blob: 91bc92d9bc437bf03e5d340583097927e5f92000 [file] [log] [blame]
//
// Copyright (c) 2017 The Khronos Group Inc.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
//
#include "Utility.h"
#include <string.h>
#include "FunctionList.h"
#if defined( __APPLE__ )
#include <sys/time.h>
#endif
int TestFunc_Float_Float(const Func *f, MTdata, bool relaxedMode);
int TestFunc_Double_Double(const Func *f, MTdata, bool relaxedMode);
extern const vtbl _unary = { "unary", TestFunc_Float_Float,
TestFunc_Double_Double };
static int BuildKernel(const char *name, int vectorSize, cl_uint kernel_count,
cl_kernel *k, cl_program *p, bool relaxedMode);
static int BuildKernelDouble(const char *name, int vectorSize,
cl_uint kernel_count, cl_kernel *k, cl_program *p,
bool relaxedMode);
static int BuildKernel(const char *name, int vectorSize, cl_uint kernel_count,
cl_kernel *k, cl_program *p, bool relaxedMode)
{
const char *c[] = {
"__kernel void math_kernel", sizeNames[vectorSize], "( __global float", sizeNames[vectorSize], "* out, __global float", sizeNames[vectorSize], "* in)\n"
"{\n"
" int i = get_global_id(0);\n"
" out[i] = ", name, "( in[i] );\n"
"}\n"
};
const char *c3[] = { "__kernel void math_kernel", sizeNames[vectorSize], "( __global float* out, __global float* in)\n"
"{\n"
" size_t i = get_global_id(0);\n"
" if( i + 1 < get_global_size(0) )\n"
" {\n"
" float3 f0 = vload3( 0, in + 3 * i );\n"
" f0 = ", name, "( f0 );\n"
" vstore3( f0, 0, out + 3*i );\n"
" }\n"
" else\n"
" {\n"
" size_t parity = i & 1; // Figure out how many elements are left over after BUFFER_SIZE % (3*sizeof(float)). Assume power of two buffer size \n"
" float3 f0;\n"
" switch( parity )\n"
" {\n"
" case 1:\n"
" f0 = (float3)( in[3*i], NAN, NAN ); \n"
" break;\n"
" case 0:\n"
" f0 = (float3)( in[3*i], in[3*i+1], NAN ); \n"
" break;\n"
" }\n"
" f0 = ", name, "( f0 );\n"
" switch( parity )\n"
" {\n"
" case 0:\n"
" out[3*i+1] = f0.y; \n"
" // fall through\n"
" case 1:\n"
" out[3*i] = f0.x; \n"
" break;\n"
" }\n"
" }\n"
"}\n"
};
const char **kern = c;
size_t kernSize = sizeof(c)/sizeof(c[0]);
if( sizeValues[vectorSize] == 3 )
{
kern = c3;
kernSize = sizeof(c3)/sizeof(c3[0]);
}
char testName[32];
snprintf( testName, sizeof( testName ) -1, "math_kernel%s", sizeNames[vectorSize] );
return MakeKernels(kern, (cl_uint)kernSize, testName, kernel_count, k, p,
relaxedMode);
}
static int BuildKernelDouble(const char *name, int vectorSize,
cl_uint kernel_count, cl_kernel *k, cl_program *p,
bool relaxedMode)
{
const char *c[] = { "#pragma OPENCL EXTENSION cl_khr_fp64 : enable\n",
"__kernel void math_kernel", sizeNames[vectorSize], "( __global double", sizeNames[vectorSize], "* out, __global double", sizeNames[vectorSize], "* in)\n"
"{\n"
" int i = get_global_id(0);\n"
" out[i] = ", name, "( in[i] );\n"
"}\n"
};
const char *c3[] = { "#pragma OPENCL EXTENSION cl_khr_fp64 : enable\n",
"__kernel void math_kernel", sizeNames[vectorSize], "( __global double* out, __global double* in)\n"
"{\n"
" size_t i = get_global_id(0);\n"
" if( i + 1 < get_global_size(0) )\n"
" {\n"
" double3 f0 = vload3( 0, in + 3 * i );\n"
" f0 = ", name, "( f0 );\n"
" vstore3( f0, 0, out + 3*i );\n"
" }\n"
" else\n"
" {\n"
" size_t parity = i & 1; // Figure out how many elements are left over after BUFFER_SIZE % (3*sizeof(float)). Assume power of two buffer size \n"
" double3 f0;\n"
" switch( parity )\n"
" {\n"
" case 1:\n"
" f0 = (double3)( in[3*i], NAN, NAN ); \n"
" break;\n"
" case 0:\n"
" f0 = (double3)( in[3*i], in[3*i+1], NAN ); \n"
" break;\n"
" }\n"
" f0 = ", name, "( f0 );\n"
" switch( parity )\n"
" {\n"
" case 0:\n"
" out[3*i+1] = f0.y; \n"
" // fall through\n"
" case 1:\n"
" out[3*i] = f0.x; \n"
" break;\n"
" }\n"
" }\n"
"}\n"
};
const char **kern = c;
size_t kernSize = sizeof(c)/sizeof(c[0]);
if( sizeValues[vectorSize] == 3 )
{
kern = c3;
kernSize = sizeof(c3)/sizeof(c3[0]);
}
char testName[32];
snprintf( testName, sizeof( testName ) -1, "math_kernel%s", sizeNames[vectorSize] );
return MakeKernels(kern, (cl_uint)kernSize, testName, kernel_count, k, p,
relaxedMode);
}
typedef struct BuildKernelInfo
{
cl_uint offset; // the first vector size to build
cl_uint kernel_count;
cl_kernel **kernels;
cl_program *programs;
const char *nameInCode;
bool relaxedMode; // Whether to build with -cl-fast-relaxed-math.
}BuildKernelInfo;
static cl_int BuildKernel_FloatFn( cl_uint job_id, cl_uint thread_id UNUSED, void *p );
static cl_int BuildKernel_FloatFn( cl_uint job_id, cl_uint thread_id UNUSED, void *p )
{
BuildKernelInfo *info = (BuildKernelInfo*) p;
cl_uint i = info->offset + job_id;
return BuildKernel(info->nameInCode, i, info->kernel_count,
info->kernels[i], info->programs + i, info->relaxedMode);
}
static cl_int BuildKernel_DoubleFn( cl_uint job_id, cl_uint thread_id UNUSED, void *p );
static cl_int BuildKernel_DoubleFn( cl_uint job_id, cl_uint thread_id UNUSED, void *p )
{
BuildKernelInfo *info = (BuildKernelInfo*) p;
cl_uint i = info->offset + job_id;
return BuildKernelDouble(info->nameInCode, i, info->kernel_count,
info->kernels[i], info->programs + i,
info->relaxedMode);
}
//Thread specific data for a worker thread
typedef struct ThreadInfo
{
cl_mem inBuf; // input buffer for the thread
cl_mem outBuf[ VECTOR_SIZE_COUNT ]; // output buffers for the thread
float maxError; // max error value. Init to 0.
double maxErrorValue; // position of the max error value. Init to 0.
cl_command_queue tQueue; // per thread command queue to improve performance
}ThreadInfo;
typedef struct TestInfo
{
size_t subBufferSize; // Size of the sub-buffer in elements
const Func *f; // A pointer to the function info
cl_program programs[ VECTOR_SIZE_COUNT ]; // programs for various vector sizes
cl_kernel *k[VECTOR_SIZE_COUNT ]; // arrays of thread-specific kernels for each worker thread: k[vector_size][thread_id]
ThreadInfo *tinfo; // An array of thread specific information for each worker thread
cl_uint threadCount; // Number of worker threads
cl_uint jobCount; // Number of jobs
cl_uint step; // step between each chunk and the next.
cl_uint scale; // stride between individual test values
float ulps; // max_allowed ulps
int ftz; // non-zero if running in flush to zero mode
int isRangeLimited; // 1 if the function is only to be evaluated over a range
float half_sin_cos_tan_limit;
bool relaxedMode; // True if test is to be run in relaxed mode, false
// otherwise.
}TestInfo;
static cl_int TestFloat( cl_uint job_id, cl_uint thread_id, void *p );
int TestFunc_Float_Float(const Func *f, MTdata d, bool relaxedMode)
{
TestInfo test_info;
cl_int error;
size_t i, j;
float maxError = 0.0f;
double maxErrorVal = 0.0;
int skipTestingRelaxed = (relaxedMode && strcmp(f->name, "tan") == 0);
logFunctionInfo(f->name, sizeof(cl_float), relaxedMode);
// Init test_info
memset( &test_info, 0, sizeof( test_info ) );
test_info.threadCount = GetThreadCount();
test_info.subBufferSize = BUFFER_SIZE / (sizeof( cl_float) * RoundUpToNextPowerOfTwo(test_info.threadCount));
test_info.scale = 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.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, &region, &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, &region, &error);
if( error || NULL == test_info.tinfo[i].outBuf[j] )
{
vlog_error( "Error: Unable to create sub-buffer of gInBuffer for region {%zd, %zd}\n", region.origin, region.size );
goto exit;
}
}
test_info.tinfo[i].tQueue = clCreateCommandQueue(gContext, gDevice, 0, &error);
if( NULL == test_info.tinfo[i].tQueue || error )
{
vlog_error( "clCreateCommandQueue failed. (%d)\n", error );
goto exit;
}
}
// Check for special cases for unary float
test_info.isRangeLimited = 0;
test_info.half_sin_cos_tan_limit = 0;
if( 0 == strcmp( f->name, "half_sin") || 0 == strcmp( f->name, "half_cos") )
{
test_info.isRangeLimited = 1;
test_info.half_sin_cos_tan_limit = 1.0f + test_info.ulps * (FLT_EPSILON/2.0f); // out of range results from finite inputs must be in [-1,1]
}
else if( 0 == strcmp( f->name, "half_tan"))
{
test_info.isRangeLimited = 1;
test_info.half_sin_cos_tan_limit = INFINITY; // out of range resut from finite inputs must be numeric
}
// Init the kernels
{
BuildKernelInfo build_info = {
gMinVectorSizeIndex, test_info.threadCount, test_info.k,
test_info.programs, f->nameInCode, relaxedMode
};
if( (error = ThreadPool_Do( BuildKernel_FloatFn, gMaxVectorSizeIndex - gMinVectorSizeIndex, &build_info ) ))
goto exit;
}
if( !gSkipCorrectnessTesting || skipTestingRelaxed)
{
error = ThreadPool_Do( TestFloat, test_info.jobCount, &test_info );
// Accumulate the arithmetic errors
for( i = 0; i < test_info.threadCount; i++ )
{
if( test_info.tinfo[i].maxError > maxError )
{
maxError = test_info.tinfo[i].maxError;
maxErrorVal = test_info.tinfo[i].maxErrorValue;
}
}
if( error )
goto exit;
if( gWimpyMode )
vlog( "Wimp pass" );
else
vlog( "passed" );
if( skipTestingRelaxed )
{
vlog(" (rlx skip correctness testing)\n");
goto exit;
}
}
if( gMeasureTimes )
{
//Init input array
uint32_t *p = (uint32_t *)gIn;
if( strstr( f->name, "exp" ) || strstr( f->name, "sin" ) || strstr( f->name, "cos" ) || strstr( f->name, "tan" ) )
for( j = 0; j < BUFFER_SIZE / sizeof( float ); j++ )
((float*)p)[j] = (float) genrand_real1(d);
else if( strstr( f->name, "log" ) )
for( j = 0; j < BUFFER_SIZE / sizeof( float ); j++ )
p[j] = genrand_int32(d) & 0x7fffffff;
else
for( j = 0; j < BUFFER_SIZE / sizeof( float ); j++ )
p[j] = genrand_int32(d);
if( (error = clEnqueueWriteBuffer(gQueue, gInBuffer, CL_FALSE, 0, BUFFER_SIZE, gIn, 0, NULL, NULL) ))
{
vlog_error( "\n*** Error %d in clEnqueueWriteBuffer ***\n", error );
return error;
}
// Run the kernels
for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ )
{
size_t vectorSize = sizeValues[j] * sizeof(cl_float);
size_t localCount = (BUFFER_SIZE + vectorSize - 1) / vectorSize;
if( ( error = clSetKernelArg( test_info.k[j][0], 0, sizeof( gOutBuffer[j] ), &gOutBuffer[j] ) )) { LogBuildError( test_info.programs[j]); goto exit; }
if( ( error = clSetKernelArg( test_info.k[j][0], 1, sizeof( gInBuffer ), &gInBuffer ) )) { LogBuildError(test_info.programs[j]); goto exit; }
double sum = 0.0;
double bestTime = INFINITY;
for( i = 0; i < PERF_LOOP_COUNT; i++ )
{
uint64_t startTime = GetTime();
if( (error = clEnqueueNDRangeKernel(gQueue, test_info.k[j][0], 1, NULL, &localCount, NULL, 0, NULL, NULL)) )
{
vlog_error( "FAILED -- could not execute kernel\n" );
goto exit;
}
// Make sure OpenCL is done
if( (error = clFinish(gQueue) ) )
{
vlog_error( "Error %d at clFinish\n", error );
goto exit;
}
uint64_t endTime = GetTime();
double current_time = SubtractTime( endTime, startTime );
sum += current_time;
if( current_time < bestTime )
bestTime = current_time;
}
if( gReportAverageTimes )
bestTime = sum / PERF_LOOP_COUNT;
double clocksPerOp = bestTime * (double) gDeviceFrequency * gComputeDevices * gSimdSize * 1e6 / (BUFFER_SIZE / sizeof( float ) );
vlog_perf( clocksPerOp, LOWER_IS_BETTER, "clocks / element", "%sf%s", f->name, sizeNames[j] );
}
}
if( ! gSkipCorrectnessTesting )
vlog( "\t%8.2f @ %a", maxError, maxErrorVal );
vlog( "\n" );
exit:
for( i = gMinVectorSizeIndex; i < gMaxVectorSizeIndex; i++ )
{
clReleaseProgram(test_info.programs[i]);
if( test_info.k[i] )
{
for( j = 0; j < test_info.threadCount; j++ )
clReleaseKernel(test_info.k[i][j]);
free( test_info.k[i] );
}
}
if( test_info.tinfo )
{
for( i = 0; i < test_info.threadCount; i++ )
{
clReleaseMemObject(test_info.tinfo[i].inBuf);
for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ )
clReleaseMemObject(test_info.tinfo[i].outBuf[j]);
clReleaseCommandQueue(test_info.tinfo[i].tQueue);
}
free( test_info.tinfo );
}
return error;
}
static cl_int TestFloat( cl_uint job_id, cl_uint thread_id, void *data )
{
const TestInfo *job = (const TestInfo *) data;
size_t buffer_elements = job->subBufferSize;
size_t buffer_size = buffer_elements * sizeof( cl_float );
cl_uint scale = job->scale;
cl_uint base = job_id * (cl_uint) job->step;
ThreadInfo *tinfo = job->tinfo + thread_id;
fptr func = job->f->func;
const char * fname = job->f->name;
bool relaxedMode = job->relaxedMode;
float ulps = getAllowedUlpError(job->f, relaxedMode);
if (relaxedMode)
{
func = job->f->rfunc;
}
cl_uint j, k;
cl_int error;
int isRangeLimited = job->isRangeLimited;
float half_sin_cos_tan_limit = job->half_sin_cos_tan_limit;
int ftz = job->ftz;
// start the map of the output arrays
cl_event e[ VECTOR_SIZE_COUNT ];
cl_uint *out[ VECTOR_SIZE_COUNT ];
for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ )
{
out[j] = (uint32_t*) clEnqueueMapBuffer( tinfo->tQueue, tinfo->outBuf[j], CL_FALSE, CL_MAP_WRITE, 0, buffer_size, 0, NULL, e + j, &error);
if( error || NULL == out[j])
{
vlog_error( "Error: clEnqueueMapBuffer %d failed! err: %d\n", j, error );
return error;
}
}
// Get that moving
if( (error = clFlush(tinfo->tQueue) ))
vlog( "clFlush failed\n" );
// Write the new values to the input array
cl_uint *p = (cl_uint*) gIn + thread_id * buffer_elements;
for( j = 0; j < buffer_elements; j++ )
{
p[j] = base + j * scale;
if (relaxedMode)
{
float p_j = *(float *) &p[j];
if ( strcmp(fname,"sin")==0 || strcmp(fname,"cos")==0 ) //the domain of the function is [-pi,pi]
{
if (fabs(p_j) > M_PI) ((float *)p)[j] = NAN;
}
if ( strcmp( fname, "reciprocal" ) == 0 )
{
const float l_limit = HEX_FLT(+, 1, 0, -, 126);
const float u_limit = HEX_FLT(+, 1, 0, +, 126);
if (fabs(p_j) < l_limit
|| fabs(p_j)
> u_limit) // the domain of the function is [2^-126,2^126]
((float *)p)[j] = NAN;
}
}
}
if( (error = clEnqueueWriteBuffer( tinfo->tQueue, tinfo->inBuf, CL_FALSE, 0, buffer_size, p, 0, NULL, NULL) ))
{
vlog_error( "Error: clEnqueueWriteBuffer failed! err: %d\n", error );
return error;
}
for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ )
{
//Wait for the map to finish
if( (error = clWaitForEvents(1, e + j) ))
{
vlog_error( "Error: clWaitForEvents failed! err: %d\n", error );
return error;
}
if( (error = clReleaseEvent( e[j] ) ))
{
vlog_error( "Error: clReleaseEvent failed! err: %d\n", error );
return error;
}
// Fill the result buffer with garbage, so that old results don't carry over
uint32_t pattern = 0xffffdead;
memset_pattern4(out[j], &pattern, buffer_size);
if( (error = clEnqueueUnmapMemObject( tinfo->tQueue, tinfo->outBuf[j], out[j], 0, NULL, NULL) ))
{
vlog_error( "Error: clEnqueueMapBuffer failed! err: %d\n", error );
return error;
}
// run the kernel
size_t vectorCount = (buffer_elements + sizeValues[j] - 1) / sizeValues[j];
cl_kernel kernel = job->k[j][thread_id]; //each worker thread has its own copy of the cl_kernel
cl_program program = job->programs[j];
if( ( error = clSetKernelArg( kernel, 0, sizeof( tinfo->outBuf[j] ), &tinfo->outBuf[j] ))){ LogBuildError(program); return error; }
if( ( error = clSetKernelArg( kernel, 1, sizeof( tinfo->inBuf ), &tinfo->inBuf ) )) { LogBuildError(program); return error; }
if( (error = clEnqueueNDRangeKernel(tinfo->tQueue, kernel, 1, NULL, &vectorCount, NULL, 0, NULL, NULL)))
{
vlog_error( "FAILED -- could not execute kernel\n" );
return error;
}
}
// Get that moving
if( (error = clFlush(tinfo->tQueue) ))
vlog( "clFlush 2 failed\n" );
if( gSkipCorrectnessTesting )
return CL_SUCCESS;
//Calculate the correctly rounded reference result
float *r = (float *)gOut_Ref + thread_id * buffer_elements;
float *s = (float *)p;
for( j = 0; j < buffer_elements; j++ )
r[j] = (float) func.f_f( s[j] );
// Read the data back -- no need to wait for the first N-1 buffers. This is an in order queue.
for( j = gMinVectorSizeIndex; j + 1 < gMaxVectorSizeIndex; j++ )
{
out[j] = (uint32_t*) clEnqueueMapBuffer( tinfo->tQueue, tinfo->outBuf[j], CL_FALSE, CL_MAP_READ, 0, buffer_size, 0, NULL, NULL, &error);
if( error || NULL == out[j] )
{
vlog_error( "Error: clEnqueueMapBuffer %d failed! err: %d\n", j, error );
return error;
}
}
// Wait for the last buffer
out[j] = (uint32_t*) clEnqueueMapBuffer( tinfo->tQueue, tinfo->outBuf[j], CL_TRUE, CL_MAP_READ, 0, buffer_size, 0, NULL, NULL, &error);
if( error || NULL == out[j] )
{
vlog_error( "Error: clEnqueueMapBuffer %d failed! err: %d\n", j, error );
return error;
}
//Verify data
uint32_t *t = (uint32_t *)r;
for( j = 0; j < buffer_elements; j++ )
{
for( k = gMinVectorSizeIndex; k < gMaxVectorSizeIndex; k++ )
{
uint32_t *q = out[k];
// If we aren't getting the correctly rounded result
if( t[j] != q[j] )
{
float test = ((float*) q)[j];
double correct = func.f_f( s[j] );
float err = Ulp_Error( test, correct );
float abs_error = Abs_Error( test, correct );
int fail = 0;
int use_abs_error = 0;
// it is possible for the output to not match the reference result but for Ulp_Error
// to be zero, for example -1.#QNAN vs. 1.#QNAN. In such cases there is no failure
if (err == 0.0f)
{
fail = 0;
}
else if (relaxedMode)
{
if ( strcmp(fname,"sin")==0 || strcmp(fname,"cos")==0 )
{
fail = ! (fabsf(abs_error) <= ulps);
use_abs_error = 1;
}
if ( strcmp(fname, "reciprocal") == 0 )
{
fail = ! (fabsf(err) <= ulps);
}
if ( strcmp(fname, "exp") == 0 || strcmp(fname, "exp2") == 0 )
{
float exp_error = ulps;
if (!gIsEmbedded)
{
exp_error += floor(fabs(2 * s[j]));
}
fail = ! (fabsf(err) <= exp_error);
ulps = exp_error;
}
if (strcmp(fname, "tan") == 0) {
if( !gFastRelaxedDerived )
{
fail = ! (fabsf(err) <= ulps);
}
// Else fast math derived implementation does not require ULP verification
}
if (strcmp(fname, "exp10") == 0)
{
if( !gFastRelaxedDerived )
{
fail = ! (fabsf(err) <= ulps);
}
// Else fast math derived implementation does not require ULP verification
}
if ( strcmp(fname,"log") == 0 || strcmp(fname,"log2") == 0 )
{
if( s[j] >= 0.5 && s[j] <= 2 )
{
fail = ! (fabsf(abs_error) <= ulps );
}
else
{
ulps = gIsEmbedded ? job->f->float_embedded_ulps : job->f->float_ulps;
fail = ! (fabsf(err) <= ulps);
}
}
// fast-relaxed implies finite-only
if( IsFloatInfinity(correct) || IsFloatNaN(correct) ||
IsFloatInfinity(s[j]) || IsFloatNaN(s[j]) ) {
fail = 0;
err = 0;
}
}
else
{
fail = ! (fabsf(err) <= ulps);
}
// half_sin/cos/tan are only valid between +-2**16, Inf, NaN
if( isRangeLimited && fabsf(s[j]) > MAKE_HEX_FLOAT(0x1.0p16f, 0x1L, 16) && fabsf(s[j]) < INFINITY )
{
if( fabsf( test ) <= half_sin_cos_tan_limit )
{
err = 0;
fail = 0;
}
}
if( fail )
{
if( ftz )
{
typedef int (*CheckForSubnormal) (double,float); // If we are in fast relaxed math, we have a different calculation for the subnormal threshold.
CheckForSubnormal isFloatResultSubnormalPtr;
if (relaxedMode)
{
isFloatResultSubnormalPtr = &IsFloatResultSubnormalAbsError;
}
else
{
isFloatResultSubnormalPtr = &IsFloatResultSubnormal;
}
// retry per section 6.5.3.2
if( (*isFloatResultSubnormalPtr)(correct, ulps) )
{
fail = fail && ( test != 0.0f );
if( ! fail )
err = 0.0f;
}
// retry per section 6.5.3.3
if( IsFloatSubnormal( s[j] ) )
{
double correct2 = func.f_f( 0.0 );
double correct3 = func.f_f( -0.0 );
float err2;
float err3;
if( use_abs_error )
{
err2 = Abs_Error( test, correct2 );
err3 = Abs_Error( test, correct3 );
}
else
{
err2 = Ulp_Error( test, correct2 );
err3 = Ulp_Error( test, correct3 );
}
fail = fail && ((!(fabsf(err2) <= ulps)) && (!(fabsf(err3) <= ulps)));
if( fabsf( err2 ) < fabsf(err ) )
err = err2;
if( fabsf( err3 ) < fabsf(err ) )
err = err3;
// retry per section 6.5.3.4
if( (*isFloatResultSubnormalPtr)(correct2, ulps ) || (*isFloatResultSubnormalPtr)(correct3, ulps ) )
{
fail = fail && ( test != 0.0f);
if( ! fail )
err = 0.0f;
}
}
}
}
if( fabsf(err ) > tinfo->maxError )
{
tinfo->maxError = fabsf(err);
tinfo->maxErrorValue = s[j];
}
if( fail )
{
vlog_error( "\nERROR: %s%s: %f ulp error at %a (0x%8.8x): *%a vs. %a\n", job->f->name, sizeNames[k], err, ((float*) s)[j], ((uint32_t*) s)[j], ((float*) t)[j], test);
return -1;
}
}
}
}
for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ )
{
if( (error = clEnqueueUnmapMemObject( tinfo->tQueue, tinfo->outBuf[j], out[j], 0, NULL, NULL)) )
{
vlog_error( "Error: clEnqueueUnmapMemObject %d failed 2! err: %d\n", j, error );
return error;
}
}
if( (error = clFlush(tinfo->tQueue) ))
vlog( "clFlush 3 failed\n" );
if( 0 == ( base & 0x0fffffff) )
{
if (gVerboseBruteForce)
{
vlog("base:%14u step:%10u scale:%10u buf_elements:%10zd ulps:%5.3f ThreadCount:%2u\n", base, job->step, job->scale, buffer_elements, job->ulps, job->threadCount);
} else
{
vlog("." );
}
fflush(stdout);
}
return CL_SUCCESS;
}
static cl_int TestDouble( cl_uint job_id, cl_uint thread_id, void *data )
{
const TestInfo *job = (const TestInfo *) data;
size_t buffer_elements = job->subBufferSize;
size_t buffer_size = buffer_elements * sizeof( cl_double );
cl_uint scale = job->scale;
cl_uint base = job_id * (cl_uint) job->step;
ThreadInfo *tinfo = job->tinfo + thread_id;
float ulps = job->ulps;
dptr func = job->f->dfunc;
cl_uint j, k;
cl_int error;
int ftz = job->ftz;
Force64BitFPUPrecision();
// start the map of the output arrays
cl_event e[ VECTOR_SIZE_COUNT ];
cl_ulong *out[ VECTOR_SIZE_COUNT ];
for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ )
{
out[j] = (cl_ulong*) clEnqueueMapBuffer( tinfo->tQueue, tinfo->outBuf[j], CL_FALSE, CL_MAP_WRITE, 0, buffer_size, 0, NULL, e + j, &error);
if( error || NULL == out[j])
{
vlog_error( "Error: clEnqueueMapBuffer %d failed! err: %d\n", j, error );
return error;
}
}
// Get that moving
if( (error = clFlush(tinfo->tQueue) ))
vlog( "clFlush failed\n" );
// Write the new values to the input array
cl_double *p = (cl_double*) gIn + thread_id * buffer_elements;
for( j = 0; j < buffer_elements; j++ )
p[j] = DoubleFromUInt32( base + j * scale);
if( (error = clEnqueueWriteBuffer( tinfo->tQueue, tinfo->inBuf, CL_FALSE, 0, buffer_size, p, 0, NULL, NULL) ))
{
vlog_error( "Error: clEnqueueWriteBuffer failed! err: %d\n", error );
return error;
}
for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ )
{
//Wait for the map to finish
if( (error = clWaitForEvents(1, e + j) ))
{
vlog_error( "Error: clWaitForEvents failed! err: %d\n", error );
return error;
}
if( (error = clReleaseEvent( e[j] ) ))
{
vlog_error( "Error: clReleaseEvent failed! err: %d\n", error );
return error;
}
// Fill the result buffer with garbage, so that old results don't carry over
uint32_t pattern = 0xffffdead;
memset_pattern4(out[j], &pattern, buffer_size);
if( (error = clEnqueueUnmapMemObject( tinfo->tQueue, tinfo->outBuf[j], out[j], 0, NULL, NULL) ))
{
vlog_error( "Error: clEnqueueMapBuffer failed! err: %d\n", error );
return error;
}
// run the kernel
size_t vectorCount = (buffer_elements + sizeValues[j] - 1) / sizeValues[j];
cl_kernel kernel = job->k[j][thread_id]; //each worker thread has its own copy of the cl_kernel
cl_program program = job->programs[j];
if( ( error = clSetKernelArg( kernel, 0, sizeof( tinfo->outBuf[j] ), &tinfo->outBuf[j] ))){ LogBuildError(program); return error; }
if( ( error = clSetKernelArg( kernel, 1, sizeof( tinfo->inBuf ), &tinfo->inBuf ) )) { LogBuildError(program); return error; }
if( (error = clEnqueueNDRangeKernel(tinfo->tQueue, kernel, 1, NULL, &vectorCount, NULL, 0, NULL, NULL)))
{
vlog_error( "FAILED -- could not execute kernel\n" );
return error;
}
}
// Get that moving
if( (error = clFlush(tinfo->tQueue) ))
vlog( "clFlush 2 failed\n" );
if( gSkipCorrectnessTesting )
return CL_SUCCESS;
//Calculate the correctly rounded reference result
cl_double *r = (cl_double *)gOut_Ref + thread_id * buffer_elements;
cl_double *s = (cl_double *)p;
for( j = 0; j < buffer_elements; j++ )
r[j] = (cl_double) func.f_f( s[j] );
// Read the data back -- no need to wait for the first N-1 buffers. This is an in order queue.
for( j = gMinVectorSizeIndex; j + 1 < gMaxVectorSizeIndex; j++ )
{
out[j] = (cl_ulong*) clEnqueueMapBuffer( tinfo->tQueue, tinfo->outBuf[j], CL_FALSE, CL_MAP_READ, 0, buffer_size, 0, NULL, NULL, &error);
if( error || NULL == out[j] )
{
vlog_error( "Error: clEnqueueMapBuffer %d failed! err: %d\n", j, error );
return error;
}
}
// Wait for the last buffer
out[j] = (cl_ulong*) clEnqueueMapBuffer( tinfo->tQueue, tinfo->outBuf[j], CL_TRUE, CL_MAP_READ, 0, buffer_size, 0, NULL, NULL, &error);
if( error || NULL == out[j] )
{
vlog_error( "Error: clEnqueueMapBuffer %d failed! err: %d\n", j, error );
return error;
}
//Verify data
cl_ulong *t = (cl_ulong *)r;
for( j = 0; j < buffer_elements; j++ )
{
for( k = gMinVectorSizeIndex; k < gMaxVectorSizeIndex; k++ )
{
cl_ulong *q = out[k];
// If we aren't getting the correctly rounded result
if( t[j] != q[j] )
{
cl_double test = ((cl_double*) q)[j];
long double correct = func.f_f( s[j] );
float err = Bruteforce_Ulp_Error_Double( test, correct );
int fail = ! (fabsf(err) <= ulps);
if( fail )
{
if( ftz )
{
// retry per section 6.5.3.2
if( IsDoubleResultSubnormal(correct, ulps) )
{
fail = fail && ( test != 0.0f );
if( ! fail )
err = 0.0f;
}
// retry per section 6.5.3.3
if( IsDoubleSubnormal( s[j] ) )
{
long double correct2 = func.f_f( 0.0L );
long double correct3 = func.f_f( -0.0L );
float err2 = Bruteforce_Ulp_Error_Double( test, correct2 );
float err3 = Bruteforce_Ulp_Error_Double( test, correct3 );
fail = fail && ((!(fabsf(err2) <= ulps)) && (!(fabsf(err3) <= ulps)));
if( fabsf( err2 ) < fabsf(err ) )
err = err2;
if( fabsf( err3 ) < fabsf(err ) )
err = err3;
// retry per section 6.5.3.4
if( IsDoubleResultSubnormal(correct2, ulps ) || IsDoubleResultSubnormal(correct3, ulps ) )
{
fail = fail && ( test != 0.0f);
if( ! fail )
err = 0.0f;
}
}
}
}
if( fabsf(err ) > tinfo->maxError )
{
tinfo->maxError = fabsf(err);
tinfo->maxErrorValue = s[j];
}
if( fail )
{
vlog_error( "\nERROR: %s%s: %f ulp error at %.13la (0x%16.16llx): *%.13la vs. %.13la\n", job->f->name, sizeNames[k], err, ((cl_double*) gIn)[j], ((cl_ulong*) gIn)[j], ((cl_double*) gOut_Ref)[j], test );
return -1;
}
}
}
}
for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ )
{
if( (error = clEnqueueUnmapMemObject( tinfo->tQueue, tinfo->outBuf[j], out[j], 0, NULL, NULL)) )
{
vlog_error( "Error: clEnqueueUnmapMemObject %d failed 2! err: %d\n", j, error );
return error;
}
}
if( (error = clFlush(tinfo->tQueue) ))
vlog( "clFlush 3 failed\n" );
if( 0 == ( base & 0x0fffffff) )
{
if (gVerboseBruteForce)
{
vlog("base:%14u step:%10u scale:%10zd buf_elements:%10u ulps:%5.3f ThreadCount:%2u\n", base, job->step, buffer_elements, job->scale, job->ulps, job->threadCount);
} else
{
vlog("." );
}
fflush(stdout);
}
return CL_SUCCESS;
}
int TestFunc_Double_Double(const Func *f, MTdata d, bool relaxedMode)
{
TestInfo test_info;
cl_int error;
size_t i, j;
float maxError = 0.0f;
double maxErrorVal = 0.0;
#if defined( __APPLE__ )
struct timeval time_val;
gettimeofday( &time_val, NULL );
double start_time = time_val.tv_sec + 1e-6 * time_val.tv_usec;
double end_time;
#endif
logFunctionInfo(f->name, sizeof(cl_double), relaxedMode);
// Init test_info
memset( &test_info, 0, sizeof( test_info ) );
test_info.threadCount = GetThreadCount();
test_info.subBufferSize = BUFFER_SIZE / (sizeof( cl_double) * RoundUpToNextPowerOfTwo(test_info.threadCount));
test_info.scale = 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.relaxedMode = relaxedMode;
// cl_kernels aren't thread safe, so we make one for each vector size for every thread
for( i = gMinVectorSizeIndex; i < gMaxVectorSizeIndex; i++ )
{
size_t array_size = test_info.threadCount * sizeof( cl_kernel );
test_info.k[i] = (cl_kernel*)malloc( array_size );
if( NULL == test_info.k[i] )
{
vlog_error( "Error: Unable to allocate storage for kernels!\n" );
error = CL_OUT_OF_HOST_MEMORY;
goto exit;
}
memset( test_info.k[i], 0, array_size );
}
test_info.tinfo = (ThreadInfo*)malloc( test_info.threadCount * sizeof(*test_info.tinfo) );
if( NULL == test_info.tinfo )
{
vlog_error( "Error: Unable to allocate storage for thread specific data.\n" );
error = CL_OUT_OF_HOST_MEMORY;
goto exit;
}
memset( test_info.tinfo, 0, test_info.threadCount * sizeof(*test_info.tinfo) );
for( i = 0; i < test_info.threadCount; i++ )
{
cl_buffer_region region = { i * test_info.subBufferSize * sizeof( cl_double), test_info.subBufferSize * sizeof( cl_double) };
test_info.tinfo[i].inBuf = clCreateSubBuffer( gInBuffer, CL_MEM_READ_ONLY, CL_BUFFER_CREATE_TYPE_REGION, &region, &error);
if( error || NULL == test_info.tinfo[i].inBuf)
{
vlog_error( "Error: Unable to create sub-buffer of gInBuffer for region {%zd, %zd}\n", region.origin, region.size );
goto exit;
}
for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ )
{
/* Qualcomm fix: 9461 read-write flags must be compatible with parent buffer */
test_info.tinfo[i].outBuf[j] = clCreateSubBuffer( gOutBuffer[j], CL_MEM_WRITE_ONLY, CL_BUFFER_CREATE_TYPE_REGION, &region, &error);
/* Qualcomm fix: end */
if( error || NULL == test_info.tinfo[i].outBuf[j] )
{
vlog_error( "Error: Unable to create sub-buffer of gInBuffer for region {%zd, %zd}\n", region.origin, region.size );
goto exit;
}
}
test_info.tinfo[i].tQueue = clCreateCommandQueue(gContext, gDevice, 0, &error);
if( NULL == test_info.tinfo[i].tQueue || error )
{
vlog_error( "clCreateCommandQueue failed. (%d)\n", error );
goto exit;
}
}
// Init the kernels
{
BuildKernelInfo build_info = {
gMinVectorSizeIndex, test_info.threadCount, test_info.k,
test_info.programs, f->nameInCode, relaxedMode
};
if( (error = ThreadPool_Do( BuildKernel_DoubleFn, gMaxVectorSizeIndex - gMinVectorSizeIndex, &build_info ) ))
goto exit;
}
if( !gSkipCorrectnessTesting )
{
error = ThreadPool_Do( TestDouble, test_info.jobCount, &test_info );
// Accumulate the arithmetic errors
for( i = 0; i < test_info.threadCount; i++ )
{
if( test_info.tinfo[i].maxError > maxError )
{
maxError = test_info.tinfo[i].maxError;
maxErrorVal = test_info.tinfo[i].maxErrorValue;
}
}
if( error )
goto exit;
if( gWimpyMode )
vlog( "Wimp pass" );
else
vlog( "passed" );
}
#if defined( __APPLE__ )
gettimeofday( &time_val, NULL);
end_time = time_val.tv_sec + 1e-6 * time_val.tv_usec;
#endif
if( gMeasureTimes )
{
//Init input array
double *p = (double *)gIn;
if( strstr( f->name, "exp" ) )
for( j = 0; j < BUFFER_SIZE / sizeof( double ); j++ )
p[j] = (double)genrand_real1(d);
else if( strstr( f->name, "log" ) )
for( j = 0; j < BUFFER_SIZE / sizeof( double ); j++ )
p[j] = fabs(DoubleFromUInt32( genrand_int32(d)));
else
for( j = 0; j < BUFFER_SIZE / sizeof( double ); j++ )
p[j] = DoubleFromUInt32( genrand_int32(d) );
if( (error = clEnqueueWriteBuffer(gQueue, gInBuffer, CL_FALSE, 0, BUFFER_SIZE, gIn, 0, NULL, NULL) ))
{
vlog_error( "\n*** Error %d in clEnqueueWriteBuffer ***\n", error );
return error;
}
// Run the kernels
for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ )
{
size_t vectorSize = sizeValues[j] * sizeof(cl_double);
size_t localCount = (BUFFER_SIZE + vectorSize - 1) / vectorSize;
if( ( error = clSetKernelArg( test_info.k[j][0], 0, sizeof( gOutBuffer[j] ), &gOutBuffer[j] ) )) { LogBuildError(test_info.programs[j]); goto exit; }
if( ( error = clSetKernelArg( test_info.k[j][0], 1, sizeof( gInBuffer ), &gInBuffer ) )) { LogBuildError(test_info.programs[j]); goto exit; }
double sum = 0.0;
double bestTime = INFINITY;
for( i = 0; i < PERF_LOOP_COUNT; i++ )
{
uint64_t startTime = GetTime();
if( (error = clEnqueueNDRangeKernel(gQueue, test_info.k[j][0], 1, NULL, &localCount, NULL, 0, NULL, NULL)) )
{
vlog_error( "FAILED -- could not execute kernel\n" );
goto exit;
}
// Make sure OpenCL is done
if( (error = clFinish(gQueue) ) )
{
vlog_error( "Error %d at clFinish\n", error );
goto exit;
}
uint64_t endTime = GetTime();
double current_time = SubtractTime( endTime, startTime );
sum += current_time;
if( current_time < bestTime )
bestTime = current_time;
}
if( gReportAverageTimes )
bestTime = sum / PERF_LOOP_COUNT;
double clocksPerOp = bestTime * (double) gDeviceFrequency * gComputeDevices * gSimdSize * 1e6 / (BUFFER_SIZE / sizeof( double ) );
vlog_perf( clocksPerOp, LOWER_IS_BETTER, "clocks / element", "%sD%s", f->name, sizeNames[j] );
}
for( ; j < gMaxVectorSizeIndex; j++ )
vlog( "\t -- " );
}
if( ! gSkipCorrectnessTesting )
vlog( "\t%8.2f @ %a", maxError, maxErrorVal );
#if defined( __APPLE__ )
vlog( "\t(%2.2f seconds)", end_time - start_time );
#endif
vlog( "\n" );
exit:
for( i = gMinVectorSizeIndex; i < gMaxVectorSizeIndex; i++ )
{
clReleaseProgram(test_info.programs[i]);
if( test_info.k[i] )
{
for( j = 0; j < test_info.threadCount; j++ )
clReleaseKernel(test_info.k[i][j]);
free( test_info.k[i] );
}
}
if( test_info.tinfo )
{
for( i = 0; i < test_info.threadCount; i++ )
{
clReleaseMemObject(test_info.tinfo[i].inBuf);
for( j = gMinVectorSizeIndex; j < gMaxVectorSizeIndex; j++ )
clReleaseMemObject(test_info.tinfo[i].outBuf[j]);
clReleaseCommandQueue(test_info.tinfo[i].tQueue);
}
free( test_info.tinfo );
}
return error;
}