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1126 lines
41 KiB
1126 lines
41 KiB
//
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// Copyright (c) 2017 The Khronos Group Inc.
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//
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// Licensed under the Apache License, Version 2.0 (the "License");
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// you may not use this file except in compliance with the License.
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// You may obtain a copy of the License at
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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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// Unless required by applicable law or agreed to in writing, software
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// distributed under the License is distributed on an "AS IS" BASIS,
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// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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// See the License for the specific language governing permissions and
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// limitations under the License.
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//
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#include "harness/compat.h"
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#include "testBase.h"
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#include "harness/testHarness.h"
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#include "harness/typeWrappers.h"
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#include "harness/conversions.h"
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#include "harness/errorHelpers.h"
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#include <float.h>
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const char *crossKernelSource =
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"__kernel void sample_test(__global float4 *sourceA, __global float4 *sourceB, __global float4 *destValues)\n"
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"{\n"
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" int tid = get_global_id(0);\n"
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" destValues[tid] = cross( sourceA[tid], sourceB[tid] );\n"
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"\n"
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"}\n" ;
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const char *crossKernelSourceV3 =
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"__kernel void sample_test(__global float *sourceA, __global float *sourceB, __global float *destValues)\n"
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"{\n"
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" int tid = get_global_id(0);\n"
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" vstore3( cross( vload3( tid, sourceA), vload3( tid, sourceB) ), tid, destValues );\n"
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"\n"
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"}\n";
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const char *twoToFloatKernelPattern =
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"__kernel void sample_test(__global float%s *sourceA, __global float%s *sourceB, __global float *destValues)\n"
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"{\n"
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" int tid = get_global_id(0);\n"
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" destValues[tid] = %s( sourceA[tid], sourceB[tid] );\n"
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"\n"
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"}\n";
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const char *twoToFloatKernelPatternV3 =
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"__kernel void sample_test(__global float%s *sourceA, __global float%s *sourceB, __global float *destValues)\n"
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"{\n"
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" int tid = get_global_id(0);\n"
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" destValues[tid] = %s( vload3( tid, (__global float*) sourceA), vload3( tid, (__global float*) sourceB) );\n"
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"\n"
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"}\n";
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const char *oneToFloatKernelPattern =
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"__kernel void sample_test(__global float%s *sourceA, __global float *destValues)\n"
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"{\n"
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" int tid = get_global_id(0);\n"
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" destValues[tid] = %s( sourceA[tid] );\n"
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"\n"
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"}\n";
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const char *oneToFloatKernelPatternV3 =
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"__kernel void sample_test(__global float%s *sourceA, __global float *destValues)\n"
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"{\n"
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" int tid = get_global_id(0);\n"
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" destValues[tid] = %s( vload3( tid, (__global float*) sourceA) );\n"
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"\n"
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"}\n";
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const char *oneToOneKernelPattern =
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"__kernel void sample_test(__global float%s *sourceA, __global float%s *destValues)\n"
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"{\n"
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" int tid = get_global_id(0);\n"
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" destValues[tid] = %s( sourceA[tid] );\n"
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"\n"
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"}\n";
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const char *oneToOneKernelPatternV3 =
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"__kernel void sample_test(__global float%s *sourceA, __global float%s *destValues)\n"
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"{\n"
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" int tid = get_global_id(0);\n"
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" vstore3( %s( vload3( tid, (__global float*) sourceA) ), tid, (__global float*) destValues );\n"
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"\n"
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"}\n";
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#define TEST_SIZE (1 << 20)
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double verifyFastDistance( float *srcA, float *srcB, size_t vecSize );
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double verifyFastLength( float *srcA, size_t vecSize );
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void vector2string( char *string, float *vector, size_t elements )
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{
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*string++ = '{';
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*string++ = ' ';
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string += sprintf( string, "%a", vector[0] );
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size_t i;
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for( i = 1; i < elements; i++ )
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string += sprintf( string, ", %a", vector[i] );
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*string++ = ' ';
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*string++ = '}';
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*string = '\0';
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}
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void fillWithTrickyNumbers( float *aVectors, float *bVectors, size_t vecSize )
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{
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static const cl_float trickyValues[] = { -FLT_EPSILON, FLT_EPSILON,
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MAKE_HEX_FLOAT(0x1.0p63f, 0x1L, 63), MAKE_HEX_FLOAT(0x1.8p63f, 0x18L, 59), MAKE_HEX_FLOAT(0x1.0p64f, 0x1L, 64), MAKE_HEX_FLOAT(-0x1.0p63f, -0x1L, 63), MAKE_HEX_FLOAT(-0x1.8p-63f, -0x18L, -67), MAKE_HEX_FLOAT(-0x1.0p64f, -0x1L, 64),
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MAKE_HEX_FLOAT(0x1.0p-63f, 0x1L, -63), MAKE_HEX_FLOAT(0x1.8p-63f, 0x18L, -67), MAKE_HEX_FLOAT(0x1.0p-64f, 0x1L, -64), MAKE_HEX_FLOAT(-0x1.0p-63f, -0x1L, -63), MAKE_HEX_FLOAT(-0x1.8p-63f, -0x18L, -67), MAKE_HEX_FLOAT(-0x1.0p-64f, -0x1L, -64),
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FLT_MAX / 2.f, -FLT_MAX / 2.f, INFINITY, -INFINITY, 0.f, -0.f };
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static const size_t trickyCount = sizeof( trickyValues ) / sizeof( trickyValues[0] );
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static const size_t stride[4] = {1, trickyCount, trickyCount*trickyCount, trickyCount*trickyCount*trickyCount };
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size_t i, j, k;
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for( j = 0; j < vecSize; j++ )
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for( k = 0; k < vecSize; k++ )
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for( i = 0; i < trickyCount; i++ )
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aVectors[ j + stride[j] * (i + k*trickyCount)*vecSize] = trickyValues[i];
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if( bVectors )
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{
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size_t copySize = vecSize * vecSize * trickyCount;
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memset( bVectors, 0, sizeof(float) * copySize );
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memset( aVectors + copySize, 0, sizeof(float) * copySize );
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memcpy( bVectors + copySize, aVectors, sizeof(float) * copySize );
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}
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}
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void cross_product( const float *vecA, const float *vecB, float *outVector, float *errorTolerances, float ulpTolerance )
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{
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outVector[ 0 ] = ( vecA[ 1 ] * vecB[ 2 ] ) - ( vecA[ 2 ] * vecB[ 1 ] );
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outVector[ 1 ] = ( vecA[ 2 ] * vecB[ 0 ] ) - ( vecA[ 0 ] * vecB[ 2 ] );
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outVector[ 2 ] = ( vecA[ 0 ] * vecB[ 1 ] ) - ( vecA[ 1 ] * vecB[ 0 ] );
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outVector[ 3 ] = 0.0f;
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errorTolerances[ 0 ] = fmaxf( fabsf( vecA[ 1 ] ), fmaxf( fabsf( vecB[ 2 ] ), fmaxf( fabsf( vecA[ 2 ] ), fabsf( vecB[ 1 ] ) ) ) );
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errorTolerances[ 1 ] = fmaxf( fabsf( vecA[ 2 ] ), fmaxf( fabsf( vecB[ 0 ] ), fmaxf( fabsf( vecA[ 0 ] ), fabsf( vecB[ 2 ] ) ) ) );
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errorTolerances[ 2 ] = fmaxf( fabsf( vecA[ 0 ] ), fmaxf( fabsf( vecB[ 1 ] ), fmaxf( fabsf( vecA[ 1 ] ), fabsf( vecB[ 0 ] ) ) ) );
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errorTolerances[ 0 ] = errorTolerances[ 0 ] * errorTolerances[ 0 ] * ( ulpTolerance * FLT_EPSILON ); // This gives us max squared times ulp tolerance, i.e. the worst-case expected variance we could expect from this result
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errorTolerances[ 1 ] = errorTolerances[ 1 ] * errorTolerances[ 1 ] * ( ulpTolerance * FLT_EPSILON );
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errorTolerances[ 2 ] = errorTolerances[ 2 ] * errorTolerances[ 2 ] * ( ulpTolerance * FLT_EPSILON );
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}
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int test_geom_cross(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements )
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{
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int vecsize;
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RandomSeed seed(gRandomSeed);
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/* Get the default rounding mode */
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cl_device_fp_config defaultRoundingMode = get_default_rounding_mode(deviceID);
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if( 0 == defaultRoundingMode )
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return -1;
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for(vecsize = 3; vecsize <= 4; ++vecsize)
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{
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clProgramWrapper program;
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clKernelWrapper kernel;
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clMemWrapper streams[3];
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BufferOwningPtr<cl_float> A(malloc(sizeof(cl_float) * TEST_SIZE * vecsize));
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BufferOwningPtr<cl_float> B(malloc(sizeof(cl_float) * TEST_SIZE * vecsize));
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BufferOwningPtr<cl_float> C(malloc(sizeof(cl_float) * TEST_SIZE * vecsize));
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cl_float testVector[4];
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int error, i;
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cl_float *inDataA = A;
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cl_float *inDataB = B;
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cl_float *outData = C;
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size_t threads[1], localThreads[1];
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/* Create kernels */
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if( create_single_kernel_helper( context, &program, &kernel, 1, vecsize == 3 ? &crossKernelSourceV3 : &crossKernelSource, "sample_test" ) )
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return -1;
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/* Generate some streams. Note: deliberately do some random data in w to verify that it gets ignored */
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for( i = 0; i < TEST_SIZE * vecsize; i++ )
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{
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inDataA[ i ] = get_random_float( -512.f, 512.f, seed );
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inDataB[ i ] = get_random_float( -512.f, 512.f, seed );
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}
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fillWithTrickyNumbers( inDataA, inDataB, vecsize );
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streams[0] = clCreateBuffer(context, CL_MEM_COPY_HOST_PTR,
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sizeof(cl_float) * vecsize * TEST_SIZE,
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inDataA, NULL);
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if( streams[0] == NULL )
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{
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log_error("ERROR: Creating input array A failed!\n");
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return -1;
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}
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streams[1] = clCreateBuffer(context, CL_MEM_COPY_HOST_PTR,
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sizeof(cl_float) * vecsize * TEST_SIZE,
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inDataB, NULL);
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if( streams[1] == NULL )
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{
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log_error("ERROR: Creating input array B failed!\n");
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return -1;
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}
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streams[2] =
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clCreateBuffer(context, CL_MEM_READ_WRITE,
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sizeof(cl_float) * vecsize * TEST_SIZE, NULL, NULL);
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if( streams[2] == NULL )
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{
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log_error("ERROR: Creating output array failed!\n");
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return -1;
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}
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/* Assign streams and execute */
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for( i = 0; i < 3; i++ )
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{
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error = clSetKernelArg(kernel, i, sizeof( streams[i] ), &streams[i]);
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test_error( error, "Unable to set indexed kernel arguments" );
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}
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/* Run the kernel */
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threads[0] = TEST_SIZE;
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error = get_max_common_work_group_size( context, kernel, threads[0], &localThreads[0] );
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test_error( error, "Unable to get work group size to use" );
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error = clEnqueueNDRangeKernel( queue, kernel, 1, NULL, threads, localThreads, 0, NULL, NULL );
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test_error( error, "Unable to execute test kernel" );
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/* Now get the results */
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error = clEnqueueReadBuffer( queue, streams[2], true, 0, sizeof( cl_float ) * TEST_SIZE * vecsize, outData, 0, NULL, NULL );
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test_error( error, "Unable to read output array!" );
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/* And verify! */
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for( i = 0; i < TEST_SIZE; i++ )
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{
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float errorTolerances[ 4 ];
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// On an embedded device w/ round-to-zero, 3 ulps is the worst-case tolerance for cross product
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cross_product( inDataA + i * vecsize, inDataB + i * vecsize, testVector, errorTolerances, 3.f );
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// RTZ devices accrue approximately double the amount of error per operation. Allow for that.
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if( defaultRoundingMode == CL_FP_ROUND_TO_ZERO )
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{
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errorTolerances[0] *= 2.0f;
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errorTolerances[1] *= 2.0f;
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errorTolerances[2] *= 2.0f;
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errorTolerances[3] *= 2.0f;
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}
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float errs[] = { fabsf( testVector[ 0 ] - outData[ i * vecsize + 0 ] ),
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fabsf( testVector[ 1 ] - outData[ i * vecsize + 1 ] ),
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fabsf( testVector[ 2 ] - outData[ i * vecsize + 2 ] ) };
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if( errs[ 0 ] > errorTolerances[ 0 ] || errs[ 1 ] > errorTolerances[ 1 ] || errs[ 2 ] > errorTolerances[ 2 ] )
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{
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log_error( "ERROR: Data sample %d does not validate! Expected (%a,%a,%a,%a), got (%a,%a,%a,%a)\n",
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i, testVector[0], testVector[1], testVector[2], testVector[3],
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outData[i*vecsize], outData[i*vecsize+1], outData[i*vecsize+2], outData[i*vecsize+3] );
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log_error( " Input: (%a %a %a) and (%a %a %a)\n",
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inDataA[ i * vecsize + 0 ], inDataA[ i * vecsize + 1 ], inDataA[ i * vecsize + 2 ],
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inDataB[ i * vecsize + 0 ], inDataB[ i * vecsize + 1 ], inDataB[ i * vecsize + 2 ] );
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log_error( " Errors: (%a out of %a), (%a out of %a), (%a out of %a)\n",
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errs[ 0 ], errorTolerances[ 0 ], errs[ 1 ], errorTolerances[ 1 ], errs[ 2 ], errorTolerances[ 2 ] );
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log_error(" ulp %f\n", Ulp_Error( outData[ i * vecsize + 1 ], testVector[ 1 ] ) );
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return -1;
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}
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}
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} // for(vecsize=...
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if(!is_extension_available(deviceID, "cl_khr_fp64")) {
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log_info("Extension cl_khr_fp64 not supported; skipping double tests.\n");
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return 0;
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} else {
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log_info("Testing doubles...\n");
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return test_geom_cross_double( deviceID, context, queue, num_elements, seed);
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}
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}
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float getMaxValue( float vecA[], float vecB[], size_t vecSize )
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{
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float a = fmaxf( fabsf( vecA[ 0 ] ), fabsf( vecB[ 0 ] ) );
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for( size_t i = 1; i < vecSize; i++ )
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a = fmaxf( fabsf( vecA[ i ] ), fmaxf( fabsf( vecB[ i ] ), a ) );
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return a;
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}
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typedef double (*twoToFloatVerifyFn)( float *srcA, float *srcB, size_t vecSize );
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int test_twoToFloat_kernel(cl_command_queue queue, cl_context context, const char *fnName,
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size_t vecSize, twoToFloatVerifyFn verifyFn, float ulpLimit, MTdata d )
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{
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clProgramWrapper program;
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clKernelWrapper kernel;
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clMemWrapper streams[3];
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int error;
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size_t i, threads[1], localThreads[1];
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char kernelSource[10240];
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char *programPtr;
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char sizeNames[][4] = { "", "2", "3", "4", "", "", "", "8", "", "", "", "", "", "", "", "16" };
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int hasInfNan = 1;
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cl_device_id device = NULL;
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error = clGetCommandQueueInfo( queue, CL_QUEUE_DEVICE, sizeof( device ), &device, NULL );
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test_error( error, "Unable to get command queue device" );
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/* Check for embedded devices doing nutty stuff */
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error = clGetDeviceInfo( device, CL_DEVICE_PROFILE, sizeof( kernelSource ), kernelSource, NULL );
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test_error( error, "Unable to get device profile" );
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if( 0 == strcmp( kernelSource, "EMBEDDED_PROFILE" ) )
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{
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cl_device_fp_config config = 0;
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error = clGetDeviceInfo( device, CL_DEVICE_SINGLE_FP_CONFIG, sizeof( config ), &config, NULL );
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test_error( error, "Unable to get CL_DEVICE_SINGLE_FP_CONFIG" );
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if( CL_FP_ROUND_TO_ZERO == (config & (CL_FP_ROUND_TO_NEAREST|CL_FP_ROUND_TO_ZERO)))
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ulpLimit *= 2.0f; // rtz operations average twice the accrued error of rte operations
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if( 0 == (config & CL_FP_INF_NAN) )
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hasInfNan = 0;
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}
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BufferOwningPtr<cl_float> A(malloc(sizeof(cl_float) * TEST_SIZE * 4));
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BufferOwningPtr<cl_float> B(malloc(sizeof(cl_float) * TEST_SIZE * 4));
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BufferOwningPtr<cl_float> C(malloc(sizeof(cl_float) * TEST_SIZE));
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cl_float *inDataA = A;
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cl_float *inDataB = B;
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cl_float *outData = C;
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/* Create the source */
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sprintf( kernelSource, vecSize == 3 ? twoToFloatKernelPatternV3 : twoToFloatKernelPattern, sizeNames[vecSize-1], sizeNames[vecSize-1], fnName );
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/* Create kernels */
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programPtr = kernelSource;
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if( create_single_kernel_helper( context, &program, &kernel, 1, (const char **)&programPtr, "sample_test" ) )
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{
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return -1;
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}
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/* Generate some streams */
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for( i = 0; i < TEST_SIZE * vecSize; i++ )
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{
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inDataA[ i ] = get_random_float( -512.f, 512.f, d );
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inDataB[ i ] = get_random_float( -512.f, 512.f, d );
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}
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fillWithTrickyNumbers( inDataA, inDataB, vecSize );
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/* Clamp values to be in range for fast_ functions */
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if( verifyFn == verifyFastDistance )
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{
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for( i = 0; i < TEST_SIZE * vecSize; i++ )
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{
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if( fabsf( inDataA[i] ) > MAKE_HEX_FLOAT(0x1.0p62f, 0x1L, 62) || fabsf( inDataA[i] ) < MAKE_HEX_FLOAT(0x1.0p-62f, 0x1L, -62) )
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inDataA[ i ] = get_random_float( -512.f, 512.f, d );
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if( fabsf( inDataB[i] ) > MAKE_HEX_FLOAT(0x1.0p62f, 0x1L, 62) || fabsf( inDataB[i] ) < MAKE_HEX_FLOAT(0x1.0p-62f, 0x1L, -62) )
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inDataB[ i ] = get_random_float( -512.f, 512.f, d );
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}
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}
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streams[0] =
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clCreateBuffer(context, CL_MEM_COPY_HOST_PTR,
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sizeof(cl_float) * vecSize * TEST_SIZE, inDataA, NULL);
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if( streams[0] == NULL )
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{
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log_error("ERROR: Creating input array A failed!\n");
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return -1;
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}
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streams[1] =
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clCreateBuffer(context, CL_MEM_COPY_HOST_PTR,
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sizeof(cl_float) * vecSize * TEST_SIZE, inDataB, NULL);
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if( streams[1] == NULL )
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{
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log_error("ERROR: Creating input array B failed!\n");
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return -1;
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}
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streams[2] = clCreateBuffer(context, CL_MEM_READ_WRITE,
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sizeof(cl_float) * TEST_SIZE, NULL, NULL);
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if( streams[2] == NULL )
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{
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log_error("ERROR: Creating output array failed!\n");
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return -1;
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}
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/* Assign streams and execute */
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for( i = 0; i < 3; i++ )
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{
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error = clSetKernelArg(kernel, (int)i, sizeof( streams[i] ), &streams[i]);
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test_error( error, "Unable to set indexed kernel arguments" );
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}
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/* Run the kernel */
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threads[0] = TEST_SIZE;
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|
|
error = get_max_common_work_group_size( context, kernel, threads[0], &localThreads[0] );
|
|
test_error( error, "Unable to get work group size to use" );
|
|
|
|
error = clEnqueueNDRangeKernel( queue, kernel, 1, NULL, threads, localThreads, 0, NULL, NULL );
|
|
test_error( error, "Unable to execute test kernel" );
|
|
|
|
/* Now get the results */
|
|
error = clEnqueueReadBuffer( queue, streams[2], true, 0, sizeof( cl_float ) * TEST_SIZE, outData, 0, NULL, NULL );
|
|
test_error( error, "Unable to read output array!" );
|
|
|
|
|
|
/* And verify! */
|
|
int skipCount = 0;
|
|
for( i = 0; i < TEST_SIZE; i++ )
|
|
{
|
|
cl_float *src1 = inDataA + i * vecSize;
|
|
cl_float *src2 = inDataB + i * vecSize;
|
|
double expected = verifyFn( src1, src2, vecSize );
|
|
if( (float) expected != outData[ i ] )
|
|
{
|
|
if( isnan(expected) && isnan( outData[i] ) )
|
|
continue;
|
|
|
|
if( ! hasInfNan )
|
|
{
|
|
size_t ii;
|
|
for( ii = 0; ii < vecSize; ii++ )
|
|
{
|
|
if( ! isfinite( src1[ii] ) || ! isfinite( src2[ii] ) )
|
|
{
|
|
skipCount++;
|
|
continue;
|
|
}
|
|
}
|
|
if( ! isfinite( (cl_float) expected ) )
|
|
{
|
|
skipCount++;
|
|
continue;
|
|
}
|
|
}
|
|
|
|
if( ulpLimit < 0 )
|
|
{
|
|
// Limit below zero means we need to test via a computed error (like cross product does)
|
|
float maxValue =
|
|
getMaxValue( inDataA + i * vecSize, inDataB + i * vecSize,vecSize );
|
|
// In this case (dot is the only one that gets here), the ulp is 2*vecSize - 1 (n + n-1 max # of errors)
|
|
float errorTolerance = maxValue * maxValue * ( 2.f * (float)vecSize - 1.f ) * FLT_EPSILON;
|
|
|
|
// Limit below zero means test via epsilon instead
|
|
double error =
|
|
fabs( (double)expected - (double)outData[ i ] );
|
|
if( error > errorTolerance )
|
|
{
|
|
|
|
log_error( "ERROR: Data sample %d at size %d does not validate! Expected (%a), got (%a), sources (%a and %a) error of %g against tolerance %g\n",
|
|
(int)i, (int)vecSize, expected,
|
|
outData[ i ],
|
|
inDataA[i*vecSize],
|
|
inDataB[i*vecSize],
|
|
(float)error,
|
|
(float)errorTolerance );
|
|
|
|
char vecA[1000], vecB[1000];
|
|
vector2string( vecA, inDataA +i * vecSize, vecSize );
|
|
vector2string( vecB, inDataB + i * vecSize, vecSize );
|
|
log_error( "\tvector A: %s, vector B: %s\n", vecA, vecB );
|
|
return -1;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
float error = Ulp_Error( outData[ i ], expected );
|
|
if( fabsf(error) > ulpLimit )
|
|
{
|
|
log_error( "ERROR: Data sample %d at size %d does not validate! Expected (%a), got (%a), sources (%a and %a) ulp of %f\n",
|
|
(int)i, (int)vecSize, expected, outData[ i ], inDataA[i*vecSize], inDataB[i*vecSize], error );
|
|
|
|
char vecA[1000], vecB[1000];
|
|
vector2string( vecA, inDataA + i * vecSize, vecSize );
|
|
vector2string( vecB, inDataB + i * vecSize, vecSize );
|
|
log_error( "\tvector A: %s, vector B: %s\n", vecA, vecB );
|
|
return -1;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if( skipCount )
|
|
log_info( "Skipped %d tests out of %d because they contained Infs or NaNs\n\tEMBEDDED_PROFILE Device does not support CL_FP_INF_NAN\n", skipCount, TEST_SIZE );
|
|
|
|
return 0;
|
|
}
|
|
|
|
double verifyDot( float *srcA, float *srcB, size_t vecSize )
|
|
{
|
|
double total = 0.f;
|
|
|
|
for( unsigned int i = 0; i < vecSize; i++ )
|
|
total += (double)srcA[ i ] * (double)srcB[ i ];
|
|
|
|
return total;
|
|
}
|
|
|
|
int test_geom_dot(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
|
|
{
|
|
size_t sizes[] = { 1, 2, 3, 4, 0 };
|
|
unsigned int size;
|
|
int retVal = 0;
|
|
RandomSeed seed(gRandomSeed);
|
|
|
|
for( size = 0; sizes[ size ] != 0 ; size++ )
|
|
{
|
|
if( test_twoToFloat_kernel( queue, context, "dot", sizes[size], verifyDot, -1.0f /*magic value*/, seed ) != 0 )
|
|
{
|
|
log_error( " dot vector size %d FAILED\n", (int)sizes[ size ] );
|
|
retVal = -1;
|
|
}
|
|
}
|
|
|
|
if (retVal)
|
|
return retVal;
|
|
|
|
if(!is_extension_available(deviceID, "cl_khr_fp64"))
|
|
{
|
|
log_info("Extension cl_khr_fp64 not supported; skipping double tests.\n");
|
|
return 0;
|
|
}
|
|
|
|
log_info("Testing doubles...\n");
|
|
return test_geom_dot_double( deviceID, context, queue, num_elements, seed);
|
|
}
|
|
|
|
double verifyFastDistance( float *srcA, float *srcB, size_t vecSize )
|
|
{
|
|
double total = 0, value;
|
|
unsigned int i;
|
|
|
|
// We calculate the distance as a double, to try and make up for the fact that
|
|
// the GPU has better precision distance since it's a single op
|
|
for( i = 0; i < vecSize; i++ )
|
|
{
|
|
value = (double)srcA[i] - (double)srcB[i];
|
|
total += value * value;
|
|
}
|
|
|
|
return sqrt( total );
|
|
}
|
|
|
|
int test_geom_fast_distance(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
|
|
{
|
|
size_t sizes[] = { 1, 2, 3, 4, 0 };
|
|
unsigned int size;
|
|
int retVal = 0;
|
|
RandomSeed seed(gRandomSeed);
|
|
|
|
for( size = 0; sizes[ size ] != 0 ; size++ )
|
|
{
|
|
float maxUlps = 8192.0f + // error in sqrt
|
|
( 1.5f * (float) sizes[size] + // cumulative error for multiplications (a-b+0.5ulp)**2 = (a-b)**2 + a*0.5ulp + b*0.5 ulp + 0.5 ulp for multiplication
|
|
0.5f * (float) (sizes[size]-1)); // cumulative error for additions
|
|
|
|
if( test_twoToFloat_kernel( queue, context, "fast_distance",
|
|
sizes[ size ], verifyFastDistance,
|
|
maxUlps, seed ) != 0 )
|
|
{
|
|
log_error( " fast_distance vector size %d FAILED\n",
|
|
(int)sizes[ size ] );
|
|
retVal = -1;
|
|
}
|
|
else
|
|
{
|
|
log_info( " fast_distance vector size %d passed\n",
|
|
(int)sizes[ size ] );
|
|
}
|
|
}
|
|
return retVal;
|
|
}
|
|
|
|
|
|
double verifyDistance( float *srcA, float *srcB, size_t vecSize )
|
|
{
|
|
double total = 0, value;
|
|
unsigned int i;
|
|
|
|
// We calculate the distance as a double, to try and make up for the fact that
|
|
// the GPU has better precision distance since it's a single op
|
|
for( i = 0; i < vecSize; i++ )
|
|
{
|
|
value = (double)srcA[i] - (double)srcB[i];
|
|
total += value * value;
|
|
}
|
|
|
|
return sqrt( total );
|
|
}
|
|
|
|
int test_geom_distance(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
|
|
{
|
|
size_t sizes[] = { 1, 2, 3, 4, 0 };
|
|
unsigned int size;
|
|
int retVal = 0;
|
|
RandomSeed seed(gRandomSeed );
|
|
|
|
for( size = 0; sizes[ size ] != 0 ; size++ )
|
|
{
|
|
float maxUlps = 3.0f + // error in sqrt
|
|
( 1.5f * (float) sizes[size] + // cumulative error for multiplications (a-b+0.5ulp)**2 = (a-b)**2 + a*0.5ulp + b*0.5 ulp + 0.5 ulp for multiplication
|
|
0.5f * (float) (sizes[size]-1)); // cumulative error for additions
|
|
|
|
if( test_twoToFloat_kernel( queue, context, "distance", sizes[ size ], verifyDistance, maxUlps, seed ) != 0 )
|
|
{
|
|
log_error( " distance vector size %d FAILED\n",
|
|
(int)sizes[ size ] );
|
|
retVal = -1;
|
|
}
|
|
else
|
|
{
|
|
log_info( " distance vector size %d passed\n", (int)sizes[ size ] );
|
|
}
|
|
}
|
|
if (retVal)
|
|
return retVal;
|
|
|
|
if(!is_extension_available(deviceID, "cl_khr_fp64"))
|
|
{
|
|
log_info("Extension cl_khr_fp64 not supported; skipping double tests.\n");
|
|
return 0;
|
|
} else {
|
|
log_info("Testing doubles...\n");
|
|
return test_geom_distance_double( deviceID, context, queue, num_elements, seed);
|
|
}
|
|
}
|
|
|
|
typedef double (*oneToFloatVerifyFn)( float *srcA, size_t vecSize );
|
|
|
|
int test_oneToFloat_kernel(cl_command_queue queue, cl_context context, const char *fnName,
|
|
size_t vecSize, oneToFloatVerifyFn verifyFn, float ulpLimit, MTdata d )
|
|
{
|
|
clProgramWrapper program;
|
|
clKernelWrapper kernel;
|
|
clMemWrapper streams[2];
|
|
BufferOwningPtr<cl_float> A(malloc(sizeof(cl_float) * TEST_SIZE * 4));
|
|
BufferOwningPtr<cl_float> B(malloc(sizeof(cl_float) * TEST_SIZE));
|
|
int error;
|
|
size_t i, threads[1], localThreads[1];
|
|
char kernelSource[10240];
|
|
char *programPtr;
|
|
char sizeNames[][4] = { "", "2", "3", "4", "", "", "", "8", "", "", "", "", "", "", "", "16" };
|
|
cl_float *inDataA = A;
|
|
cl_float *outData = B;
|
|
|
|
/* Create the source */
|
|
sprintf( kernelSource, vecSize == 3? oneToFloatKernelPatternV3 : oneToFloatKernelPattern, sizeNames[vecSize-1], fnName );
|
|
|
|
/* Create kernels */
|
|
programPtr = kernelSource;
|
|
if( create_single_kernel_helper( context, &program, &kernel, 1, (const char **)&programPtr, "sample_test" ) )
|
|
{
|
|
return -1;
|
|
}
|
|
|
|
/* Generate some streams */
|
|
for( i = 0; i < TEST_SIZE * vecSize; i++ )
|
|
{
|
|
inDataA[ i ] = get_random_float( -512.f, 512.f, d );
|
|
}
|
|
fillWithTrickyNumbers( inDataA, NULL, vecSize );
|
|
|
|
/* Clamp values to be in range for fast_ functions */
|
|
if( verifyFn == verifyFastLength )
|
|
{
|
|
for( i = 0; i < TEST_SIZE * vecSize; i++ )
|
|
{
|
|
if( fabsf( inDataA[i] ) > MAKE_HEX_FLOAT(0x1.0p62f, 0x1L, 62) || fabsf( inDataA[i] ) < MAKE_HEX_FLOAT(0x1.0p-62f, 0x1L, -62) )
|
|
inDataA[ i ] = get_random_float( -512.f, 512.f, d );
|
|
}
|
|
}
|
|
|
|
streams[0] =
|
|
clCreateBuffer(context, CL_MEM_COPY_HOST_PTR,
|
|
sizeof(cl_float) * vecSize * TEST_SIZE, inDataA, NULL);
|
|
if( streams[0] == NULL )
|
|
{
|
|
log_error("ERROR: Creating input array A failed!\n");
|
|
return -1;
|
|
}
|
|
streams[1] = clCreateBuffer(context, CL_MEM_READ_WRITE,
|
|
sizeof(cl_float) * TEST_SIZE, NULL, NULL);
|
|
if( streams[1] == NULL )
|
|
{
|
|
log_error("ERROR: Creating output array failed!\n");
|
|
return -1;
|
|
}
|
|
|
|
/* Assign streams and execute */
|
|
error = clSetKernelArg( kernel, 0, sizeof( streams[ 0 ] ), &streams[0] );
|
|
test_error( error, "Unable to set indexed kernel arguments" );
|
|
error = clSetKernelArg( kernel, 1, sizeof( streams[ 1 ] ), &streams[1] );
|
|
test_error( error, "Unable to set indexed kernel arguments" );
|
|
|
|
/* Run the kernel */
|
|
threads[0] = TEST_SIZE;
|
|
|
|
error = get_max_common_work_group_size( context, kernel, threads[0],
|
|
&localThreads[0] );
|
|
test_error( error, "Unable to get work group size to use" );
|
|
|
|
error = clEnqueueNDRangeKernel( queue, kernel, 1, NULL, threads,
|
|
localThreads, 0, NULL, NULL );
|
|
test_error( error, "Unable to execute test kernel" );
|
|
|
|
/* Now get the results */
|
|
error = clEnqueueReadBuffer( queue, streams[1], true, 0,
|
|
sizeof( cl_float ) * TEST_SIZE, outData,
|
|
0, NULL, NULL );
|
|
test_error( error, "Unable to read output array!" );
|
|
|
|
/* And verify! */
|
|
for( i = 0; i < TEST_SIZE; i++ )
|
|
{
|
|
double expected = verifyFn( inDataA + i * vecSize, vecSize );
|
|
if( (float) expected != outData[ i ] )
|
|
{
|
|
float ulps = Ulp_Error( outData[i], expected );
|
|
if( fabsf( ulps ) <= ulpLimit )
|
|
continue;
|
|
|
|
// We have to special case NAN
|
|
if( isnan( outData[ i ] ) && isnan( expected ) )
|
|
continue;
|
|
|
|
if(! (fabsf(ulps) < ulpLimit) )
|
|
{
|
|
log_error( "ERROR: Data sample %d at size %d does not validate! Expected (%a), got (%a), source (%a), ulp %f\n",
|
|
(int)i, (int)vecSize, expected, outData[ i ], inDataA[i*vecSize], ulps );
|
|
char vecA[1000];
|
|
vector2string( vecA, inDataA + i *vecSize, vecSize );
|
|
log_error( "\tvector: %s", vecA );
|
|
return -1;
|
|
}
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
double verifyLength( float *srcA, size_t vecSize )
|
|
{
|
|
double total = 0;
|
|
unsigned int i;
|
|
|
|
// We calculate the distance as a double, to try and make up for the fact that
|
|
// the GPU has better precision distance since it's a single op
|
|
for( i = 0; i < vecSize; i++ )
|
|
{
|
|
total += (double)srcA[i] * (double)srcA[i];
|
|
}
|
|
|
|
return sqrt( total );
|
|
}
|
|
|
|
int test_geom_length(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
|
|
{
|
|
size_t sizes[] = { 1, 2, 3, 4, 0 };
|
|
unsigned int size;
|
|
int retVal = 0;
|
|
RandomSeed seed( gRandomSeed );
|
|
|
|
for( size = 0; sizes[ size ] != 0 ; size++ )
|
|
{
|
|
float maxUlps = 3.0f + // error in sqrt
|
|
0.5f * // effect on e of taking sqrt( x + e )
|
|
( 0.5f * (float) sizes[size] + // cumulative error for multiplications
|
|
0.5f * (float) (sizes[size]-1)); // cumulative error for additions
|
|
|
|
if( test_oneToFloat_kernel( queue, context, "length", sizes[ size ], verifyLength, maxUlps, seed ) != 0 )
|
|
{
|
|
log_error( " length vector size %d FAILED\n", (int)sizes[ size ] );
|
|
retVal = -1;
|
|
}
|
|
else
|
|
{
|
|
log_info( " length vector vector size %d passed\n", (int)sizes[ size ] );
|
|
}
|
|
}
|
|
if (retVal)
|
|
return retVal;
|
|
|
|
if(!is_extension_available(deviceID, "cl_khr_fp64"))
|
|
{
|
|
log_info("Extension cl_khr_fp64 not supported; skipping double tests.\n");
|
|
return 0;
|
|
}
|
|
else
|
|
{
|
|
log_info("Testing doubles...\n");
|
|
return test_geom_length_double( deviceID, context, queue, num_elements, seed);
|
|
}
|
|
}
|
|
|
|
|
|
double verifyFastLength( float *srcA, size_t vecSize )
|
|
{
|
|
double total = 0;
|
|
unsigned int i;
|
|
|
|
// We calculate the distance as a double, to try and make up for the fact that
|
|
// the GPU has better precision distance since it's a single op
|
|
for( i = 0; i < vecSize; i++ )
|
|
{
|
|
total += (double)srcA[i] * (double)srcA[i];
|
|
}
|
|
|
|
return sqrt( total );
|
|
}
|
|
|
|
int test_geom_fast_length(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
|
|
{
|
|
size_t sizes[] = { 1, 2, 3, 4, 0 };
|
|
unsigned int size;
|
|
int retVal = 0;
|
|
RandomSeed seed(gRandomSeed);
|
|
|
|
for( size = 0; sizes[ size ] != 0 ; size++ )
|
|
{
|
|
float maxUlps = 8192.0f + // error in half_sqrt
|
|
( 0.5f * (float) sizes[size] + // cumulative error for multiplications
|
|
0.5f * (float) (sizes[size]-1)); // cumulative error for additions
|
|
|
|
if( test_oneToFloat_kernel( queue, context, "fast_length", sizes[ size ], verifyFastLength, maxUlps, seed ) != 0 )
|
|
{
|
|
log_error( " fast_length vector size %d FAILED\n", (int)sizes[ size ] );
|
|
retVal = -1;
|
|
}
|
|
else
|
|
{
|
|
log_info( " fast_length vector size %d passed\n", (int)sizes[ size ] );
|
|
}
|
|
}
|
|
return retVal;
|
|
}
|
|
|
|
|
|
typedef void (*oneToOneVerifyFn)( float *srcA, float *dstA, size_t vecSize );
|
|
|
|
|
|
int test_oneToOne_kernel(cl_command_queue queue, cl_context context, const char *fnName,
|
|
size_t vecSize, oneToOneVerifyFn verifyFn, float ulpLimit, int softball, MTdata d )
|
|
{
|
|
clProgramWrapper program;
|
|
clKernelWrapper kernel;
|
|
clMemWrapper streams[2];
|
|
BufferOwningPtr<cl_float> A(malloc(sizeof(cl_float) * TEST_SIZE
|
|
* vecSize));
|
|
BufferOwningPtr<cl_float> B(malloc(sizeof(cl_float) * TEST_SIZE
|
|
* vecSize));
|
|
int error;
|
|
size_t i, j, threads[1], localThreads[1];
|
|
char kernelSource[10240];
|
|
char *programPtr;
|
|
char sizeNames[][4] = { "", "2", "3", "4", "", "", "", "8", "", "", "", "", "", "", "", "16" };
|
|
cl_float *inDataA = A;
|
|
cl_float *outData = B;
|
|
float ulp_error = 0;
|
|
|
|
/* Create the source */
|
|
sprintf( kernelSource, vecSize == 3 ? oneToOneKernelPatternV3: oneToOneKernelPattern, sizeNames[vecSize-1], sizeNames[vecSize-1], fnName );
|
|
|
|
/* Create kernels */
|
|
programPtr = kernelSource;
|
|
if( create_single_kernel_helper( context, &program, &kernel, 1, (const char **)&programPtr, "sample_test" ) )
|
|
return -1;
|
|
|
|
/* Initialize data. First element always 0. */
|
|
memset( inDataA, 0, sizeof(cl_float) * vecSize );
|
|
if( 0 == strcmp( fnName, "fast_normalize" ))
|
|
{ // keep problematic cases out of the fast function
|
|
for( i = vecSize; i < TEST_SIZE * vecSize; i++ )
|
|
{
|
|
cl_float z = get_random_float( -MAKE_HEX_FLOAT( 0x1.0p60f, 1, 60), MAKE_HEX_FLOAT( 0x1.0p60f, 1, 60), d);
|
|
if( fabsf(z) < MAKE_HEX_FLOAT( 0x1.0p-60f, 1, -60) )
|
|
z = copysignf( 0.0f, z );
|
|
inDataA[i] = z;
|
|
}
|
|
}
|
|
else
|
|
{
|
|
for( i = vecSize; i < TEST_SIZE * vecSize; i++ )
|
|
inDataA[i] = any_float(d);
|
|
}
|
|
|
|
streams[0] =
|
|
clCreateBuffer(context, CL_MEM_COPY_HOST_PTR,
|
|
sizeof(cl_float) * vecSize * TEST_SIZE, inDataA, NULL);
|
|
if( streams[0] == NULL )
|
|
{
|
|
log_error("ERROR: Creating input array A failed!\n");
|
|
return -1;
|
|
}
|
|
streams[1] =
|
|
clCreateBuffer(context, CL_MEM_READ_WRITE,
|
|
sizeof(cl_float) * vecSize * TEST_SIZE, NULL, NULL);
|
|
if( streams[1] == NULL )
|
|
{
|
|
log_error("ERROR: Creating output array failed!\n");
|
|
return -1;
|
|
}
|
|
|
|
/* Assign streams and execute */
|
|
error = clSetKernelArg(kernel, 0, sizeof( streams[0] ), &streams[0] );
|
|
test_error( error, "Unable to set indexed kernel arguments" );
|
|
error = clSetKernelArg(kernel, 1, sizeof( streams[1] ), &streams[1] );
|
|
test_error( error, "Unable to set indexed kernel arguments" );
|
|
|
|
/* Run the kernel */
|
|
threads[0] = TEST_SIZE;
|
|
|
|
error = get_max_common_work_group_size( context, kernel, threads[0], &localThreads[0] );
|
|
test_error( error, "Unable to get work group size to use" );
|
|
|
|
error = clEnqueueNDRangeKernel( queue, kernel, 1, NULL, threads, localThreads, 0, NULL, NULL );
|
|
test_error( error, "Unable to execute test kernel" );
|
|
|
|
/* Now get the results */
|
|
error = clEnqueueReadBuffer( queue, streams[1], true, 0, sizeof( cl_float ) * TEST_SIZE * vecSize, outData, 0, NULL, NULL );
|
|
test_error( error, "Unable to read output array!" );
|
|
|
|
/* And verify! */
|
|
for( i = 0; i < TEST_SIZE; i++ )
|
|
{
|
|
float expected[4];
|
|
int fail = 0;
|
|
verifyFn( inDataA + i * vecSize, expected, vecSize );
|
|
for( j = 0; j < vecSize; j++ )
|
|
{
|
|
// We have to special case NAN
|
|
if( isnan( outData[ i * vecSize + j ] )
|
|
&& isnan( expected[ j ] ) )
|
|
continue;
|
|
|
|
if( expected[j] != outData[ i * vecSize + j ] ) {
|
|
ulp_error = Ulp_Error( outData[i*vecSize+j], expected[ j ] );
|
|
|
|
if( fabsf(ulp_error) > ulpLimit ) {
|
|
fail = 1;
|
|
break;
|
|
}
|
|
}
|
|
|
|
}
|
|
|
|
// try again with subnormals flushed to zero if the platform flushes
|
|
if( fail && gFlushDenormsToZero )
|
|
{
|
|
float temp[4], expected2[4];
|
|
for( j = 0; j < vecSize; j++ )
|
|
{
|
|
if( IsFloatSubnormal(inDataA[i*vecSize+j] ) )
|
|
temp[j] = copysignf( 0.0f, inDataA[i*vecSize+j] );
|
|
else
|
|
temp[j] = inDataA[ i*vecSize +j];
|
|
}
|
|
|
|
verifyFn( temp, expected2, vecSize );
|
|
fail = 0;
|
|
|
|
for( j = 0; j < vecSize; j++ )
|
|
{
|
|
// We have to special case NAN
|
|
if( isnan( outData[ i * vecSize + j ] ) && isnan( expected[ j ] ) )
|
|
continue;
|
|
|
|
if( expected2[j] != outData[ i * vecSize + j ] )
|
|
{
|
|
ulp_error = Ulp_Error(outData[i*vecSize + j ], expected[ j ] );
|
|
|
|
if( fabsf(ulp_error) > ulpLimit )
|
|
{
|
|
if( IsFloatSubnormal(expected2[j]) )
|
|
{
|
|
expected2[j] = 0.0f;
|
|
if( expected2[j] != outData[i*vecSize + j ] )
|
|
{
|
|
ulp_error = Ulp_Error( outData[ i * vecSize + j ], expected[ j ] );
|
|
if( fabsf(ulp_error) > ulpLimit ) {
|
|
fail = 1;
|
|
break;
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
}
|
|
|
|
if( fail )
|
|
{
|
|
log_error( "ERROR: Data sample {%d,%d} at size %d does not validate! Expected %12.24f (%a), got %12.24f (%a), ulp %f\n",
|
|
(int)i, (int)j, (int)vecSize, expected[j], expected[j], outData[ i*vecSize+j], outData[ i*vecSize+j], ulp_error );
|
|
log_error( " Source: " );
|
|
for( size_t q = 0; q < vecSize; q++ )
|
|
log_error( "%g ", inDataA[ i * vecSize+q]);
|
|
log_error( "\n : " );
|
|
for( size_t q = 0; q < vecSize; q++ )
|
|
log_error( "%a ", inDataA[i*vecSize +q] );
|
|
log_error( "\n" );
|
|
log_error( " Result: " );
|
|
for( size_t q = 0; q < vecSize; q++ )
|
|
log_error( "%g ", outData[ i *vecSize + q ] );
|
|
log_error( "\n : " );
|
|
for( size_t q = 0; q < vecSize; q++ )
|
|
log_error( "%a ", outData[ i * vecSize + q ] );
|
|
log_error( "\n" );
|
|
log_error( " Expected: " );
|
|
for( size_t q = 0; q < vecSize; q++ )
|
|
log_error( "%g ", expected[ q ] );
|
|
log_error( "\n : " );
|
|
for( size_t q = 0; q < vecSize; q++ )
|
|
log_error( "%a ", expected[ q ] );
|
|
log_error( "\n" );
|
|
return -1;
|
|
}
|
|
}
|
|
|
|
return 0;
|
|
}
|
|
|
|
void verifyNormalize( float *srcA, float *dst, size_t vecSize )
|
|
{
|
|
double total = 0, value;
|
|
unsigned int i;
|
|
|
|
// We calculate everything as a double, to try and make up for the fact that
|
|
// the GPU has better precision distance since it's a single op
|
|
for( i = 0; i < vecSize; i++ )
|
|
total += (double)srcA[i] * (double)srcA[i];
|
|
|
|
if( total == 0.f )
|
|
{
|
|
// Special edge case: copy vector over without change
|
|
for( i = 0; i < vecSize; i++ )
|
|
dst[i] = srcA[i];
|
|
return;
|
|
}
|
|
|
|
// Deal with infinities
|
|
if( total == INFINITY )
|
|
{
|
|
total = 0.0f;
|
|
for( i = 0; i < vecSize; i++ )
|
|
{
|
|
if( fabsf( srcA[i]) == INFINITY )
|
|
dst[i] = copysignf( 1.0f, srcA[i] );
|
|
else
|
|
dst[i] = copysignf( 0.0f, srcA[i] );
|
|
total += (double)dst[i] * (double)dst[i];
|
|
}
|
|
|
|
srcA = dst;
|
|
}
|
|
|
|
value = sqrt( total );
|
|
for( i = 0; i < vecSize; i++ )
|
|
dst[i] = (float)( (double)srcA[i] / value );
|
|
}
|
|
|
|
int test_geom_normalize(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
|
|
{
|
|
size_t sizes[] = { 1, 2, 3, 4, 0 };
|
|
unsigned int size;
|
|
int retVal = 0;
|
|
RandomSeed seed(gRandomSeed);
|
|
|
|
for( size = 0; sizes[ size ] != 0 ; size++ )
|
|
{
|
|
float maxUlps = 2.5f + // error in rsqrt + error in multiply
|
|
( 0.5f * (float) sizes[size] + // cumulative error for multiplications
|
|
0.5f * (float) (sizes[size]-1)); // cumulative error for additions
|
|
if( test_oneToOne_kernel( queue, context, "normalize", sizes[ size ], verifyNormalize, maxUlps, 0, seed ) != 0 )
|
|
{
|
|
log_error( " normalized vector size %d FAILED\n", (int)sizes[ size ] );
|
|
retVal = -1;
|
|
}
|
|
else
|
|
{
|
|
log_info( " normalized vector size %d passed\n", (int)sizes[ size ] );
|
|
}
|
|
}
|
|
if (retVal)
|
|
return retVal;
|
|
|
|
if(!is_extension_available(deviceID, "cl_khr_fp64"))
|
|
{
|
|
log_info("Extension cl_khr_fp64 not supported; skipping double tests.\n");
|
|
return 0;
|
|
} else {
|
|
log_info("Testing doubles...\n");
|
|
return test_geom_normalize_double( deviceID, context, queue, num_elements, seed);
|
|
}
|
|
}
|
|
|
|
|
|
int test_geom_fast_normalize(cl_device_id deviceID, cl_context context, cl_command_queue queue, int num_elements)
|
|
{
|
|
size_t sizes[] = { 1, 2, 3, 4, 0 };
|
|
unsigned int size;
|
|
int retVal = 0;
|
|
RandomSeed seed( gRandomSeed );
|
|
|
|
for( size = 0; sizes[ size ] != 0 ; size++ )
|
|
{
|
|
float maxUlps = 8192.5f + // error in rsqrt + error in multiply
|
|
( 0.5f * (float) sizes[size] + // cumulative error for multiplications
|
|
0.5f * (float) (sizes[size]-1)); // cumulative error for additions
|
|
|
|
if( test_oneToOne_kernel( queue, context, "fast_normalize", sizes[ size ], verifyNormalize, maxUlps, 1, seed ) != 0 )
|
|
{
|
|
log_error( " fast_normalize vector size %d FAILED\n", (int)sizes[ size ] );
|
|
retVal = -1;
|
|
}
|
|
else
|
|
{
|
|
log_info( " fast_normalize vector size %d passed\n", (int)sizes[ size ] );
|
|
}
|
|
}
|
|
return retVal;
|
|
}
|
|
|
|
|
|
|