cuda-samples/Samples/matrixMulDrv/matrixMulDrv.cpp

/* Copyright (c) 2018, NVIDIA CORPORATION. All rights reserved.
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 * modification, are permitted provided that the following conditions
 * are met:
 *  * Redistributions of source code must retain the above copyright
 *    notice, this list of conditions and the following disclaimer.
 *  * Redistributions in binary form must reproduce the above copyright
 *    notice, this list of conditions and the following disclaimer in the
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 *  * Neither the name of NVIDIA CORPORATION nor the names of its
 *    contributors may be used to endorse or promote products derived
 *    from this software without specific prior written permission.
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 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
 * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
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/* Matrix multiplication: C = A * B.
 * Host code.
 *
 * This sample implements matrix multiplication using the CUDA driver API.
 * It has been written for clarity of exposition to illustrate various CUDA
 * programming principles, not with the goal of providing the most
 * performant generic kernel for matrix multiplication.
 *
 * CUBLAS provides high-performance matrix multiplication.
 * See also:
 * V. Volkov and J. Demmel, "Benchmarking GPUs to tune dense linear algebra,"
 * in Proc. 2008 ACM/IEEE Conf. on Supercomputing (SC '08),
 * Piscataway, NJ: IEEE Press, 2008, pp. Art. 31:1-11.
 *
 * Volkov, V. 2010. Better performance at lower occupancy,
 * GPU Technology Conference 2~010 (GTC 2010).
 *
 */

// includes, system
#include <builtin_types.h>
#include <cuda.h>
#include <drvapi_error_string.h>
#include <math.h>
#include <stdio.h>
#include <stdlib.h>

// includes, project
#include <helper_cuda_drvapi.h>
#include <helper_image.h>
#include <helper_string.h>
#include <helper_timer.h>

#include <cstring>
#include <iostream>
#include <string>
#include "matrixMul.h"

// includes, CUDA
const bool use_64bit_memory_address = false;

////////////////////////////////////////////////////////////////////////////////
// declaration, forward
void runTest(int argc, char **argv);
void randomInit(float *, int);

extern "C" void computeGold(float *, const float *, const float *, unsigned int,
                            unsigned int, unsigned int);

static CUresult initCUDA(int argc, char **argv, CUfunction *pMatrixMul);

// define input ptx file for different platforms
#if defined(_WIN64) || defined(__LP64__)
#define PTX_FILE "matrixMul_kernel64.ptx"
#define CUBIN_FILE "matrixMul_kernel64.cubin"
#else
#define PTX_FILE "matrixMul_kernel32.ptx"
#define CUBIN_FILE "matrixMul_kernel32.cubin"
#endif

////////////////////////////////////////////////////////////////////////////////
// Globals
////////////////////////////////////////////////////////////////////////////////
CUdevice cuDevice;
CUcontext cuContext;
CUmodule cuModule;
size_t totalGlobalMem;

const char *sSDKsample = "matrixMulDrv (Driver API)";

void constantInit(float *data, int size, float val) {
  for (int i = 0; i < size; ++i) {
    data[i] = val;
  }
}

////////////////////////////////////////////////////////////////////////////////
// Program main
////////////////////////////////////////////////////////////////////////////////
int main(int argc, char **argv) {
  printf("[ %s ]\n", sSDKsample);

  runTest(argc, argv);
}

////////////////////////////////////////////////////////////////////////////////
//! Run a simple test for CUDA
////////////////////////////////////////////////////////////////////////////////
void runTest(int argc, char **argv) {
  // initialize CUDA
  CUfunction matrixMul = NULL;
  int block_size = 32;

  CUresult error_id = initCUDA(argc, argv, &matrixMul);

  if (error_id != CUDA_SUCCESS) {
    printf("initCUDA() returned %d\n-> %s\n", error_id,
           getCudaDrvErrorString(error_id));
    exit(EXIT_FAILURE);
  }

  // set seed for rand()
  srand(2006);

  // allocate host memory for matrices A and B
  unsigned int size_A = WA * HA;
  unsigned int mem_size_A = sizeof(float) * size_A;
  float *h_A = reinterpret_cast<float *>(malloc(mem_size_A));
  unsigned int size_B = WB * HB;
  unsigned int mem_size_B = sizeof(float) * size_B;
  float *h_B = reinterpret_cast<float *>(malloc(mem_size_B));

  // initialize host memory
  const float valB = 0.01f;
  constantInit(h_A, size_A, 1.0f);
  constantInit(h_B, size_B, valB);

  // First reserve about 4GB of memory, so we ensure that all memory allocated
  // afterwards is > 4GB
  CUdeviceptr d_Mem[4];

  if (use_64bit_memory_address) {
    unsigned int mem_size = 1024 * 1024 * 1024;
    checkCudaErrors(cuMemAlloc(&d_Mem[0], mem_size));
    checkCudaErrors(cuMemAlloc(&d_Mem[1], mem_size));
    checkCudaErrors(cuMemAlloc(&d_Mem[2], mem_size));
    checkCudaErrors(cuMemAlloc(&d_Mem[3], mem_size));
  }

  // allocate device memory
  CUdeviceptr d_A;
  checkCudaErrors(cuMemAlloc(&d_A, mem_size_A));
  CUdeviceptr d_B;
  checkCudaErrors(cuMemAlloc(&d_B, mem_size_B));

  // copy host memory to device
  checkCudaErrors(cuMemcpyHtoD(d_A, h_A, mem_size_A));
  checkCudaErrors(cuMemcpyHtoD(d_B, h_B, mem_size_B));

  // allocate device memory for result
  size_t size_C = WC * HC;
  size_t mem_size_C = sizeof(float) * size_C;

  CUdeviceptr d_C;
  checkCudaErrors(cuMemAlloc(&d_C, mem_size_C));

  // allocate mem for the result on host side
  float *h_C = reinterpret_cast<float *>(malloc(mem_size_C));

  // create and start timer
  StopWatchInterface *timer = NULL;
  sdkCreateTimer(&timer);

  // start the timer
  sdkStartTimer(&timer);

  // There are two ways to launch CUDA kernels via the Driver API.
  // In this CUDA Sample, we illustrate both ways to pass parameters
  // and specify parameters.  By default we use the simpler method.
  dim3 block(block_size, block_size, 1);
  dim3 grid(WC / block_size, HC / block_size, 1);

  if (1) {
    // This is the new CUDA 4.0 API for Kernel Parameter passing and Kernel
    // Launching (simplier method)
    if (use_64bit_memory_address &&
        (totalGlobalMem > (uint64_t)4 * 1024 * 1024 * 1024L)) {
      size_t Matrix_Width_A = (size_t)WA;
      size_t Matrix_Width_B = (size_t)WB;
      void *args[5] = {&d_C, &d_A, &d_B, &Matrix_Width_A, &Matrix_Width_B};
      // new CUDA 4.0 Driver API Kernel launch call
      checkCudaErrors(cuLaunchKernel(
          matrixMul, grid.x, grid.y, grid.z, block.x, block.y, block.z,
          2 * block_size * block_size * sizeof(float), NULL, args, NULL));

    } else {
      int Matrix_Width_A = WA;
      int Matrix_Width_B = WB;
      void *args[5] = {&d_C, &d_A, &d_B, &Matrix_Width_A, &Matrix_Width_B};
      // new CUDA 4.0 Driver API Kernel launch call
      checkCudaErrors(cuLaunchKernel(
          matrixMul, grid.x, grid.y, grid.z, block.x, block.y, block.z,
          2 * block_size * block_size * sizeof(float), NULL, args, NULL));
    }

  } else {
    // This is the new CUDA 4.0 API for Kernel Parameter passing and Kernel
    // Launching (advanced method)
    int offset = 0;
    char argBuffer[256];

    // pass in launch parameters (not actually de-referencing CUdeviceptr).
    // CUdeviceptr is storing the value of the parameters
    *(reinterpret_cast<CUdeviceptr *>(&argBuffer[offset])) = d_C;
    offset += sizeof(d_C);
    *(reinterpret_cast<CUdeviceptr *>(&argBuffer[offset])) = d_A;
    offset += sizeof(d_A);
    *(reinterpret_cast<CUdeviceptr *>(&argBuffer[offset])) = d_B;
    offset += sizeof(d_B);

    if (use_64bit_memory_address &&
        (totalGlobalMem > (uint64_t)4 * 1024 * 1024 * 1024L)) {
      size_t Matrix_Width_A = (size_t)WA;
      size_t Matrix_Width_B = (size_t)WB;

      *(reinterpret_cast<CUdeviceptr *>(&argBuffer[offset])) = Matrix_Width_A;
      offset += sizeof(Matrix_Width_A);
      *(reinterpret_cast<CUdeviceptr *>(&argBuffer[offset])) = Matrix_Width_B;
      offset += sizeof(Matrix_Width_B);
    } else {
      int Matrix_Width_A = WA;
      int Matrix_Width_B = WB;

      *(reinterpret_cast<int *>(&argBuffer[offset])) = Matrix_Width_A;
      offset += sizeof(Matrix_Width_A);
      *(reinterpret_cast<int *>(&argBuffer[offset])) = Matrix_Width_B;
      offset += sizeof(Matrix_Width_B);
    }

    void *kernel_launch_config[5] = {CU_LAUNCH_PARAM_BUFFER_POINTER, argBuffer,
                                     CU_LAUNCH_PARAM_BUFFER_SIZE, &offset,
                                     CU_LAUNCH_PARAM_END};

    // new CUDA 4.0 Driver API Kernel launch call
    checkCudaErrors(cuLaunchKernel(
        matrixMul, grid.x, grid.y, grid.z, block.x, block.y, block.z,
        2 * block_size * block_size * sizeof(float), NULL, NULL,
        reinterpret_cast<void **>(&kernel_launch_config)));
  }

  // copy result from device to host
  checkCudaErrors(cuMemcpyDtoH(reinterpret_cast<void *>(h_C), d_C, mem_size_C));

  // stop and destroy timer
  sdkStopTimer(&timer);
  printf("Processing time: %f (ms)\n", sdkGetTimerValue(&timer));
  sdkDeleteTimer(&timer);

  printf("Checking computed result for correctness: ");
  bool correct = true;

  for (int i = 0; i < static_cast<int>(WC * HC); i++) {
    if (fabs(h_C[i] - (WA * valB)) > 1e-5) {
      printf("Error! Matrix[%05d]=%.8f, ref=%.8f error term is > 1e-5\n", i,
             h_C[i], WA * valB);
      correct = false;
    }
  }

  printf("%s\n", correct ? "Result = PASS" : "Result = FAIL");

  printf(
      "\nNOTE: The CUDA Samples are not meant for performance measurements. "
      "Results may vary when GPU Boost is enabled.\n");

  // clean up memory
  if (use_64bit_memory_address) {
    cuMemFree(d_Mem[0]);
    cuMemFree(d_Mem[1]);
    cuMemFree(d_Mem[2]);
    cuMemFree(d_Mem[3]);
  }

  free(h_A);
  free(h_B);
  free(h_C);
  checkCudaErrors(cuMemFree(d_A));
  checkCudaErrors(cuMemFree(d_B));
  checkCudaErrors(cuMemFree(d_C));
  checkCudaErrors(cuCtxDestroy(cuContext));
}

// Allocates a matrix with random float entries.
void randomInit(float *data, int size) {
  for (int i = 0; i < size; ++i) {
    data[i] = rand() / static_cast<float>(RAND_MAX);
  }
}

bool inline findModulePath(const char *module_file, std::string &module_path,
                           char **argv, std::string &ptx_source) {
  char *actual_path = sdkFindFilePath(module_file, argv[0]);

  if (actual_path) {
    module_path = actual_path;
  } else {
    printf("> findModulePath file not found: <%s> \n", module_file);
    return false;
  }

  if (module_path.empty()) {
    printf("> findModulePath file not found: <%s> \n", module_file);
    return false;
  } else {
    printf("> findModulePath <%s>\n", module_path.c_str());

    if (module_path.rfind(".ptx") != std::string::npos) {
      FILE *fp = fopen(module_path.c_str(), "rb");
      fseek(fp, 0, SEEK_END);
      int file_size = ftell(fp);
      char *buf = new char[file_size + 1];
      fseek(fp, 0, SEEK_SET);
      fread(buf, sizeof(char), file_size, fp);
      fclose(fp);
      buf[file_size] = '\0';
      ptx_source = buf;
      delete[] buf;
    }

    return true;
  }
}

static CUresult initCUDA(int argc, char **argv, CUfunction *pMatrixMul) {
  CUfunction cuFunction = 0;
  CUresult status;
  int major = 0, minor = 0;
  char deviceName[100];
  std::string module_path, ptx_source;

  cuDevice = findCudaDeviceDRV(argc, (const char **)argv);

  // get compute capabilities and the devicename
  checkCudaErrors(cuDeviceGetAttribute(
      &major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevice));
  checkCudaErrors(cuDeviceGetAttribute(
      &minor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevice));
  checkCudaErrors(cuDeviceGetName(deviceName, 256, cuDevice));
  printf("> GPU Device has SM %d.%d compute capability\n", major, minor);

  checkCudaErrors(cuDeviceTotalMem(&totalGlobalMem, cuDevice));
  printf("  Total amount of global memory:     %llu bytes\n",
         (long long unsigned int)totalGlobalMem);
  printf("  64-bit Memory Address:             %s\n",
         (totalGlobalMem > (uint64_t)4 * 1024 * 1024 * 1024L) ? "YES" : "NO");

  status = cuCtxCreate(&cuContext, 0, cuDevice);

  if (CUDA_SUCCESS != status) {
    goto Error;
  }

  // first search for the module path before we load the results
  if (!findModulePath(PTX_FILE, module_path, argv, ptx_source)) {
    if (!findModulePath(CUBIN_FILE, module_path, argv, ptx_source)) {
      printf(
          "> findModulePath could not find <matrixMul_kernel> ptx or cubin\n");
      status = CUDA_ERROR_NOT_FOUND;
      goto Error;
    }
  } else {
    printf("> initCUDA loading module: <%s>\n", module_path.c_str());
  }

  if (module_path.rfind("ptx") != std::string::npos) {
    // in this branch we use compilation with parameters
    const unsigned int jitNumOptions = 3;
    CUjit_option *jitOptions = new CUjit_option[jitNumOptions];
    void **jitOptVals = new void *[jitNumOptions];

    // set up size of compilation log buffer
    jitOptions[0] = CU_JIT_INFO_LOG_BUFFER_SIZE_BYTES;
    int jitLogBufferSize = 1024;
    jitOptVals[0] = reinterpret_cast<void *>(jitLogBufferSize);

    // set up pointer to the compilation log buffer
    jitOptions[1] = CU_JIT_INFO_LOG_BUFFER;
    char *jitLogBuffer = new char[jitLogBufferSize];
    jitOptVals[1] = jitLogBuffer;

    // set up pointer to set the Maximum # of registers for a particular kernel
    jitOptions[2] = CU_JIT_MAX_REGISTERS;
    int jitRegCount = 32;
    jitOptVals[2] = reinterpret_cast<void *>(jitRegCount);

    status =
        cuModuleLoadDataEx(&cuModule, ptx_source.c_str(), jitNumOptions,
                           jitOptions, reinterpret_cast<void **>(jitOptVals));

    printf("> PTX JIT log:\n%s\n", jitLogBuffer);
  } else {
    status = cuModuleLoad(&cuModule, module_path.c_str());
  }

  if (CUDA_SUCCESS != status) {
    goto Error;
  }

#if USE_64BIT_MEMORY_ADDRESS

  if (totalGlobalMem > (uint64_t)4 * 1024 * 1024 * 1024L) {
    status = cuModuleGetFunction(&cuFunction, cuModule, "matrixMul_bs32_64bit");
  } else
#endif
  {
    status = cuModuleGetFunction(&cuFunction, cuModule, "matrixMul_bs32_32bit");
  }

  if (CUDA_SUCCESS != status) {
    goto Error;
  }

  *pMatrixMul = cuFunction;

  return CUDA_SUCCESS;
Error:
  cuCtxDestroy(cuContext);
  return status;
}