blob: a31bfb2f23b9412987d07e1a13ad00b82681a718 [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 "function_list.h"
#include "test_functions.h"
#include "utility.h"
#include <cstring>
const float twoToMinus126 = MAKE_HEX_FLOAT(0x1p-126f, 1, -126);
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"
" size_t 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;\n"
" float3 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);
}
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)
{
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);
}
// 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 const size_t specialValuesFloatCount =
sizeof(specialValuesFloat) / sizeof(specialValuesFloat[0]);
// 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(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;
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 = getTestScale(sizeof(cl_float));
if (gWimpyMode)
{
test_info.subBufferSize = gWimpyBufferSize
/ (sizeof(cl_float)
* RoundUpToNextPowerOfTwo(test_info.threadCount));
}
test_info.step = (cl_uint)test_info.subBufferSize * test_info.scale;
if (test_info.step / test_info.subBufferSize != test_info.scale)
{
// there was overflow
test_info.jobCount = 1;
}
else
{
test_info.jobCount = (cl_uint)((1ULL << 32) / test_info.step);
}
test_info.f = f;
test_info.ulps = gIsEmbedded ? f->float_embedded_ulps : f->float_ulps;
test_info.ftz =
f->ftz || gForceFTZ || 0 == (CL_FP_DENORM & gFloatCapabilities);
test_info.relaxedMode = relaxedMode;
test_info.isFDim = 0 == strcmp("fdim", f->nameInCode);
test_info.skipNanInf = test_info.isFDim && !gInfNanSupport;
test_info.isNextafter = 0 == strcmp("nextafter", f->nameInCode);
// 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;
}
test_info.tinfo[i].inBuf2 =
clCreateSubBuffer(gInBuffer2, CL_MEM_READ_ONLY,
CL_BUFFER_CREATE_TYPE_REGION, &region, &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,
&region, &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
cl_uint *p = (cl_uint *)gIn;
cl_uint *p2 = (cl_uint *)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:
// 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 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;
cl_float *r = 0;
cl_float *s = 0;
cl_float *s2 = 0;
cl_int copysign_test = 0;
RoundingMode oldRoundMode;
int skipVerification = 0;
if (relaxedMode)
{
func = job->f->rfunc;
if (strcmp(name, "pow") == 0 && gFastRelaxedDerived)
{
ulps = INFINITY;
skipVerification = 1;
}
}
// 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];
++x;
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;
}