Rename HIP_ATP_MARKER and profiling vars
HIP_PROFILE_API HIP_DB_START_API HIP_DB_STOP_API Change-Id: I6c4da67212ff8217e6356a2622d4c6278a188c34
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@@ -0,0 +1,36 @@
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HIP_PATH?= $(wildcard /opt/rocm/hip)
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ifeq (,$(HIP_PATH))
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HIP_PATH=../../..
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endif
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HIPCC=$(HIP_PATH)/bin/hipcc
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TARGET=hcc
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SOURCES = MatrixTranspose.cpp
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OBJECTS = $(SOURCES:.cpp=.o)
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EXECUTABLE=./MatrixTranspose
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.PHONY: test
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all: $(EXECUTABLE) test
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CXXFLAGS =-g
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CXX=$(HIPCC)
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$(EXECUTABLE): $(OBJECTS)
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$(HIPCC) $(OBJECTS) -o $@
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test: $(EXECUTABLE)
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$(EXECUTABLE)
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clean:
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rm -f $(EXECUTABLE)
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rm -f $(OBJECTS)
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rm -f $(HIP_PATH)/src/*.o
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@@ -0,0 +1,172 @@
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/*
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Copyright (c) 2015-2016 Advanced Micro Devices, Inc. All rights reserved.
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
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in the Software without restriction, including without limitation the rights
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to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
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furnished to do so, subject to the following conditions:
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The above copyright notice and this permission notice shall be included in
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all copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN
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THE SOFTWARE.
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*/
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#include<iostream>
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// hip header file
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#include "hip/hip_runtime.h"
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#define WIDTH 1024
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#define NUM (WIDTH*WIDTH)
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#define THREADS_PER_BLOCK_X 4
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#define THREADS_PER_BLOCK_Y 4
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#define THREADS_PER_BLOCK_Z 1
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// Device (Kernel) function, it must be void
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// hipLaunchParm provides the execution configuration
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__global__ void matrixTranspose(hipLaunchParm lp,
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float *out,
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float *in,
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const int width)
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{
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int x = hipBlockDim_x * hipBlockIdx_x + hipThreadIdx_x;
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int y = hipBlockDim_y * hipBlockIdx_y + hipThreadIdx_y;
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out[y * width + x] = in[x * width + y];
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}
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// CPU implementation of matrix transpose
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void matrixTransposeCPUReference(
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float * output,
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float * input,
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const unsigned int width)
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{
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for(unsigned int j=0; j < width; j++)
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{
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for(unsigned int i=0; i < width; i++)
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{
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output[i*width + j] = input[j*width + i];
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}
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}
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}
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int main() {
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float* Matrix;
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float* TransposeMatrix;
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float* cpuTransposeMatrix;
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float* gpuMatrix;
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float* gpuTransposeMatrix;
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hipDeviceProp_t devProp;
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hipGetDeviceProperties(&devProp, 0);
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std::cout << "Device name " << devProp.name << std::endl;
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hipEvent_t start, stop;
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hipEventCreate(&start);
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hipEventCreate(&stop);
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float eventMs = 1.0f;
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int i;
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int errors;
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Matrix = (float*)malloc(NUM * sizeof(float));
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TransposeMatrix = (float*)malloc(NUM * sizeof(float));
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cpuTransposeMatrix = (float*)malloc(NUM * sizeof(float));
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// initialize the input data
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for (i = 0; i < NUM; i++) {
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Matrix[i] = (float)i*10.0f;
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}
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// allocate the memory on the device side
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hipMalloc((void**)&gpuMatrix, NUM * sizeof(float));
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hipMalloc((void**)&gpuTransposeMatrix, NUM * sizeof(float));
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// Record the start event
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hipEventRecord(start, NULL);
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// Memory transfer from host to device
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hipMemcpy(gpuMatrix, Matrix, NUM*sizeof(float), hipMemcpyHostToDevice);
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// Record the stop event
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hipEventRecord(stop, NULL);
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hipEventSynchronize(stop);
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hipEventElapsedTime(&eventMs, start, stop);
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printf ("hipMemcpyHostToDevice time taken = %6.3fms\n", eventMs);
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// Record the start event
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hipEventRecord(start, NULL);
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// Lauching kernel from host
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hipLaunchKernel(matrixTranspose,
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dim3(WIDTH/THREADS_PER_BLOCK_X, WIDTH/THREADS_PER_BLOCK_Y),
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dim3(THREADS_PER_BLOCK_X, THREADS_PER_BLOCK_Y),
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0, 0,
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gpuTransposeMatrix , gpuMatrix, WIDTH);
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// Record the stop event
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hipEventRecord(stop, NULL);
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hipEventSynchronize(stop);
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hipEventElapsedTime(&eventMs, start, stop);
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printf ("kernel Execution time = %6.3fms\n", eventMs);
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// Record the start event
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hipEventRecord(start, NULL);
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// Memory transfer from device to host
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hipMemcpy(TransposeMatrix, gpuTransposeMatrix, NUM*sizeof(float), hipMemcpyDeviceToHost);
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// Record the stop event
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hipEventRecord(stop, NULL);
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hipEventSynchronize(stop);
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hipEventElapsedTime(&eventMs, start, stop);
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printf ("hipMemcpyDeviceToHost time taken = %6.3fms\n", eventMs);
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// CPU MatrixTranspose computation
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matrixTransposeCPUReference(cpuTransposeMatrix, Matrix, WIDTH);
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// verify the results
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errors = 0;
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double eps = 1.0E-6;
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for (i = 0; i < NUM; i++) {
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if (std::abs(TransposeMatrix[i] - cpuTransposeMatrix[i]) > eps ) {
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errors++;
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}
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}
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if (errors!=0) {
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printf("FAILED: %d errors\n",errors);
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} else {
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printf ("PASSED!\n");
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}
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//free the resources on device side
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hipFree(gpuMatrix);
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hipFree(gpuTransposeMatrix);
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//free the resources on host side
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free(Matrix);
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free(TransposeMatrix);
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free(cpuTransposeMatrix);
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return errors;
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}
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@@ -0,0 +1,51 @@
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## Using hipEvents to measure performance ###
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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 won't make amy changes to the source code. We'll explain how to use the codexl/rocm-profiler for hip timeline tracing.
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## Introduction:
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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.
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## Requirement:
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[CodeXL Installation](http://gpuopen.com/compute-product/codexl/)
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## prerequiste knowledge:
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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.
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## Simple Matrix Transpose
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We will be using the Simple Matrix Transpose source code from the previous tutorial as it is.
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## Using CodeXL markers for HIP Functions
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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:
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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.
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2. Build HIP with ATP markers enabled HIP pre-built packages are enabled with ATP marker support by default. To enable ATP marker support when building HIP from source, use the option -DCOMPILE_HIP_ATP_MARKER=1 during the cmake configure step.
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3. Set HIP_ATP_MARKER
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`export HIP_ATP_MARKER=1`
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4. Recompile the target application
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5. Run with profiler enabled to generate ATP file.
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`/opt/rocm/bin/rocm-profiler -o <outputATPFileName> -A <applicationName> <applicationArguments>`
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##Using HIP_TRACE_API
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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:
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`HIP_TRACE_API=1 HIP_DB=0x2 ./myHipApp`
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Note this trace mode uses colors. "less -r" can handle raw control characters and will display the debug output in proper colors.
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## More Info:
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- [HIP FAQ](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_faq.md)
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- [HIP Kernel Language](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_kernel_language.md)
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- [HIP Runtime API (Doxygen)](http://gpuopen-professionalcompute-tools.github.io/HIP)
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- [HIP Porting Guide](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/docs/markdown/hip_porting_guide.md)
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- [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)
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- [hipify-clang](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/hipify-clang/README.md)
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- [Developer/CONTRIBUTING Info](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/CONTRIBUTING.md)
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- [Release Notes](https://github.com/GPUOpen-ProfessionalCompute-Tools/HIP/RELEASE.md)
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