| // |
| // 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" |
| |
| int TestFunc_Float_Float_Float(const Func *f, MTdata, bool relaxedMode); |
| int TestFunc_Double_Double_Double(const Func *f, MTdata, bool relaxedMode); |
| int TestFunc_Float_Float_Float_nextafter(const Func *f, MTdata, |
| bool relaxedMode); |
| int TestFunc_Double_Double_Double_nextafter(const Func *f, MTdata, |
| bool relaxedMode); |
| int TestFunc_Float_Float_Float_common(const Func *f, MTdata, int isNextafter, |
| bool relaxedMode); |
| int TestFunc_Double_Double_Double_common(const Func *f, MTdata, int isNextafter, |
| bool relaxedMode); |
| |
| const float twoToMinus126 = MAKE_HEX_FLOAT(0x1p-126f, 1, -126); |
| const double twoToMinus1022 = MAKE_HEX_DOUBLE(0x1p-1022, 1, -1022); |
| |
| extern const vtbl _binary = { "binary", TestFunc_Float_Float_Float, |
| TestFunc_Double_Double_Double }; |
| |
| extern const vtbl _binary_nextafter = { |
| "binary_nextafter", TestFunc_Float_Float_Float_nextafter, |
| TestFunc_Double_Double_Double_nextafter |
| }; |
| |
| static int BuildKernel(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], "* in1, __global float", sizeNames[vectorSize], "* in2 )\n" |
| "{\n" |
| " int i = get_global_id(0);\n" |
| " out[i] = ", name, "( in1[i], in2[i] );\n" |
| "}\n" |
| }; |
| |
| const char *c3[] = { "__kernel void math_kernel", sizeNames[vectorSize], "( __global float* out, __global float* in, __global float* in2)\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" |
| " float3 f1 = vload3( 0, in2 + 3 * i );\n" |
| " f0 = ", name, "( f0, f1 );\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, f1;\n" |
| " switch( parity )\n" |
| " {\n" |
| " case 1:\n" |
| " f0 = (float3)( in[3*i], NAN, NAN ); \n" |
| " f1 = (float3)( in2[3*i], NAN, NAN ); \n" |
| " break;\n" |
| " case 0:\n" |
| " f0 = (float3)( in[3*i], in[3*i+1], NAN ); \n" |
| " f1 = (float3)( in2[3*i], in2[3*i+1], NAN ); \n" |
| " break;\n" |
| " }\n" |
| " f0 = ", name, "( f0, f1 );\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], "* in1, __global double", sizeNames[vectorSize], "* in2 )\n" |
| "{\n" |
| " int i = get_global_id(0);\n" |
| " out[i] = ", name, "( in1[i], in2[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, __global double* in2)\n" |
| "{\n" |
| " size_t i = get_global_id(0);\n" |
| " if( i + 1 < get_global_size(0) )\n" |
| " {\n" |
| " double3 d0 = vload3( 0, in + 3 * i );\n" |
| " double3 d1 = vload3( 0, in2 + 3 * i );\n" |
| " d0 = ", name, "( d0, d1 );\n" |
| " vstore3( d0, 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 d0, d1;\n" |
| " switch( parity )\n" |
| " {\n" |
| " case 1:\n" |
| " d0 = (double3)( in[3*i], NAN, NAN ); \n" |
| " d1 = (double3)( in2[3*i], NAN, NAN ); \n" |
| " break;\n" |
| " case 0:\n" |
| " d0 = (double3)( in[3*i], in[3*i+1], NAN ); \n" |
| " d1 = (double3)( in2[3*i], in2[3*i+1], NAN ); \n" |
| " break;\n" |
| " }\n" |
| " d0 = ", name, "( d0, d1 );\n" |
| " switch( parity )\n" |
| " {\n" |
| " case 0:\n" |
| " out[3*i+1] = d0.y; \n" |
| " // fall through\n" |
| " case 1:\n" |
| " out[3*i] = d0.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); |
| } |
| |
| // A table of more difficult cases to get right |
| static const float specialValuesFloat[] = { |
| -NAN, -INFINITY, -FLT_MAX, MAKE_HEX_FLOAT(-0x1.000002p64f, -0x1000002L, 40), MAKE_HEX_FLOAT(-0x1.0p64f, -0x1L, 64), MAKE_HEX_FLOAT(-0x1.fffffep63f, -0x1fffffeL, 39), MAKE_HEX_FLOAT(-0x1.000002p63f, -0x1000002L, 39), MAKE_HEX_FLOAT(-0x1.0p63f, -0x1L, 63), MAKE_HEX_FLOAT(-0x1.fffffep62f, -0x1fffffeL, 38), |
| MAKE_HEX_FLOAT(-0x1.000002p32f, -0x1000002L, 8), MAKE_HEX_FLOAT(-0x1.0p32f, -0x1L, 32), MAKE_HEX_FLOAT(-0x1.fffffep31f, -0x1fffffeL, 7), MAKE_HEX_FLOAT(-0x1.000002p31f, -0x1000002L, 7), MAKE_HEX_FLOAT(-0x1.0p31f, -0x1L, 31), MAKE_HEX_FLOAT(-0x1.fffffep30f, -0x1fffffeL, 6), -1000.f, -100.f, -4.0f, -3.5f, |
| -3.0f, MAKE_HEX_FLOAT(-0x1.800002p1f, -0x1800002L, -23), -2.5f, MAKE_HEX_FLOAT(-0x1.7ffffep1f, -0x17ffffeL, -23), -2.0f, MAKE_HEX_FLOAT(-0x1.800002p0f, -0x1800002L, -24), -1.5f, MAKE_HEX_FLOAT(-0x1.7ffffep0f, -0x17ffffeL, -24),MAKE_HEX_FLOAT(-0x1.000002p0f, -0x1000002L, -24), -1.0f, MAKE_HEX_FLOAT(-0x1.fffffep-1f, -0x1fffffeL, -25), |
| MAKE_HEX_FLOAT(-0x1.000002p-1f, -0x1000002L, -25), -0.5f, MAKE_HEX_FLOAT(-0x1.fffffep-2f, -0x1fffffeL, -26), MAKE_HEX_FLOAT(-0x1.000002p-2f, -0x1000002L, -26), -0.25f, MAKE_HEX_FLOAT(-0x1.fffffep-3f, -0x1fffffeL, -27), |
| MAKE_HEX_FLOAT(-0x1.000002p-126f, -0x1000002L, -150), -FLT_MIN, MAKE_HEX_FLOAT(-0x0.fffffep-126f, -0x0fffffeL, -150), MAKE_HEX_FLOAT(-0x0.000ffep-126f, -0x0000ffeL, -150), MAKE_HEX_FLOAT(-0x0.0000fep-126f, -0x00000feL, -150), MAKE_HEX_FLOAT(-0x0.00000ep-126f, -0x000000eL, -150), MAKE_HEX_FLOAT(-0x0.00000cp-126f, -0x000000cL, -150), MAKE_HEX_FLOAT(-0x0.00000ap-126f, -0x000000aL, -150), |
| MAKE_HEX_FLOAT(-0x0.000008p-126f, -0x0000008L, -150), MAKE_HEX_FLOAT(-0x0.000006p-126f, -0x0000006L, -150), MAKE_HEX_FLOAT(-0x0.000004p-126f, -0x0000004L, -150), MAKE_HEX_FLOAT(-0x0.000002p-126f, -0x0000002L, -150), -0.0f, |
| |
| +NAN, +INFINITY, +FLT_MAX, MAKE_HEX_FLOAT(+0x1.000002p64f, +0x1000002L, 40), MAKE_HEX_FLOAT(+0x1.0p64f, +0x1L, 64), MAKE_HEX_FLOAT(+0x1.fffffep63f, +0x1fffffeL, 39), MAKE_HEX_FLOAT(+0x1.000002p63f, +0x1000002L, 39), MAKE_HEX_FLOAT(+0x1.0p63f, +0x1L, 63), MAKE_HEX_FLOAT(+0x1.fffffep62f, +0x1fffffeL, 38), |
| MAKE_HEX_FLOAT(+0x1.000002p32f, +0x1000002L, 8), MAKE_HEX_FLOAT(+0x1.0p32f, +0x1L, 32), MAKE_HEX_FLOAT(+0x1.fffffep31f, +0x1fffffeL, 7), MAKE_HEX_FLOAT(+0x1.000002p31f, +0x1000002L, 7), MAKE_HEX_FLOAT(+0x1.0p31f, +0x1L, 31), MAKE_HEX_FLOAT(+0x1.fffffep30f, +0x1fffffeL, 6), +1000.f, +100.f, +4.0f, +3.5f, |
| +3.0f, MAKE_HEX_FLOAT(+0x1.800002p1f, +0x1800002L, -23), 2.5f, MAKE_HEX_FLOAT(+0x1.7ffffep1f, +0x17ffffeL, -23),+2.0f, MAKE_HEX_FLOAT(+0x1.800002p0f, +0x1800002L, -24), 1.5f, MAKE_HEX_FLOAT(+0x1.7ffffep0f, +0x17ffffeL, -24), MAKE_HEX_FLOAT(+0x1.000002p0f, +0x1000002L, -24), +1.0f, MAKE_HEX_FLOAT(+0x1.fffffep-1f, +0x1fffffeL, -25), |
| MAKE_HEX_FLOAT(+0x1.000002p-1f, +0x1000002L, -25), +0.5f, MAKE_HEX_FLOAT(+0x1.fffffep-2f, +0x1fffffeL, -26), MAKE_HEX_FLOAT(+0x1.000002p-2f, +0x1000002L, -26), +0.25f, MAKE_HEX_FLOAT(+0x1.fffffep-3f, +0x1fffffeL, -27), |
| MAKE_HEX_FLOAT(0x1.000002p-126f, 0x1000002L, -150), +FLT_MIN, MAKE_HEX_FLOAT(+0x0.fffffep-126f, +0x0fffffeL, -150), MAKE_HEX_FLOAT(+0x0.000ffep-126f, +0x0000ffeL, -150), MAKE_HEX_FLOAT(+0x0.0000fep-126f, +0x00000feL, -150), MAKE_HEX_FLOAT(+0x0.00000ep-126f, +0x000000eL, -150), MAKE_HEX_FLOAT(+0x0.00000cp-126f, +0x000000cL, -150), MAKE_HEX_FLOAT(+0x0.00000ap-126f, +0x000000aL, -150), |
| MAKE_HEX_FLOAT(+0x0.000008p-126f, +0x0000008L, -150), MAKE_HEX_FLOAT(+0x0.000006p-126f, +0x0000006L, -150), MAKE_HEX_FLOAT(+0x0.000004p-126f, +0x0000004L, -150), MAKE_HEX_FLOAT(+0x0.000002p-126f, +0x0000002L, -150), +0.0f |
| }; |
| |
| static size_t specialValuesFloatCount = sizeof( specialValuesFloat ) / sizeof( specialValuesFloat[0] ); |
| |
| 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 inBuf2; // 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 (param 1). Init to 0. |
| double maxErrorValue2; // position of the max error value (param 2). Init to 0. |
| MTdata d; |
| 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 isFDim; |
| int skipNanInf; |
| int isNextafter; |
| bool relaxedMode; // True if test is running in relaxed mode, false |
| // otherwise. |
| } TestInfo; |
| |
| static cl_int TestFloat( cl_uint job_id, cl_uint thread_id, void *p ); |
| |
| int TestFunc_Float_Float_Float_common(const Func *f, MTdata d, int isNextafter, |
| bool relaxedMode) |
| { |
| TestInfo test_info; |
| cl_int error; |
| size_t i, j; |
| float maxError = 0.0f; |
| double maxErrorVal = 0.0; |
| double maxErrorVal2 = 0.0; |
| int skipTestingRelaxed = 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 = 1; |
| |
| if (gWimpyMode){ |
| test_info.subBufferSize = gWimpyBufferSize / (sizeof( cl_float) * RoundUpToNextPowerOfTwo(test_info.threadCount)); |
| test_info.scale = (cl_uint) sizeof(cl_float) * 2 * gWimpyReductionFactor; |
| } |
| 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.isFDim = 0 == strcmp( "fdim", f->nameInCode ); |
| test_info.skipNanInf = test_info.isFDim && ! gInfNanSupport; |
| test_info.isNextafter = isNextafter; |
| 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; |
| } |
| test_info.tinfo[i].inBuf2 = clCreateSubBuffer( gInBuffer2, CL_MEM_READ_ONLY, CL_BUFFER_CREATE_TYPE_REGION, ®ion, &error); |
| if( error || NULL == test_info.tinfo[i].inBuf2 ) |
| { |
| vlog_error( "Error: Unable to create sub-buffer of gInBuffer2 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 gOutBuffer[%d] for region {%zd, %zd}\n", (int) j, 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; |
| } |
| |
| test_info.tinfo[i].d = init_genrand(genrand_int32(d)); |
| } |
| |
| // 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; |
| } |
| |
| // Run the kernels |
| if( !gSkipCorrectnessTesting ) |
| { |
| 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; |
| maxErrorVal2 = test_info.tinfo[i].maxErrorValue2; |
| } |
| } |
| |
| if( error ) |
| goto exit; |
| |
| if( gWimpyMode ) |
| vlog( "Wimp pass" ); |
| else |
| vlog( "passed" ); |
| } |
| |
| |
| if( gMeasureTimes ) |
| { |
| //Init input arrays |
| uint32_t *p = (uint32_t *)gIn; |
| uint32_t *p2 = (uint32_t *)gIn2; |
| for( j = 0; j < BUFFER_SIZE / sizeof( float ); j++ ) |
| { |
| p[j] = (genrand_int32(d) & ~0x40000000) | 0x20000000; |
| p2[j] = 0x3fc00000; |
| } |
| |
| 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; |
| } |
| if( (error = clEnqueueWriteBuffer(gQueue, gInBuffer2, CL_FALSE, 0, BUFFER_SIZE, gIn2, 0, NULL, NULL) )) |
| { |
| vlog_error( "\n*** Error %d in clEnqueueWriteBuffer2 ***\n", error ); |
| return error; |
| } |
| |
| |
| // Run the kernels |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| { |
| size_t vectorSize = sizeof( cl_float ) * sizeValues[j]; |
| size_t localCount = (BUFFER_SIZE + vectorSize - 1) / vectorSize; // BUFFER_SIZE / vectorSize rounded up |
| 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; } |
| if( ( error = clSetKernelArg( test_info.k[j][0], 2, sizeof( gInBuffer2 ), &gInBuffer2 ) )) { 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 time = SubtractTime( endTime, startTime ); |
| sum += time; |
| if( time < bestTime ) |
| bestTime = 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, %a}", maxError, maxErrorVal, maxErrorVal2 ); |
| 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++ ) |
| { |
| free_mtdata( test_info.tinfo[i].d ); |
| clReleaseMemObject(test_info.tinfo[i].inBuf); |
| clReleaseMemObject(test_info.tinfo[i].inBuf2); |
| 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 base = job_id * (cl_uint) job->step; |
| ThreadInfo *tinfo = job->tinfo + thread_id; |
| fptr func = job->f->func; |
| int ftz = job->ftz; |
| bool relaxedMode = job->relaxedMode; |
| float ulps = getAllowedUlpError(job->f, relaxedMode); |
| MTdata d = tinfo->d; |
| cl_uint j, k; |
| cl_int error; |
| cl_uchar *overflow = (cl_uchar*)malloc(buffer_size); |
| const char *name = job->f->name; |
| int isFDim = job->isFDim; |
| int skipNanInf = job->skipNanInf; |
| int isNextafter = job->isNextafter; |
| cl_uint *t = 0; |
| float *r=0,*s=0,*s2=0; |
| cl_int copysign_test = 0; |
| RoundingMode oldRoundMode; |
| int skipVerification = 0; |
| |
| if (relaxedMode) |
| { |
| if (strcmp(name,"pow")==0 && gFastRelaxedDerived) |
| { |
| func = job->f->rfunc; |
| ulps = INFINITY; |
| skipVerification = 1; |
| }else |
| { |
| func = job->f->rfunc; |
| } |
| } |
| |
| // 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] = (cl_uint*) 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" ); |
| |
| //Init input array |
| cl_uint *p = (cl_uint *)gIn + thread_id * buffer_elements; |
| cl_uint *p2 = (cl_uint *)gIn2 + thread_id * buffer_elements; |
| j = 0; |
| |
| int totalSpecialValueCount = specialValuesFloatCount * specialValuesFloatCount; |
| int indx = (totalSpecialValueCount - 1) / buffer_elements; |
| |
| if (job_id <= (cl_uint)indx) |
| { // test edge cases |
| float *fp = (float *)p; |
| float *fp2 = (float *)p2; |
| uint32_t x, y; |
| |
| x = (job_id * buffer_elements) % specialValuesFloatCount; |
| y = (job_id * buffer_elements) / specialValuesFloatCount; |
| |
| for( ; j < buffer_elements; j++ ) |
| { |
| fp[j] = specialValuesFloat[x]; |
| fp2[j] = specialValuesFloat[y]; |
| if( ++x >= specialValuesFloatCount ) |
| { |
| x = 0; |
| y++; |
| if( y >= specialValuesFloatCount ) |
| break; |
| } |
| } |
| } |
| |
| //Init any remaining values. |
| for( ; j < buffer_elements; j++ ) |
| { |
| p[j] = genrand_int32(d); |
| p2[j] = genrand_int32(d); |
| } |
| |
| 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 ); |
| goto exit; |
| } |
| |
| if( (error = clEnqueueWriteBuffer( tinfo->tQueue, tinfo->inBuf2, CL_FALSE, 0, buffer_size, p2, 0, NULL, NULL) )) |
| { |
| vlog_error( "Error: clEnqueueWriteBuffer failed! err: %d\n", error ); |
| goto exit; |
| } |
| |
| 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 ); |
| goto exit; |
| } |
| if( (error = clReleaseEvent( e[j] ) )) |
| { |
| vlog_error( "Error: clReleaseEvent failed! err: %d\n", error ); |
| goto exit; |
| } |
| |
| // 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 ); |
| goto exit; |
| } |
| |
| // 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 = clSetKernelArg( kernel, 2, sizeof( tinfo->inBuf2 ), &tinfo->inBuf2 ) )) { 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" ); |
| goto exit; |
| } |
| } |
| |
| // Get that moving |
| if( (error = clFlush(tinfo->tQueue) )) |
| vlog( "clFlush 2 failed\n" ); |
| |
| if( gSkipCorrectnessTesting ) |
| { |
| if( (error = clFinish(tinfo->tQueue)) ) |
| { |
| vlog_error( "Error: clFinish failed! err: %d\n", error ); |
| goto exit; |
| } |
| free(overflow); |
| return CL_SUCCESS; |
| } |
| |
| FPU_mode_type oldMode; |
| oldRoundMode = kRoundToNearestEven; |
| if( isFDim ) |
| { |
| //Calculate the correctly rounded reference result |
| memset( &oldMode, 0, sizeof( oldMode ) ); |
| if( ftz ) |
| ForceFTZ( &oldMode ); |
| |
| // Set the rounding mode to match the device |
| if (gIsInRTZMode) |
| oldRoundMode = set_round(kRoundTowardZero, kfloat); |
| } |
| |
| if(!strcmp(name, "copysign")) |
| copysign_test = 1; |
| |
| #define ref_func(s, s2) (copysign_test ? func.f_ff_f( s, s2 ) : func.f_ff( s, s2 )) |
| |
| //Calculate the correctly rounded reference result |
| r = (float *)gOut_Ref + thread_id * buffer_elements; |
| s = (float *)gIn + thread_id * buffer_elements; |
| s2 = (float *)gIn2 + thread_id * buffer_elements; |
| if( skipNanInf ) |
| { |
| for( j = 0; j < buffer_elements; j++ ) |
| { |
| feclearexcept(FE_OVERFLOW); |
| r[j] = (float) ref_func( s[j], s2[j] ); |
| overflow[j] = FE_OVERFLOW == (FE_OVERFLOW & fetestexcept(FE_OVERFLOW)); |
| } |
| } |
| else |
| { |
| for( j = 0; j < buffer_elements; j++ ) |
| r[j] = (float) ref_func( s[j], s2[j] ); |
| } |
| |
| if( isFDim && ftz ) |
| RestoreFPState( &oldMode ); |
| |
| // 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_uint*) 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 ); |
| goto exit; |
| } |
| } |
| |
| // Wait for the last buffer |
| out[j] = (cl_uint*) 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 ); |
| goto exit; |
| } |
| |
| if (!skipVerification) { |
| //Verify data |
| t = (cl_uint *)r; |
| for( j = 0; j < buffer_elements; j++ ) |
| { |
| for( k = gMinVectorSizeIndex; k < gMaxVectorSizeIndex; k++ ) |
| { |
| cl_uint *q = out[k]; |
| |
| // If we aren't getting the correctly rounded result |
| if( t[j] != q[j] ) |
| { |
| float test = ((float*) q)[j]; |
| double correct = ref_func( s[j], s2[j] ); |
| |
| // Per section 10 paragraph 6, accept any result if an input or output is a infinity or NaN or overflow |
| // As per OpenCL 2.0 spec, section 5.8.4.3, enabling fast-relaxed-math mode also enables |
| // -cl-finite-math-only optimization. This optimization allows to assume that arguments and |
| // results are not NaNs or +/-INFs. Hence, accept any result if inputs or results are NaNs or INFs. |
| if (relaxedMode || skipNanInf) |
| { |
| if( skipNanInf && overflow[j]) |
| continue; |
| // Note: no double rounding here. Reference functions calculate in single precision. |
| if( IsFloatInfinity(correct) || IsFloatNaN(correct) || |
| IsFloatInfinity(s2[j]) || IsFloatNaN(s2[j]) || |
| IsFloatInfinity(s[j]) || IsFloatNaN(s[j]) ) |
| continue; |
| } |
| |
| float err = Ulp_Error( test, correct ); |
| int fail = ! (fabsf(err) <= ulps); |
| |
| if( fail && ftz ) |
| { |
| // retry per section 6.5.3.2 |
| if( IsFloatResultSubnormal(correct, ulps ) ) |
| { |
| fail = fail && ( test != 0.0f ); |
| if( ! fail ) |
| err = 0.0f; |
| } |
| |
| // nextafter on FTZ platforms may return the smallest |
| // normal float (2^-126) given a denormal or a zero |
| // as the first argument. The rationale here is that |
| // nextafter flushes the argument to zero and then |
| // returns the next representable number in the |
| // direction of the second argument, and since |
| // denorms are considered as zero, the smallest |
| // normal number is the next representable number. |
| // In which case, it should have the same sign as the |
| // second argument. |
| if (isNextafter ) |
| { |
| if(IsFloatSubnormal(s[j]) || s[j] == 0.0f) |
| { |
| float value = copysignf(twoToMinus126, s2[j]); |
| fail = fail && (test != value); |
| if (!fail) |
| err = 0.0f; |
| } |
| } |
| else |
| { |
| // retry per section 6.5.3.3 |
| if( IsFloatSubnormal( s[j] ) ) |
| { |
| double correct2, correct3; |
| float err2, err3; |
| |
| if( skipNanInf ) |
| feclearexcept(FE_OVERFLOW); |
| |
| correct2 = ref_func( 0.0, s2[j] ); |
| correct3 = ref_func( -0.0, s2[j] ); |
| |
| // Per section 10 paragraph 6, accept any result if an input or output is a infinity or NaN or overflow |
| // As per OpenCL 2.0 spec, section 5.8.4.3, enabling fast-relaxed-math mode also enables |
| // -cl-finite-math-only optimization. This optimization allows to assume that arguments and |
| // results are not NaNs or +/-INFs. Hence, accept any result if inputs or results are NaNs or INFs. |
| if (relaxedMode || skipNanInf) |
| { |
| if( fetestexcept(FE_OVERFLOW) && skipNanInf ) |
| continue; |
| |
| // Note: no double rounding here. Reference functions calculate in single precision. |
| if( IsFloatInfinity(correct2) || IsFloatNaN(correct2) || |
| IsFloatInfinity(correct3) || IsFloatNaN(correct3) ) |
| continue; |
| } |
| |
| 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( IsFloatResultSubnormal( correct2, ulps ) || IsFloatResultSubnormal( correct3, ulps ) ) |
| { |
| fail = fail && ( test != 0.0f); |
| if( ! fail ) |
| err = 0.0f; |
| } |
| |
| //try with both args as zero |
| if( IsFloatSubnormal( s2[j] ) ) |
| { |
| double correct4, correct5; |
| float err4, err5; |
| |
| if( skipNanInf ) |
| feclearexcept(FE_OVERFLOW); |
| |
| correct2 = ref_func( 0.0, 0.0 ); |
| correct3 = ref_func( -0.0, 0.0 ); |
| correct4 = ref_func( 0.0, -0.0 ); |
| correct5 = ref_func( -0.0, -0.0 ); |
| |
| // Per section 10 paragraph 6, accept any result if an input or output is a infinity or NaN or overflow |
| // As per OpenCL 2.0 spec, section 5.8.4.3, enabling fast-relaxed-math mode also enables |
| // -cl-finite-math-only optimization. This optimization allows to assume that arguments and |
| // results are not NaNs or +/-INFs. Hence, accept any result if inputs or results are NaNs or INFs. |
| if (relaxedMode || skipNanInf) |
| { |
| if( fetestexcept(FE_OVERFLOW) && skipNanInf ) |
| continue; |
| |
| // Note: no double rounding here. Reference functions calculate in single precision. |
| if( IsFloatInfinity(correct2) || IsFloatNaN(correct2) || |
| IsFloatInfinity(correct3) || IsFloatNaN(correct3) || |
| IsFloatInfinity(correct4) || IsFloatNaN(correct4) || |
| IsFloatInfinity(correct5) || IsFloatNaN(correct5) ) |
| continue; |
| } |
| |
| err2 = Ulp_Error( test, correct2 ); |
| err3 = Ulp_Error( test, correct3 ); |
| err4 = Ulp_Error( test, correct4 ); |
| err5 = Ulp_Error( test, correct5 ); |
| fail = fail && ((!(fabsf(err2) <= ulps)) && (!(fabsf(err3) <= ulps)) && |
| (!(fabsf(err4) <= ulps)) && (!(fabsf(err5) <= ulps))); |
| if( fabsf( err2 ) < fabsf(err ) ) |
| err = err2; |
| if( fabsf( err3 ) < fabsf(err ) ) |
| err = err3; |
| if( fabsf( err4 ) < fabsf(err ) ) |
| err = err4; |
| if( fabsf( err5 ) < fabsf(err ) ) |
| err = err5; |
| |
| // retry per section 6.5.3.4 |
| if( IsFloatResultSubnormal( correct2, ulps ) || IsFloatResultSubnormal( correct3, ulps ) || |
| IsFloatResultSubnormal( correct4, ulps ) || IsFloatResultSubnormal( correct5, ulps ) ) |
| { |
| fail = fail && ( test != 0.0f); |
| if( ! fail ) |
| err = 0.0f; |
| } |
| } |
| } |
| else if(IsFloatSubnormal(s2[j]) ) |
| { |
| double correct2, correct3; |
| float err2, err3; |
| |
| if( skipNanInf ) |
| feclearexcept(FE_OVERFLOW); |
| |
| correct2 = ref_func( s[j], 0.0 ); |
| correct3 = ref_func( s[j], -0.0 ); |
| |
| // Per section 10 paragraph 6, accept any result if an input or output is a infinity or NaN or overflow |
| // As per OpenCL 2.0 spec, section 5.8.4.3, enabling fast-relaxed-math mode also enables |
| // -cl-finite-math-only optimization. This optimization allows to assume that arguments and |
| // results are not NaNs or +/-INFs. Hence, accept any result if inputs or results are NaNs or INFs. |
| if (relaxedMode || skipNanInf) |
| { |
| // Note: no double rounding here. Reference functions calculate in single precision. |
| if( overflow[j] && skipNanInf) |
| continue; |
| |
| if( IsFloatInfinity(correct2) || IsFloatNaN(correct2) || |
| IsFloatInfinity(correct3) || IsFloatNaN(correct3) ) |
| continue; |
| } |
| |
| 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( IsFloatResultSubnormal( correct2, ulps ) || IsFloatResultSubnormal( 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]; |
| tinfo->maxErrorValue2 = s2[j]; |
| } |
| if( fail ) |
| { |
| vlog_error( "\nERROR: %s%s: %f ulp error at {%a (0x%x), %a (0x%x)}: *%a vs. %a (0x%8.8x) at index: %d\n", name, sizeNames[k], err, s[j], ((cl_uint*)s)[j], s2[j], ((cl_uint*)s2)[j], r[j], test, ((cl_uint*)&test)[0], j ); |
| error = -1; |
| goto exit; |
| } |
| } |
| } |
| } |
| } |
| |
| if (isFDim && gIsInRTZMode) |
| (void)set_round(oldRoundMode, kfloat); |
| |
| 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:%10zu buf_elements:%10u ulps:%5.3f ThreadCount:%2u\n", base, job->step, job->scale, buffer_elements, job->ulps, job->threadCount); |
| } else |
| { |
| vlog("." ); |
| } |
| fflush(stdout); |
| } |
| |
| |
| exit: |
| if( overflow ) |
| free( overflow ); |
| return error; |
| |
| } |
| |
| |
| // A table of more difficult cases to get right |
| static const double specialValuesDouble[] = { |
| -NAN, -INFINITY, -DBL_MAX, MAKE_HEX_DOUBLE(-0x1.0000000000001p64, -0x10000000000001LL, 12), MAKE_HEX_DOUBLE(-0x1.0p64, -0x1LL, 64), MAKE_HEX_DOUBLE(-0x1.fffffffffffffp63, -0x1fffffffffffffLL, 11), MAKE_HEX_DOUBLE(-0x1.0000000000001p63, -0x10000000000001LL, 11), MAKE_HEX_DOUBLE(-0x1.0p63, -0x1LL, 63), MAKE_HEX_DOUBLE(-0x1.fffffffffffffp62, -0x1fffffffffffffLL, 10), |
| MAKE_HEX_DOUBLE(-0x1.000002p32, -0x1000002LL, 8), MAKE_HEX_DOUBLE(-0x1.0p32, -0x1LL, 32), MAKE_HEX_DOUBLE(-0x1.fffffffffffffp31, -0x1fffffffffffffLL, -21), MAKE_HEX_DOUBLE(-0x1.0000000000001p31, -0x10000000000001LL, -21), MAKE_HEX_DOUBLE(-0x1.0p31, -0x1LL, 31), MAKE_HEX_DOUBLE(-0x1.fffffffffffffp30, -0x1fffffffffffffLL, -22), -1000., -100., -4.0, -3.5, |
| -3.0, MAKE_HEX_DOUBLE(-0x1.8000000000001p1, -0x18000000000001LL, -51), -2.5, MAKE_HEX_DOUBLE(-0x1.7ffffffffffffp1, -0x17ffffffffffffLL, -51), -2.0, MAKE_HEX_DOUBLE(-0x1.8000000000001p0, -0x18000000000001LL, -52), -1.5, MAKE_HEX_DOUBLE(-0x1.7ffffffffffffp0, -0x17ffffffffffffLL, -52),MAKE_HEX_DOUBLE(-0x1.0000000000001p0, -0x10000000000001LL, -52), -1.0, MAKE_HEX_DOUBLE(-0x1.fffffffffffffp-1, -0x1fffffffffffffLL, -53), |
| MAKE_HEX_DOUBLE(-0x1.0000000000001p-1, -0x10000000000001LL, -53), -0.5, MAKE_HEX_DOUBLE(-0x1.fffffffffffffp-2, -0x1fffffffffffffLL, -54), MAKE_HEX_DOUBLE(-0x1.0000000000001p-2, -0x10000000000001LL, -54), -0.25, MAKE_HEX_DOUBLE(-0x1.fffffffffffffp-3, -0x1fffffffffffffLL, -55), |
| MAKE_HEX_DOUBLE(-0x1.0000000000001p-1022, -0x10000000000001LL, -1074), -DBL_MIN, MAKE_HEX_DOUBLE(-0x0.fffffffffffffp-1022, -0x0fffffffffffffLL, -1074), MAKE_HEX_DOUBLE(-0x0.0000000000fffp-1022, -0x00000000000fffLL, -1074), MAKE_HEX_DOUBLE(-0x0.00000000000fep-1022, -0x000000000000feLL, -1074), MAKE_HEX_DOUBLE(-0x0.000000000000ep-1022, -0x0000000000000eLL, -1074), MAKE_HEX_DOUBLE(-0x0.000000000000cp-1022, -0x0000000000000cLL, -1074), MAKE_HEX_DOUBLE(-0x0.000000000000ap-1022, -0x0000000000000aLL, -1074), |
| MAKE_HEX_DOUBLE(-0x0.0000000000008p-1022, -0x00000000000008LL, -1074), MAKE_HEX_DOUBLE(-0x0.0000000000007p-1022, -0x00000000000007LL, -1074), MAKE_HEX_DOUBLE(-0x0.0000000000006p-1022, -0x00000000000006LL, -1074), MAKE_HEX_DOUBLE(-0x0.0000000000005p-1022, -0x00000000000005LL, -1074), MAKE_HEX_DOUBLE(-0x0.0000000000004p-1022, -0x00000000000004LL, -1074), |
| MAKE_HEX_DOUBLE(-0x0.0000000000003p-1022, -0x00000000000003LL, -1074), MAKE_HEX_DOUBLE(-0x0.0000000000002p-1022, -0x00000000000002LL, -1074), MAKE_HEX_DOUBLE(-0x0.0000000000001p-1022, -0x00000000000001LL, -1074), -0.0, |
| |
| +NAN, +INFINITY, +DBL_MAX, MAKE_HEX_DOUBLE(+0x1.0000000000001p64, +0x10000000000001LL, 12), MAKE_HEX_DOUBLE(+0x1.0p64, +0x1LL, 64), MAKE_HEX_DOUBLE(+0x1.fffffffffffffp63, +0x1fffffffffffffLL, 11), MAKE_HEX_DOUBLE(+0x1.0000000000001p63, +0x10000000000001LL, 11), MAKE_HEX_DOUBLE(+0x1.0p63, +0x1LL, 63), MAKE_HEX_DOUBLE(+0x1.fffffffffffffp62, +0x1fffffffffffffLL, 10), |
| MAKE_HEX_DOUBLE(+0x1.000002p32, +0x1000002LL, 8), MAKE_HEX_DOUBLE(+0x1.0p32, +0x1LL, 32), MAKE_HEX_DOUBLE(+0x1.fffffffffffffp31, +0x1fffffffffffffLL, -21), MAKE_HEX_DOUBLE(+0x1.0000000000001p31, +0x10000000000001LL, -21), MAKE_HEX_DOUBLE(+0x1.0p31, +0x1LL, 31), MAKE_HEX_DOUBLE(+0x1.fffffffffffffp30, +0x1fffffffffffffLL, -22), +1000., +100., +4.0, +3.5, |
| +3.0, MAKE_HEX_DOUBLE(+0x1.8000000000001p1, +0x18000000000001LL, -51), +2.5, MAKE_HEX_DOUBLE(+0x1.7ffffffffffffp1, +0x17ffffffffffffLL, -51), +2.0, MAKE_HEX_DOUBLE(+0x1.8000000000001p0, +0x18000000000001LL, -52), +1.5, MAKE_HEX_DOUBLE(+0x1.7ffffffffffffp0, +0x17ffffffffffffLL, -52),MAKE_HEX_DOUBLE(-0x1.0000000000001p0, -0x10000000000001LL, -52), +1.0, MAKE_HEX_DOUBLE(+0x1.fffffffffffffp-1, +0x1fffffffffffffLL, -53), |
| MAKE_HEX_DOUBLE(+0x1.0000000000001p-1, +0x10000000000001LL, -53), +0.5, MAKE_HEX_DOUBLE(+0x1.fffffffffffffp-2, +0x1fffffffffffffLL, -54), MAKE_HEX_DOUBLE(+0x1.0000000000001p-2, +0x10000000000001LL, -54), +0.25, MAKE_HEX_DOUBLE(+0x1.fffffffffffffp-3, +0x1fffffffffffffLL, -55), |
| MAKE_HEX_DOUBLE(+0x1.0000000000001p-1022, +0x10000000000001LL, -1074), +DBL_MIN, MAKE_HEX_DOUBLE(+0x0.fffffffffffffp-1022, +0x0fffffffffffffLL, -1074), MAKE_HEX_DOUBLE(+0x0.0000000000fffp-1022, +0x00000000000fffLL, -1074), MAKE_HEX_DOUBLE(+0x0.00000000000fep-1022, +0x000000000000feLL, -1074), MAKE_HEX_DOUBLE(+0x0.000000000000ep-1022, +0x0000000000000eLL, -1074), MAKE_HEX_DOUBLE(+0x0.000000000000cp-1022, +0x0000000000000cLL, -1074), MAKE_HEX_DOUBLE(+0x0.000000000000ap-1022, +0x0000000000000aLL, -1074), |
| MAKE_HEX_DOUBLE(+0x0.0000000000008p-1022, +0x00000000000008LL, -1074), MAKE_HEX_DOUBLE(+0x0.0000000000007p-1022, +0x00000000000007LL, -1074), MAKE_HEX_DOUBLE(+0x0.0000000000006p-1022, +0x00000000000006LL, -1074), MAKE_HEX_DOUBLE(+0x0.0000000000005p-1022, +0x00000000000005LL, -1074), MAKE_HEX_DOUBLE(+0x0.0000000000004p-1022, +0x00000000000004LL, -1074), |
| MAKE_HEX_DOUBLE(+0x0.0000000000003p-1022, +0x00000000000003LL, -1074), MAKE_HEX_DOUBLE(+0x0.0000000000002p-1022, +0x00000000000002LL, -1074), MAKE_HEX_DOUBLE(+0x0.0000000000001p-1022, +0x00000000000001LL, -1074), +0.0, |
| }; |
| |
| static size_t specialValuesDoubleCount = sizeof( specialValuesDouble ) / sizeof( specialValuesDouble[0] ); |
| |
| static cl_int TestDouble( cl_uint job_id, cl_uint thread_id, void *p ); |
| |
| int TestFunc_Double_Double_Double_common(const Func *f, MTdata d, |
| int isNextafter, bool relaxedMode) |
| { |
| TestInfo test_info; |
| cl_int error; |
| size_t i, j; |
| float maxError = 0.0f; |
| double maxErrorVal = 0.0; |
| double maxErrorVal2 = 0.0; |
| |
| 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 = 1; |
| |
| |
| if (gWimpyMode){ |
| test_info.subBufferSize = gWimpyBufferSize / (sizeof( cl_double) * RoundUpToNextPowerOfTwo(test_info.threadCount)); |
| test_info.scale = (cl_uint) sizeof(cl_double) * 2 * gWimpyReductionFactor; |
| } |
| 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.isFDim = 0 == strcmp( "fdim", f->nameInCode ); |
| test_info.skipNanInf = 0; |
| test_info.isNextafter = isNextafter; |
| // 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; |
| } |
| test_info.tinfo[i].inBuf2 = clCreateSubBuffer( gInBuffer2, 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; |
| } |
| test_info.tinfo[i].d = init_genrand(genrand_int32(d)); |
| } |
| |
| |
| // 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; |
| maxErrorVal2 = test_info.tinfo[i].maxErrorValue2; |
| } |
| } |
| |
| if( error ) |
| goto exit; |
| |
| if( gWimpyMode ) |
| vlog( "Wimp pass" ); |
| else |
| vlog( "passed" ); |
| } |
| |
| if( gMeasureTimes ) |
| { |
| //Init input arrays |
| double *p = (double *)gIn; |
| double *p2 = (double *)gIn2; |
| for( j = 0; j < BUFFER_SIZE / sizeof( cl_double ); j++ ) |
| { |
| p[j] = DoubleFromUInt32(genrand_int32(d)); |
| p2[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; |
| } |
| if( (error = clEnqueueWriteBuffer(gQueue, gInBuffer2, CL_FALSE, 0, BUFFER_SIZE, gIn2, 0, NULL, NULL) )) |
| { |
| vlog_error( "\n*** Error %d in clEnqueueWriteBuffer2 ***\n", error ); |
| return error; |
| } |
| |
| |
| // Run the kernels |
| for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ ) |
| { |
| size_t vectorSize = sizeof( cl_double ) * sizeValues[j]; |
| size_t localCount = (BUFFER_SIZE + vectorSize - 1) / vectorSize; // BUFFER_SIZE / vectorSize rounded up |
| 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; } |
| if( ( error = clSetKernelArg( test_info.k[j][0], 2, sizeof( gInBuffer2 ), &gInBuffer2 ) )) { 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 time = SubtractTime( endTime, startTime ); |
| sum += time; |
| if( time < bestTime ) |
| bestTime = 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, %a}", maxError, maxErrorVal, maxErrorVal2 ); |
| vlog( "\n" ); |
| |
| |
| exit: |
| // Release |
| 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++ ) |
| { |
| free_mtdata( test_info.tinfo[i].d ); |
| clReleaseMemObject(test_info.tinfo[i].inBuf); |
| clReleaseMemObject(test_info.tinfo[i].inBuf2); |
| 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 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 base = job_id * (cl_uint) job->step; |
| ThreadInfo *tinfo = job->tinfo + thread_id; |
| float ulps = job->ulps; |
| dptr func = job->f->dfunc; |
| int ftz = job->ftz; |
| MTdata d = tinfo->d; |
| cl_uint j, k; |
| cl_int error; |
| const char *name = job->f->name; |
| |
| int isNextafter = job->isNextafter; |
| cl_ulong *t; |
| cl_double *r,*s,*s2; |
| |
| 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" ); |
| |
| //Init input array |
| cl_ulong *p = (cl_ulong *)gIn + thread_id * buffer_elements; |
| cl_ulong *p2 = (cl_ulong *)gIn2 + thread_id * buffer_elements; |
| j = 0; |
| int totalSpecialValueCount = specialValuesDoubleCount * specialValuesDoubleCount; |
| int indx = (totalSpecialValueCount - 1) / buffer_elements; |
| |
| if( job_id <= (cl_uint)indx ) |
| { // test edge cases |
| cl_double *fp = (cl_double *)p; |
| cl_double *fp2 = (cl_double *)p2; |
| uint32_t x, y; |
| |
| x = (job_id * buffer_elements) % specialValuesDoubleCount; |
| y = (job_id * buffer_elements) / specialValuesDoubleCount; |
| |
| for( ; j < buffer_elements; j++ ) |
| { |
| fp[j] = specialValuesDouble[x]; |
| fp2[j] = specialValuesDouble[y]; |
| if( ++x >= specialValuesDoubleCount ) |
| { |
| x = 0; |
| y++; |
| if( y >= specialValuesDoubleCount ) |
| break; |
| } |
| } |
| } |
| |
| //Init any remaining values. |
| for( ; j < buffer_elements; j++ ) |
| { |
| p[j] = genrand_int64(d); |
| p2[j] = genrand_int64(d); |
| } |
| |
| 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 ); |
| goto exit; |
| } |
| |
| if( (error = clEnqueueWriteBuffer( tinfo->tQueue, tinfo->inBuf2, CL_FALSE, 0, buffer_size, p2, 0, NULL, NULL) )) |
| { |
| vlog_error( "Error: clEnqueueWriteBuffer failed! err: %d\n", error ); |
| goto exit; |
| } |
| |
| 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 ); |
| goto exit; |
| } |
| if( (error = clReleaseEvent( e[j] ) )) |
| { |
| vlog_error( "Error: clReleaseEvent failed! err: %d\n", error ); |
| goto exit; |
| } |
| |
| // 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 ); |
| goto exit; |
| } |
| |
| // 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 = clSetKernelArg( kernel, 2, sizeof( tinfo->inBuf2 ), &tinfo->inBuf2 ) )) { 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" ); |
| goto exit; |
| } |
| } |
| |
| // Get that moving |
| if( (error = clFlush(tinfo->tQueue) )) |
| vlog( "clFlush 2 failed\n" ); |
| |
| if( gSkipCorrectnessTesting ) |
| return CL_SUCCESS; |
| |
| //Calculate the correctly rounded reference result |
| r = (cl_double *)gOut_Ref + thread_id * buffer_elements; |
| s = (cl_double *)gIn + thread_id * buffer_elements; |
| s2 = (cl_double *)gIn2 + thread_id * buffer_elements; |
| for( j = 0; j < buffer_elements; j++ ) |
| r[j] = (cl_double) func.f_ff( s[j], s2[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 ); |
| goto exit; |
| } |
| } |
| |
| // 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 ); |
| goto exit; |
| } |
| |
| //Verify data |
| 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_ff( s[j], s2[j] ); |
| float err = Bruteforce_Ulp_Error_Double( test, correct ); |
| int fail = ! (fabsf(err) <= ulps); |
| |
| if( fail && ftz ) |
| { |
| // retry per section 6.5.3.2 |
| if( IsDoubleResultSubnormal(correct, ulps ) ) |
| { |
| fail = fail && ( test != 0.0f ); |
| if( ! fail ) |
| err = 0.0f; |
| } |
| |
| // nextafter on FTZ platforms may return the smallest |
| // normal float (2^-126) given a denormal or a zero |
| // as the first argument. The rationale here is that |
| // nextafter flushes the argument to zero and then |
| // returns the next representable number in the |
| // direction of the second argument, and since |
| // denorms are considered as zero, the smallest |
| // normal number is the next representable number. |
| // In which case, it should have the same sign as the |
| // second argument. |
| if (isNextafter ) |
| { |
| if(IsDoubleSubnormal(s[j]) || s[j] == 0.0f) |
| { |
| cl_double value = copysign(twoToMinus1022, s2[j]); |
| fail = fail && (test != value); |
| if (!fail) |
| err = 0.0f; |
| } |
| } |
| else |
| { |
| // retry per section 6.5.3.3 |
| if( IsDoubleSubnormal( s[j] ) ) |
| { |
| long double correct2 = func.f_ff( 0.0, s2[j] ); |
| long double correct3 = func.f_ff( -0.0, s2[j] ); |
| 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; |
| } |
| |
| //try with both args as zero |
| if( IsDoubleSubnormal( s2[j] ) ) |
| { |
| correct2 = func.f_ff( 0.0, 0.0 ); |
| correct3 = func.f_ff( -0.0, 0.0 ); |
| long double correct4 = func.f_ff( 0.0, -0.0 ); |
| long double correct5 = func.f_ff( -0.0, -0.0 ); |
| err2 = Bruteforce_Ulp_Error_Double( test, correct2 ); |
| err3 = Bruteforce_Ulp_Error_Double( test, correct3 ); |
| float err4 = Bruteforce_Ulp_Error_Double( test, correct4 ); |
| float err5 = Bruteforce_Ulp_Error_Double( test, correct5 ); |
| fail = fail && ((!(fabsf(err2) <= ulps)) && (!(fabsf(err3) <= ulps)) && |
| (!(fabsf(err4) <= ulps)) && (!(fabsf(err5) <= ulps))); |
| if( fabsf( err2 ) < fabsf(err ) ) |
| err = err2; |
| if( fabsf( err3 ) < fabsf(err ) ) |
| err = err3; |
| if( fabsf( err4 ) < fabsf(err ) ) |
| err = err4; |
| if( fabsf( err5 ) < fabsf(err ) ) |
| err = err5; |
| |
| // retry per section 6.5.3.4 |
| if( IsDoubleResultSubnormal( correct2, ulps ) || IsDoubleResultSubnormal( correct3, ulps ) || |
| IsDoubleResultSubnormal( correct4, ulps ) || IsDoubleResultSubnormal( correct5, ulps ) ) |
| { |
| fail = fail && ( test != 0.0f); |
| if( ! fail ) |
| err = 0.0f; |
| } |
| } |
| } |
| else if(IsDoubleSubnormal(s2[j]) ) |
| { |
| long double correct2 = func.f_ff( s[j], 0.0 ); |
| long double correct3 = func.f_ff( s[j], -0.0 ); |
| 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]; |
| tinfo->maxErrorValue2 = s2[j]; |
| } |
| if( fail ) |
| { |
| vlog_error( "\nERROR: %s%s: %f ulp error at {%.13la, %.13la}: *%.13la vs. %.13la\n", name, sizeNames[k], err, s[j], s2[j], r[j], test ); |
| error = -1; |
| goto exit; |
| } |
| } |
| } |
| } |
| |
| 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:%10zu buf_elements:%10u ulps:%5.3f ThreadCount:%2u\n", base, job->step, job->scale, buffer_elements, job->ulps, job->threadCount); |
| } else |
| { |
| vlog("." ); |
| } |
| fflush(stdout); |
| } |
| exit: |
| return error; |
| |
| } |
| |
| int TestFunc_Float_Float_Float(const Func *f, MTdata d, bool relaxedMode) |
| { |
| return TestFunc_Float_Float_Float_common(f, d, 0, relaxedMode); |
| } |
| |
| int TestFunc_Double_Double_Double(const Func *f, MTdata d, bool relaxedMode) |
| { |
| return TestFunc_Double_Double_Double_common(f, d, 0, relaxedMode); |
| } |
| |
| int TestFunc_Float_Float_Float_nextafter(const Func *f, MTdata d, |
| bool relaxedMode) |
| { |
| return TestFunc_Float_Float_Float_common(f, d, 1, relaxedMode); |
| } |
| |
| int TestFunc_Double_Double_Double_nextafter(const Func *f, MTdata d, |
| bool relaxedMode) |
| { |
| return TestFunc_Double_Double_Double_common(f, d, 1, relaxedMode); |
| } |
| |
