: Unable to allocate storage for kernels!\n" ); |
| error = CL_OUT_OF_HOST_MEMORY; |
| goto exit; |
| } |
| memset( test_info.k[i], 0, array_size ); |
| } |
| test_info.tinfo = (ThreadInfo*)malloc( test_info.threadCount * sizeof(*test_info.tinfo) ); |
| if( NULL == test_info.tinfo ) |
| { |
| vlog_error( "Error: Unable to allocate storage for thread specific data.\n" ); |
| error = CL_OUT_OF_HOST_MEMORY; |
| goto exit; |
| } |
| memset( test_info.tinfo, 0, test_info.threadCount * sizeof(*test_info.tinfo) ); |
| for( i = 0; i < test_info.threadCount; i++ ) |
| { |
| cl_buffer_region region = { i * test_info.subBufferSize * sizeof( cl_double), test_info.subBufferSize * sizeof( cl_double) }; |
| test_info.tinfo[i].inBuf = clCreateSubBuffer( gInBuffer, CL_MEM_READ_ONLY, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &error); |
| if( error || NULL == test_info.tinfo[i].inBuf) |
| { |
| vlog_error( "Error: Unable to create sub-buffer of gInBuffer for region {%zd, %zd}\n", region.origin, region.size ); |
| goto exit; |
| } |
| |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| { |
| /* Qualcomm fix: 9461 read-write flags must be compatible with parent buffer */ |
| test_info.tinfo[i].outBuf[j] = clCreateSubBuffer( gOutBuffer[j], CL_MEM_WRITE_ONLY, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &error); |
| /* Qualcomm fix: end */ |
| if( error || NULL == test_info.tinfo[i].outBuf[j] ) |
| { |
| vlog_error( "Error: Unable to create sub-buffer of gInBuffer for region {%zd, %zd}\n", region.origin, region.size ); |
| goto exit; |
| } |
| } |
| test_info.tinfo[i].tQueue = clCreateCommandQueue(gContext, gDevice, 0, &error); |
| if( NULL == test_info.tinfo[i].tQueue || error ) |
| { |
| vlog_error( "clCreateCommandQueue failed. (%d)\n", error ); |
| goto exit; |
| } |
| } |
| |
| // Init the kernels |
| { |
| BuildKernelInfo build_info = { |
| gMinVectorSizeIndex, test_info.threadCount, test_info.k, |
| test_info.programs, f->nameInCode, relaxedMode |
| }; |
| if( (error = ThreadPool_Do( BuildKernel_DoubleFn, gMaxVectorSizeIndex - gMinVectorSizeIndex, &build_info ) )) |
| goto exit; |
| } |
| |
| if( !gSkipCorrectnessTesting ) |
| { |
| error = ThreadPool_Do( TestDouble, test_info.jobCount, &test_info ); |
| |
| // Accumulate the arithmetic errors |
| for( i = 0; i < test_info.threadCount; i++ ) |
| { |
| if( test_info.tinfo[i].maxError > maxError ) |
| { |
| maxError = test_info.tinfo[i].maxError; |
| maxErrorVal = test_info.tinfo[i].maxErrorValue; |
| } |
| } |
| |
| if( error ) |
| goto exit; |
| |
| if( gWimpyMode ) |
| vlog( "Wimp pass" ); |
| else |
| vlog( "passed" ); |
| } |
| |
| |
| #if defined( __APPLE__ ) |
| gettimeofday( &time_val, NULL); |
| end_time = time_val.tv_sec + 1e-6 * time_val.tv_usec; |
| #endif |
| |
| if( gMeasureTimes ) |
| { |
| //Init input array |
| double *p = (double *)gIn; |
| |
| if( strstr( f->name, "exp" ) ) |
| for( j = 0; j < BUFFER_SIZE / sizeof( double ); j++ ) |
| p[j] = (double)genrand_real1(d); |
| else if( strstr( f->name, "log" ) ) |
| for( j = 0; j < BUFFER_SIZE / sizeof( double ); j++ ) |
| p[j] = fabs(DoubleFromUInt32( genrand_int32(d))); |
| else |
| for( j = 0; j < BUFFER_SIZE / sizeof( double ); j++ ) |
| p[j] = DoubleFromUInt32( genrand_int32(d) ); |
| if( (error = clEnqueueWriteBuffer(gQueue, gInBuffer, CL_FALSE, 0, BUFFER_SIZE, gIn, 0, NULL, NULL) )) |
| { |
| vlog_error( "\n*** Error %d in clEnqueueWriteBuffer ***\n", error ); |
| return error; |
| } |
| |
| |
| // Run the kernels |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| { |
| size_t vectorSize = sizeValues[j] * sizeof(cl_double); |
| size_t localCount = (BUFFER_SIZE + vectorSize - 1) / vectorSize; |
| if( ( error = clSetKernelArg( test_info.k[j][0], 0, sizeof( gOutBuffer[j] ), &gOutBuffer[j] ) )) { LogBuildError(test_info.programs[j]); goto exit; } |
| if( ( error = clSetKernelArg( test_info.k[j][0], 1, sizeof( gInBuffer ), &gInBuffer ) )) { LogBuildError(test_info.programs[j]); goto exit; } |
| |
| double sum = 0.0; |
| double bestTime = INFINITY; |
| for( i = 0; i < PERF_LOOP_COUNT; i++ ) |
| { |
| uint64_t startTime = GetTime(); |
| if( (error = clEnqueueNDRangeKernel(gQueue, test_info.k[j][0], 1, NULL, &localCount, NULL, 0, NULL, NULL)) ) |
| { |
| vlog_error( "FAILED -- could not execute kernel\n" ); |
| goto exit; |
| } |
| |
| // Make sure OpenCL is done |
| if( (error = clFinish(gQueue) ) ) |
| { |
| vlog_error( "Error %d at clFinish\n", error ); |
| goto exit; |
| } |
| |
| uint64_t endTime = GetTime(); |
| double current_time = SubtractTime( endTime, startTime ); |
| sum += current_time; |
| if( current_time < bestTime ) |
| bestTime = current_time; |
| } |
| |
| if( gReportAverageTimes ) |
| bestTime = sum / PERF_LOOP_COUNT; |
| double clocksPerOp = bestTime * (double) gDeviceFrequency * gComputeDevices * gSimdSize * 1e6 / (BUFFER_SIZE / sizeof( double ) ); |
| vlog_perf( clocksPerOp, LOWER_IS_BETTER, "clocks / element", "%sD%s", f->name, sizeNames[j] ); |
| } |
| for( ; j < gMaxVectorSizeIndex; j++ ) |
| vlog( "\t -- " ); |
| } |
| |
| if( ! gSkipCorrectnessTesting ) |
| vlog( "\t%8.2f @ %a", maxError, maxErrorVal ); |
| |
| #if defined( __APPLE__ ) |
| vlog( "\t(%2.2f seconds)", end_time - start_time ); |
| #endif |
| vlog( "\n" ); |
| |
| exit: |
| for( i = gMinVectorSizeIndex; i < gMaxVectorSizeIndex; i++ ) |
| { |
| clReleaseProgram(test_info.programs[i]); |
| if( test_info.k[i] ) |
| { |
| for( j = 0; j < test_info.threadCount; j++ ) |
| clReleaseKernel(test_info.k[i][j]); |
| |
| free( test_info.k[i] ); |
| } |
| } |
| if( test_info.tinfo ) |
| { |
| for( i = 0; i < test_info.threadCount; i++ ) |
| { |
| clReleaseMemObject(test_info.tinfo[i].inBuf); |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| clReleaseMemObject(test_info.tinfo[i].outBuf[j]); |
| clReleaseCommandQueue(test_info.tinfo[i].tQueue); |
| } |
| |
| free( test_info.tinfo ); |
| } |
| |
| return error; |
| } |
| |
| |
