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Add new hipdemangleatp and snapshot sample update for new functionality

Telusuri Sumber 
Change-Id: Ie19c683b2b0bdfeb0c3fcf89444c2e21b7f606e7
This commit is contained in:
Ben Sander committed 2016-10-27 23:09:31 -05:00
1 orang tua 23dc2f02cd
melakukan f8c4fa982a
5 files changed
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samples/2_Cookbook/2_Profiler/Makefile
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HIP_PATH?= $(wildcard /opt/rocm/hip)
HIPCC=$(HIP_PATH)/bin/hipcc
HIPPROFILER=/opt/rocm/bin/rocm-profiler
PROFILER_OPT=-A -o MT.atp -e HIP_PROFILE_API=1
HIPPROFILER_POST_CMD=$(HIP_PATH)/bin/hipdemangleatp MT.atp
TARGET=hcc
SOURCES = MatrixTranspose.cpp
OBJECTS = $(SOURCES:.cpp=.o)
EXECUTABLE=./MatrixTranspose
.PHONY: test
all: $(EXECUTABLE) profile
OPT =-g
CXXFLAGS =$(OPT)
CXX=$(HIPCC)
$(EXECUTABLE): $(OBJECTS)
$(HIPCC) $(OBJECTS) -o $@
profile: $(EXECUTABLE)
$(HIPPROFILER) $(PROFILER_OPT) $(EXECUTABLE)
$(HIPPROFILER_POST_CMD)
run: $(EXECUTABLE)
$(EXECUTABLE)
clean:
rm -f $(EXECUTABLE)
rm -f $(OBJECTS)
rm -f $(HIP_PATH)/src/*.o
samples/2_Cookbook/2_Profiler/MatrixTranspose.cpp
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/*
Copyright (c) 2015-2016 Advanced Micro Devices, Inc. All rights reserved.
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in
all copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
THE SOFTWARE.
*/
#include<iostream>
// hip header file
#include "hip/hip_runtime.h"
#include "hip/hip_profile.h"
#define WIDTH 1024
#define NUM (WIDTH*WIDTH)
#define THREADS_PER_BLOCK_X 4
#define THREADS_PER_BLOCK_Y 4
#define THREADS_PER_BLOCK_Z 1
// Device (Kernel) function, it must be void
// hipLaunchParm provides the execution configuration
__global__ void matrixTranspose(hipLaunchParm lp,
float *out,
float *in,
const int width)
{
int x = hipBlockDim_x * hipBlockIdx_x + hipThreadIdx_x;
int y = hipBlockDim_y * hipBlockIdx_y + hipThreadIdx_y;
out[y * width + x] = in[x * width + y];
}
// CPU implementation of matrix transpose
void matrixTransposeCPUReference(
float * output,
float * input,
const unsigned int width)
{
for(unsigned int j=0; j < width; j++)
{
for(unsigned int i=0; i < width; i++)
{
output[i*width + j] = input[j*width + i];
}
}
}
int main() {
//HIP_SCOPED_MARKER(__func__, "MainFunc");
HIP_BEGIN_MARKER(__func__, "MainFunc");
float* Matrix;
float* TransposeMatrix;
float* cpuTransposeMatrix;
float* gpuMatrix;
float* gpuTransposeMatrix;
hipDeviceProp_t devProp;
hipGetDeviceProperties(&devProp, 0);
std::cout << "Device name " << devProp.name << std::endl;
hipEvent_t start, stop;
float eventMs = 1.0f;
{
// Show example of how to create a "scoped marker".
// The scoped marker records the time spent inside the { scope } of the marker - the begin timestamp is at the
// beginning of the code scope, and the end is recorded when the SCOPE exits. This can be viewed in CodeXL
// timeline relative to other GPU and CPU events.
// This marker captures the time spent in setup including host allocation, initialization, and device memory allocation.
HIP_SCOPED_MARKER("Setup", "App");
hipEventCreate(&start);
hipEventCreate(&stop);
Matrix = (float*)malloc(NUM * sizeof(float));
TransposeMatrix = (float*)malloc(NUM * sizeof(float));
cpuTransposeMatrix = (float*)malloc(NUM * sizeof(float));
// initialize the input data
for (int i = 0; i < NUM; i++) {
Matrix[i] = (float)i*10.0f;
}
// allocate the memory on the device side
hipMalloc((void**)&gpuMatrix, NUM * sizeof(float));
hipMalloc((void**)&gpuTransposeMatrix, NUM * sizeof(float));
}
{
HIP_SCOPED_MARKER("Loop", "App");
// Record the start event
hipEventRecord(start, NULL);
// Memory transfer from host to device
hipMemcpy(gpuMatrix, Matrix, NUM*sizeof(float), hipMemcpyHostToDevice);
// Record the stop event
hipEventRecord(stop, NULL);
hipEventSynchronize(stop);
hipEventElapsedTime(&eventMs, start, stop);
printf ("hipMemcpyHostToDevice time taken = %6.3fms\n", eventMs);
// Record the start event
hipEventRecord(start, NULL);
// Lauching kernel from host
hipLaunchKernel(matrixTranspose,
dim3(WIDTH/THREADS_PER_BLOCK_X, WIDTH/THREADS_PER_BLOCK_Y),
dim3(THREADS_PER_BLOCK_X, THREADS_PER_BLOCK_Y),
0, 0,
gpuTransposeMatrix , gpuMatrix, WIDTH);
// Record the stop event
hipEventRecord(stop, NULL);
hipEventSynchronize(stop);
hipEventElapsedTime(&eventMs, start, stop);
}
int errors = 0;
{
HIP_SCOPED_MARKER("Teardown", "App");
printf ("kernel Execution time = %6.3fms\n", eventMs);
// Record the start event
hipEventRecord(start, NULL);
// Memory transfer from device to host
hipMemcpy(TransposeMatrix, gpuTransposeMatrix, NUM*sizeof(float), hipMemcpyDeviceToHost);
// Record the stop event
hipEventRecord(stop, NULL);
hipEventSynchronize(stop);
hipEventElapsedTime(&eventMs, start, stop);
printf ("hipMemcpyDeviceToHost time taken = %6.3fms\n", eventMs);
// CPU MatrixTranspose computation
matrixTransposeCPUReference(cpuTransposeMatrix, Matrix, WIDTH);
// verify the results
double eps = 1.0E-6;
for (int i = 0; i < NUM; i++) {
if (std::abs(TransposeMatrix[i] - cpuTransposeMatrix[i]) > eps ) {
errors++;
}
}
if (errors!=0) {
printf("FAILED: %d errors\n",errors);
} else {
printf ("PASSED!\n");
}
//free the resources on device side
hipFree(gpuMatrix);
hipFree(gpuTransposeMatrix);
//free the resources on host side
free(Matrix);
free(TransposeMatrix);
free(cpuTransposeMatrix);
}
HIP_END_MARKER();
return errors;
}
samples/2_Cookbook/2_Profiler/Readme.md
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## Using hipEvents to measure performance ###
This tutorial is follow-up of the previous two tutorial where we learn how to write our first hip program, in which we compute Matrix Transpose and in second one, we added feature to measure time taken for memory transfer and kernel execution. In this tutorial, we'll explain how to use the codexl/rocm-profiler for hip timeline tracing. Also, we will augment the source code with additional markers so we can see the high-level application flow alongside the information that CodeXL automatically collects.
## Introduction:
CodeXL and rocm-profiler are the tool used for profiling the application, which is of prominent use in optimizing the application by means of finding the memory bottlenecks and etc.
## Requirement:
[CodeXL Installation](http://gpuopen.com/compute-product/codexl/)
## prerequiste knowledge:
Programmers familiar with CUDA, OpenCL will be able to quickly learn and start coding with the HIP API. In case you are not, don't worry. You choose to start with the best one. We'll be explaining everything assuming you are completely new to gpgpu programming.
## Simple Matrix Transpose
We will be using the Simple Matrix Transpose source code from the previous tutorial as it is.
## Using CodeXL markers for HIP Functions
HIP can generate markers at function being/end which are displayed on the CodeXL timeline view. To do this, you need to install ROCm-Profiler and enable HIP to generate the markers:
1. Install ROCm-Profiler Installing HIP from the rocm pre-built packages, installs the ROCm-Profiler as well. Alternatively, you can build ROCm-Profiler using the instructions given below.
2. Run with profiler enabled to generate ATP file.
(These steps are also captured in the Makefile)
The HIP_PROFILE_API enables display of the HIP APIs on the CodeXL trimeline view.
`/opt/rocm/bin/rocm-profiler -o <outputATPFileName> -A <applicationName> -e HIP_PROFILE_API=1 <applicationArguments>`
##Using HIP_TRACE_API
You can also print the HIP function strings to stderr using HIP_TRACE_API environment variable. This can also be combined with the more detailed debug information provided by the HIP_DB switch. For example:
`HIP_TRACE_API=1 HIP_DB=0x2 ./myHipApp`
Note this trace mode uses colors. "less -r" can handle raw control characters and will display the debug output in proper colors.
## More Info:
- [HIP FAQ](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_faq.md)
- [HIP Kernel Language](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_kernel_language.md)
- [HIP Runtime API (Doxygen)](http://gpuopen-professionalcompute-tools.github.io/HIP)
- [HIP Porting Guide](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_porting_guide.md)
- [HIP Terminology](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_terms.md) (including Rosetta Stone of GPU computing terms across CUDA/HIP/HC/AMP/OpenL)
- [hipify-clang](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/hipify-clang/README.md)
- [Developer/CONTRIBUTING Info](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/CONTRIBUTING.md)
- [Release Notes](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/RELEASE.md)