cuda-samples/Samples/UnifiedMemoryPerf/matrixMultiplyPerf.cu

/* Copyright (c) 2019, NVIDIA CORPORATION. All rights reserved.
 *
 * Redistribution and use in source and binary forms, with or without
 * 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
 *    documentation and/or other materials provided with the distribution.
 *  * 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.
 *
 * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS ``AS IS'' AND ANY
 * EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
 * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR
 * PURPOSE ARE DISCLAIMED.  IN NO EVENT SHALL THE COPYRIGHT OWNER OR
 * CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
 * EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
 * PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
 * PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY
 * OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
 * (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
 * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
 */

#include <helper_cuda.h>
#include <helper_timer.h>
#include "commonDefs.hpp"
#include "commonKernels.hpp"

#define VERIFY_GPU_CORRECTNESS 0

size_t maxSampleSizeInMb = 64;
int numKernelRuns = 100;
int verboseResults = 0;

const char *memAllocTypeStr[MEMALLOC_TYPE_COUNT] = {
    "Managed_Memory_With_Hints",
    "Managed_Memory_With_Hints_FullyAsync",
    "Managed_Memory_NoHints",
    "Zero_Copy",
    "Memcpy_HostMalloc_DeviceCudaMalloc",
    "MemcpyAsync_HostMalloc_DeviceCudaMalloc",
    "Memcpy_HostCudaHostAlloc_DeviceCudaMalloc",
    "MemcpyAsync_HostCudaHostAlloc_DeviceCudaMalloc"};

const char *memAllocTypeShortStr[MEMALLOC_TYPE_COUNT] = {
    "UMhint",   // Managed Memory With Hints
    "UMhntAs",  // Managed Memory With_Hints Async
    "UMeasy",   // Managed_Memory with No Hints
    "0Copy",    // Zero Copy
    "MemCopy",  // USE HOST PAGEABLE AND DEVICE_MEMORY
    "CpAsync",  // USE HOST PAGEABLE AND DEVICE_MEMORY ASYNC
    "CpHpglk",  // USE HOST PAGELOCKED AND DEVICE MEMORY
    "CpPglAs"   // USE HOST PAGELOCKED AND DEVICE MEMORY ASYNC
};

static float RandFloat(float low, float high) {
  float t = (float)rand() / (float)RAND_MAX;
  return (1.0f - t) * low + t * high;
}

void fillMatrixWithRandomValues(float *matrix, unsigned int matrixDim) {
  unsigned int i, j;
  for (i = 0; i < matrixDim; ++i) {
    for (j = 0; j < matrixDim; ++j) {
      matrix[j + i * matrixDim] = RandFloat(0.0f, 10.0f);
    }
  }
}

#if VERIFY_GPU_CORRECTNESS
void verifyMatrixMultiplyCorrectness(float *C, float *A, float *B,
                                     unsigned int matrixDim) {
  unsigned int i, j, k, numErrors = 0;
  for (i = 0; i < matrixDim; ++i) {
    for (j = 0; j < matrixDim; ++j) {
      float result = 0.0f;
      for (k = 0; k < matrixDim; ++k) {
        result += A[k + i * matrixDim] * B[j + k * matrixDim];
      }
      if (fabs(C[j + i * matrixDim] - result) > 0.001 * matrixDim) {
        printf("At [%u, %u]: Expected %f, Found %f\n", i, j, result,
               C[j + i * matrixDim]);
        ++numErrors;
      }
    }
  }
  if (numErrors != 0) {
    printf("%d value mismatches occured\n", numErrors);
    fflush(stdout);
    exit(EXIT_FAILURE);  // exit since value mismatches occured
  }
}
#endif

void copyMatrix(float *dstMatrix, float *srcMatrix, unsigned int matrixDim) {
  size_t size = matrixDim * matrixDim * sizeof(float);
  memcpy(dstMatrix, srcMatrix, size);
}

void verifyMatrixData(float *expectedData, float *observedData,
                      unsigned int matrixDim) {
  unsigned int i, j, numErrors = 0;
  for (i = 0; i < matrixDim; ++i) {
    for (j = 0; j < matrixDim; ++j) {
      if (expectedData[j + i * matrixDim] != observedData[j + i * matrixDim]) {
        ++numErrors;
        if (verboseResults) {
          printf("At [%u, %u]: Expected %f, Found %f\n", i, j,
                 expectedData[j + i * matrixDim],
                 observedData[j + i * matrixDim]);
        }
      }
    }
  }
  if (numErrors != 0) {
    printf("%d value mismatches occured\n", numErrors);
    fflush(stdout);
    exit(EXIT_FAILURE);  // exit since value mismatches occured
  }
}

#define BLOCK_SIZE 32
__global__ void matrixMultiplyKernel(float *C, float *A, float *B,
                                     unsigned int matrixDim) {
  // Block index
  int bx = blockIdx.x;
  int by = blockIdx.y;

  // Thread index
  int tx = threadIdx.x;
  int ty = threadIdx.y;

  unsigned int wA = matrixDim;
  unsigned int wB = matrixDim;

  // Index of the first sub-matrix of A processed by the block
  int aBegin = matrixDim * BLOCK_SIZE * by;

  // Index of the last sub-matrix of A processed by the block
  int aEnd = aBegin + wA - 1;

  // Step size used to iterate through the sub-matrices of A
  int aStep = BLOCK_SIZE;

  // Index of the first sub-matrix of B processed by the block
  int bBegin = BLOCK_SIZE * bx;

  // Step size used to iterate through the sub-matrices of B
  int bStep = BLOCK_SIZE * wB;

  // Csub is used to store the element of the block sub-matrix
  // that is computed by the thread
  float Csub = 0;

  // Loop over all the sub-matrices of A and B
  // required to compute the block sub-matrix
  for (int a = aBegin, b = bBegin; a <= aEnd; a += aStep, b += bStep) {
    // Declaration of the shared memory array As used to
    // store the sub-matrix of A
    __shared__ float As[BLOCK_SIZE][BLOCK_SIZE];

    // Declaration of the shared memory array Bs used to
    // store the sub-matrix of B
    __shared__ float Bs[BLOCK_SIZE][BLOCK_SIZE];

    // Load the matrices from device memory
    // to shared memory; each thread loads
    // one element of each matrix
    As[ty][tx] = A[a + wA * ty + tx];
    Bs[ty][tx] = B[b + wB * ty + tx];

    // Synchronize to make sure the matrices are loaded
    __syncthreads();

    // Multiply the two matrices together;
    // each thread computes one element
    // of the block sub-matrix
#pragma unroll

    for (int k = 0; k < BLOCK_SIZE; ++k) {
      Csub += As[ty][k] * Bs[k][tx];
    }

    // Synchronize to make sure that the preceding
    // computation is done before loading two new
    // sub-matrices of A and B in the next iteration
    __syncthreads();
  }

  // Write the block sub-matrix to device memory;
  // each thread writes one element
  int c = wB * BLOCK_SIZE * by + BLOCK_SIZE * bx;
  C[c + wB * ty + tx] = Csub;
}

void runMatrixMultiplyKernel(unsigned int matrixDim, int allocType,
                             unsigned int numLoops, double *gpuLaunchCallsTimes,
                             double *gpuTransferToCallsTimes,
                             double *gpuTransferFromCallsTimes,
                             double *gpuLaunchAndTransferCallsTimes,
                             double *gpuLaunchTransferSyncTimes,
                             double *cpuAccessTimes, double *overallTimes,
                             int device_id) {
  float *dptrA = NULL, *hptrA = NULL;
  float *dptrB = NULL, *hptrB = NULL;
  float *dptrC = NULL, *hptrC = NULL;
  float *randValuesX = NULL, *randValuesY = NULL;
  float *randValuesVerifyXmulY = NULL, *randValuesVerifyYmulX = NULL;
  bool copyRequired = false, hintsRequired = false;
  bool someTransferOpRequired;
  bool isAsync = false;
  cudaStream_t streamToRunOn;
  unsigned int *latch;
  size_t size = matrixDim * matrixDim * sizeof(float);
  dim3 threads(32, 32);
  dim3 grid(matrixDim / threads.x, matrixDim / threads.y);
  StopWatchInterface *gpuLaunchCallsTimer = 0, *gpuTransferCallsTimer = 0;
  StopWatchInterface *gpuSyncTimer = 0, *cpuAccessTimer = 0;
  sdkCreateTimer(&gpuLaunchCallsTimer);
  sdkCreateTimer(&gpuTransferCallsTimer);
  sdkCreateTimer(&gpuSyncTimer);
  sdkCreateTimer(&cpuAccessTimer);
  unsigned int i;

  cudaDeviceProp deviceProp;
  checkCudaErrors(cudaGetDeviceProperties(&deviceProp, device_id));
  checkCudaErrors(cudaStreamCreate(&streamToRunOn));

  randValuesX = (float *)malloc(size);
  if (!randValuesX) {
    exit(EXIT_FAILURE);  // exit since memory allocation error
  }
  randValuesY = (float *)malloc(size);
  if (!randValuesY) {
    exit(EXIT_FAILURE);  // exit since memory allocation error
  }
  randValuesVerifyXmulY = (float *)malloc(size);
  if (!randValuesVerifyXmulY) {
    exit(EXIT_FAILURE);  // exit since memory allocation error
  }
  randValuesVerifyYmulX = (float *)malloc(size);
  if (!randValuesVerifyYmulX) {
    exit(EXIT_FAILURE);  // exit since memory allocation error
  }
  checkCudaErrors(cudaMalloc(&dptrA, size));
  checkCudaErrors(cudaMalloc(&dptrB, size));
  checkCudaErrors(cudaMalloc(&dptrC, size));

  fillMatrixWithRandomValues(randValuesX, matrixDim);
  fillMatrixWithRandomValues(randValuesY, matrixDim);

  checkCudaErrors(
      cudaMemcpyAsync(dptrA, randValuesX, size, cudaMemcpyHostToDevice));
  checkCudaErrors(
      cudaMemcpyAsync(dptrB, randValuesY, size, cudaMemcpyHostToDevice));
  matrixMultiplyKernel<<<grid, threads>>>(dptrC, dptrA, dptrB, matrixDim);
  checkCudaErrors(cudaMemcpyAsync(randValuesVerifyXmulY, dptrC, size,
                                  cudaMemcpyDeviceToHost));
  checkCudaErrors(cudaStreamSynchronize(NULL));
  matrixMultiplyKernel<<<grid, threads>>>(dptrC, dptrB, dptrA, matrixDim);
  checkCudaErrors(cudaMemcpyAsync(randValuesVerifyYmulX, dptrC, size,
                                  cudaMemcpyDeviceToHost));
  checkCudaErrors(cudaStreamSynchronize(NULL));
#if VERIFY_GPU_CORRECTNESS
  verifyMatrixMultiplyCorrectness(randValuesVerifyXmulY, randValuesX,
                                  randValuesY, matrixDim);
  verifyMatrixMultiplyCorrectness(randValuesVerifyYmulX, randValuesY,
                                  randValuesX, matrixDim);
#endif
  checkCudaErrors(cudaFree(dptrA));
  checkCudaErrors(cudaFree(dptrB));
  checkCudaErrors(cudaFree(dptrC));

  checkCudaErrors(cudaMallocHost(&latch, sizeof(unsigned int)));

  switch (allocType) {
    case USE_HOST_PAGEABLE_AND_DEVICE_MEMORY:
    case USE_HOST_PAGEABLE_AND_DEVICE_MEMORY_ASYNC:
      hptrA = (float *)malloc(size);
      if (!hptrA) {
        exit(EXIT_FAILURE);  // exit since memory allocation error
      }
      hptrB = (float *)malloc(size);
      if (!hptrB) {
        exit(EXIT_FAILURE);  // exit since memory allocation error
      }
      hptrC = (float *)malloc(size);
      if (!hptrC) {
        exit(EXIT_FAILURE);  // exit since memory allocation error
      }
      checkCudaErrors(cudaMalloc(&dptrA, size));
      checkCudaErrors(cudaMalloc(&dptrB, size));
      checkCudaErrors(cudaMalloc(&dptrC, size));
      copyRequired = true;
      break;

    case USE_HOST_PAGELOCKED_AND_DEVICE_MEMORY:
    case USE_HOST_PAGELOCKED_AND_DEVICE_MEMORY_ASYNC:
      checkCudaErrors(cudaMallocHost(&hptrA, size));
      checkCudaErrors(cudaMallocHost(&hptrB, size));
      checkCudaErrors(cudaMallocHost(&hptrC, size));
      checkCudaErrors(cudaMalloc(&dptrA, size));
      checkCudaErrors(cudaMalloc(&dptrB, size));
      checkCudaErrors(cudaMalloc(&dptrC, size));
      copyRequired = true;
      break;

    case USE_ZERO_COPY:
      checkCudaErrors(cudaMallocHost(&hptrA, size));
      checkCudaErrors(cudaMallocHost(&hptrB, size));
      checkCudaErrors(cudaMallocHost(&hptrC, size));
      checkCudaErrors(cudaHostGetDevicePointer(&dptrA, hptrA, 0));
      checkCudaErrors(cudaHostGetDevicePointer(&dptrB, hptrB, 0));
      checkCudaErrors(cudaHostGetDevicePointer(&dptrC, hptrC, 0));
      break;

    case USE_MANAGED_MEMORY:
      checkCudaErrors(cudaMallocManaged(&dptrA, size));
      checkCudaErrors(cudaMallocManaged(&dptrB, size));
      checkCudaErrors(cudaMallocManaged(&dptrC, size));
      hptrA = dptrA;
      hptrB = dptrB;
      hptrC = dptrC;
      break;

    case USE_MANAGED_MEMORY_WITH_HINTS:
    case USE_MANAGED_MEMORY_WITH_HINTS_ASYNC:
      if (deviceProp.concurrentManagedAccess) {
        checkCudaErrors(cudaMallocManaged(&dptrA, size));
        checkCudaErrors(cudaMallocManaged(&dptrB, size));
        checkCudaErrors(cudaMallocManaged(&dptrC, size));
        checkCudaErrors(cudaMemPrefetchAsync(dptrA, size, cudaCpuDeviceId));
        checkCudaErrors(cudaMemPrefetchAsync(dptrB, size, cudaCpuDeviceId));
        checkCudaErrors(cudaMemPrefetchAsync(dptrC, size, cudaCpuDeviceId));
      } else {
        checkCudaErrors(cudaMallocManaged(&dptrA, size, cudaMemAttachHost));
        checkCudaErrors(cudaMallocManaged(&dptrB, size, cudaMemAttachHost));
        checkCudaErrors(cudaMallocManaged(&dptrC, size, cudaMemAttachHost));
      }
      hptrA = dptrA;
      hptrB = dptrB;
      hptrC = dptrC;
      hintsRequired = true;
      break;

    default:
      exit(EXIT_FAILURE);  // exit with error
  }

  if (allocType == USE_HOST_PAGEABLE_AND_DEVICE_MEMORY_ASYNC ||
      allocType == USE_HOST_PAGELOCKED_AND_DEVICE_MEMORY_ASYNC ||
      allocType == USE_MANAGED_MEMORY_WITH_HINTS_ASYNC) {
    isAsync = true;
  }

  someTransferOpRequired = copyRequired || hintsRequired;

  // fill buffers with 0 to avoid any first access page-fault overheads.
  memset(hptrA, 0, size);
  memset(hptrB, 0, size);
  memset(hptrC, 0, size);

  for (i = 0; i < numLoops; i++) {
    cpuAccessTimes[i] = 0.0;
    gpuLaunchCallsTimes[i] = 0.0;
    gpuTransferToCallsTimes[i] = 0.0;
    gpuTransferFromCallsTimes[i] = 0.0;

    sdkStartTimer(&cpuAccessTimer);
    {
      copyMatrix(hptrA, (i & 0x1 == 0) ? randValuesX : randValuesY, matrixDim);
      copyMatrix(hptrB, (i & 0x1 == 0) ? randValuesY : randValuesX, matrixDim);
    }
    sdkStopTimer(&cpuAccessTimer);
    cpuAccessTimes[i] += sdkGetAverageTimerValue(&cpuAccessTimer);
    sdkResetTimer(&cpuAccessTimer);

    if (isAsync && hintsRequired) {
      *latch = 0;
      // Prevent any work on stream from starting until all work is pushed
      spinWhileLessThanOne<<<1, 1, 0, streamToRunOn>>>(latch);
    }

    if (someTransferOpRequired) {
      sdkStartTimer(&gpuTransferCallsTimer);
      if (copyRequired) {
        if (isAsync) {
          checkCudaErrors(cudaMemcpyAsync(
              dptrA, hptrA, size, cudaMemcpyHostToDevice, streamToRunOn));
          checkCudaErrors(cudaMemcpyAsync(
              dptrB, hptrB, size, cudaMemcpyHostToDevice, streamToRunOn));
        } else {
          checkCudaErrors(
              cudaMemcpy(dptrA, hptrA, size, cudaMemcpyHostToDevice));
          checkCudaErrors(
              cudaMemcpy(dptrB, hptrB, size, cudaMemcpyHostToDevice));
        }
      }
      if (hintsRequired) {
        if (deviceProp.concurrentManagedAccess) {
          checkCudaErrors(
              cudaMemPrefetchAsync(dptrA, size, device_id, streamToRunOn));
          checkCudaErrors(
              cudaMemPrefetchAsync(dptrB, size, device_id, streamToRunOn));
          checkCudaErrors(
              cudaMemPrefetchAsync(dptrC, size, device_id, streamToRunOn));
        } else {
          checkCudaErrors(cudaStreamAttachMemAsync(streamToRunOn, dptrA, 0,
                                                   cudaMemAttachGlobal));
          checkCudaErrors(cudaStreamAttachMemAsync(streamToRunOn, dptrB, 0,
                                                   cudaMemAttachGlobal));
          checkCudaErrors(cudaStreamAttachMemAsync(streamToRunOn, dptrC, 0,
                                                   cudaMemAttachGlobal));
        }
        if (!isAsync) {
          checkCudaErrors(cudaStreamSynchronize(streamToRunOn));
        }
      }

      sdkStopTimer(&gpuTransferCallsTimer);
      gpuTransferToCallsTimes[i] +=
          sdkGetAverageTimerValue(&gpuTransferCallsTimer);
      sdkResetTimer(&gpuTransferCallsTimer);
    }

    sdkStartTimer(&gpuLaunchCallsTimer);
    {
      matrixMultiplyKernel<<<grid, threads, 0, streamToRunOn>>>(
          dptrC, dptrA, dptrB, matrixDim);
      if (!isAsync) {
        checkCudaErrors(cudaStreamSynchronize(streamToRunOn));
      }
    }
    sdkStopTimer(&gpuLaunchCallsTimer);

    gpuLaunchCallsTimes[i] += sdkGetAverageTimerValue(&gpuLaunchCallsTimer);
    sdkResetTimer(&gpuLaunchCallsTimer);

    if (someTransferOpRequired) {
      sdkStartTimer(&gpuTransferCallsTimer);
      if (hintsRequired) {
        if (deviceProp.concurrentManagedAccess) {
          checkCudaErrors(cudaMemPrefetchAsync(dptrA, size, cudaCpuDeviceId));
          checkCudaErrors(cudaMemPrefetchAsync(dptrB, size, cudaCpuDeviceId));
          checkCudaErrors(cudaMemPrefetchAsync(dptrC, size, cudaCpuDeviceId));
        } else {
          checkCudaErrors(cudaStreamAttachMemAsync(streamToRunOn, dptrA, 0,
                                                   cudaMemAttachHost));
          checkCudaErrors(cudaStreamAttachMemAsync(streamToRunOn, dptrB, 0,
                                                   cudaMemAttachHost));
          checkCudaErrors(cudaStreamAttachMemAsync(streamToRunOn, dptrC, 0,
                                                   cudaMemAttachHost));
        }
        if (!isAsync) {
          checkCudaErrors(cudaStreamSynchronize(streamToRunOn));
        }
      }
      if (copyRequired) {
        if (isAsync) {
          checkCudaErrors(cudaMemcpyAsync(
              hptrC, dptrC, size, cudaMemcpyDeviceToHost, streamToRunOn));
        } else {
          checkCudaErrors(
              cudaMemcpy(hptrC, dptrC, size, cudaMemcpyDeviceToHost));
        }
      }
      sdkStopTimer(&gpuTransferCallsTimer);
      gpuTransferFromCallsTimes[i] +=
          sdkGetAverageTimerValue(&gpuTransferCallsTimer);
      sdkResetTimer(&gpuTransferCallsTimer);
    }
    gpuLaunchAndTransferCallsTimes[i] = gpuLaunchCallsTimes[i] +
                                        gpuTransferToCallsTimes[i] +
                                        gpuTransferFromCallsTimes[i];
    gpuLaunchTransferSyncTimes[i] = gpuLaunchAndTransferCallsTimes[i];
    if (isAsync) {
      sdkStartTimer(&gpuSyncTimer);
      {
        if (hintsRequired) {
          *latch = 1;
        }
        checkCudaErrors(cudaStreamSynchronize(streamToRunOn));
      }
      sdkStopTimer(&gpuSyncTimer);
      gpuLaunchTransferSyncTimes[i] += sdkGetAverageTimerValue(&gpuSyncTimer);
      sdkResetTimer(&gpuSyncTimer);
    }

    sdkStartTimer(&cpuAccessTimer);
    {
      verifyMatrixData(
          (i & 0x1 == 0) ? randValuesVerifyXmulY : randValuesVerifyYmulX, hptrC,
          matrixDim);
    }
    sdkStopTimer(&cpuAccessTimer);
    cpuAccessTimes[i] += sdkGetAverageTimerValue(&cpuAccessTimer);
    sdkResetTimer(&cpuAccessTimer);
    overallTimes[i] = cpuAccessTimes[i] + gpuLaunchTransferSyncTimes[i];
  }

  switch (allocType) {
    case USE_HOST_PAGEABLE_AND_DEVICE_MEMORY:
    case USE_HOST_PAGEABLE_AND_DEVICE_MEMORY_ASYNC:
      free(hptrA);
      free(hptrB);
      free(hptrC);
      checkCudaErrors(cudaFree(dptrA));
      checkCudaErrors(cudaFree(dptrB));
      checkCudaErrors(cudaFree(dptrC));
      break;

    case USE_HOST_PAGELOCKED_AND_DEVICE_MEMORY:
    case USE_HOST_PAGELOCKED_AND_DEVICE_MEMORY_ASYNC:
      checkCudaErrors(cudaFreeHost(hptrA));
      checkCudaErrors(cudaFreeHost(hptrB));
      checkCudaErrors(cudaFreeHost(hptrC));
      checkCudaErrors(cudaFree(dptrA));
      checkCudaErrors(cudaFree(dptrB));
      checkCudaErrors(cudaFree(dptrC));
      break;

    case USE_ZERO_COPY:
      checkCudaErrors(cudaFreeHost(hptrA));
      checkCudaErrors(cudaFreeHost(hptrB));
      checkCudaErrors(cudaFreeHost(hptrC));
      break;

    case USE_MANAGED_MEMORY:
    case USE_MANAGED_MEMORY_WITH_HINTS:
    case USE_MANAGED_MEMORY_WITH_HINTS_ASYNC:
      checkCudaErrors(cudaFree(dptrA));
      checkCudaErrors(cudaFree(dptrB));
      checkCudaErrors(cudaFree(dptrC));
      break;

    default:
      exit(EXIT_FAILURE);  // exit due to error
  }

  checkCudaErrors(cudaStreamDestroy(streamToRunOn));
  checkCudaErrors(cudaFreeHost(latch));
  free(randValuesX);
  free(randValuesY);
  free(randValuesVerifyXmulY);
  free(randValuesVerifyYmulX);
  sdkDeleteTimer(&gpuLaunchCallsTimer);
  sdkDeleteTimer(&gpuTransferCallsTimer);
  sdkDeleteTimer(&gpuSyncTimer);
  sdkDeleteTimer(&cpuAccessTimer);
}

void matrixMultiplyPerfRunner(bool reportAsBandwidth,
                              bool print_launch_transfer_results,
                              bool print_std_deviation, int device_id) {
  int i;
  unsigned int minMatrixDim = 32;
  unsigned int multiplierDim = 2;
  unsigned int matrixDim;
  unsigned int minSize = minMatrixDim * minMatrixDim * sizeof(float);
  unsigned int maxSize =
      (maxSampleSizeInMb * ONE_MB) /
      4;  // 3 buffers are used, but dividing by 4 (power of 2)
  unsigned int multiplier = multiplierDim * multiplierDim;
  unsigned int numSizesToTest;

  struct testResults *results;
  struct resultsData *gpuLaunchCallsTimes;
  struct resultsData *gpuTransferToCallsTimes;
  struct resultsData *gpuTransferFromCallsTimes;
  struct resultsData *gpuLaunchAndTransferCallsTimes;
  struct resultsData *gpuLaunchTransferSyncTimes;
  struct resultsData *cpuAccessTimes;
  struct resultsData *overallTimes;
  unsigned long *sizesToTest;
  unsigned int j;

  numSizesToTest = findNumSizesToTest(minSize, maxSize, multiplier);

  createAndInitTestResults(&results, "matrixMultiplyPerf", numKernelRuns,
                           numSizesToTest);

  sizesToTest = getPtrSizesToTest(results);

  createResultDataAndAddToTestResults(&gpuLaunchCallsTimes, results,
                                      "GPU Kernel Launch Call Time", false,
                                      reportAsBandwidth);
  createResultDataAndAddToTestResults(&gpuTransferToCallsTimes, results,
                                      "CPU to GPU Transfer Calls Time", false,
                                      reportAsBandwidth);
  createResultDataAndAddToTestResults(&gpuTransferFromCallsTimes, results,
                                      "GPU to CPU Transfer Calls Time", false,
                                      reportAsBandwidth);
  createResultDataAndAddToTestResults(&gpuLaunchAndTransferCallsTimes, results,
                                      "GPU Launch and Transfer Calls Time",
                                      false, reportAsBandwidth);
  createResultDataAndAddToTestResults(&gpuLaunchTransferSyncTimes, results,
                                      "GPU Launch Transfer and Sync Time",
                                      false, reportAsBandwidth);
  createResultDataAndAddToTestResults(
      &cpuAccessTimes, results, "CPU Access Time", false, reportAsBandwidth);
  createResultDataAndAddToTestResults(&overallTimes, results, "Overall Time",
                                      false, reportAsBandwidth);

  printf("Running ");
  for (matrixDim = minMatrixDim, j = 0;
       matrixDim * matrixDim <= maxSize / sizeof(float);
       matrixDim *= multiplierDim, ++j) {
    sizesToTest[j] = matrixDim * matrixDim * sizeof(float);
    for (i = MEMALLOC_TYPE_START; i <= MEMALLOC_TYPE_END; i++) {
      printf(".");
      fflush(stdout);
      runMatrixMultiplyKernel(
          matrixDim, i, numKernelRuns,
          getPtrRunTimesInMs(gpuLaunchCallsTimes, i, j),
          getPtrRunTimesInMs(gpuTransferToCallsTimes, i, j),
          getPtrRunTimesInMs(gpuTransferFromCallsTimes, i, j),
          getPtrRunTimesInMs(gpuLaunchAndTransferCallsTimes, i, j),
          getPtrRunTimesInMs(gpuLaunchTransferSyncTimes, i, j),
          getPtrRunTimesInMs(cpuAccessTimes, i, j),
          getPtrRunTimesInMs(overallTimes, i, j), device_id);
    }
  }
  printf("\n");
  printResults(results, print_launch_transfer_results, print_std_deviation);
  freeTestResultsAndAllResultsData(results);
}

static void usage() {
  printf(
      "./cudaMemoryTypesPerf [-device=<device_id>] [-reportAsBandwidth] "
      "[-print-launch-transfer-results] [-print-std-deviation] [-verbose]\n");
  printf("Options:\n");
  printf(
      "-reportAsBandwidth:             By default time taken is printed, this "
      "option allows to instead print bandwidth.\n");
  printf(
      "-print-launch-transfer-results: By default overall results are printed, "
      "this option allows to print data transfers and kernel time as well.\n");
  printf(
      "-print-std-deviation:           Prints std deviation of the results.\n");
  printf(
      "-kernel-iterations=<num>:       Number of times the kernel tests should "
      "be run[default is 100 iterations].\n");
  printf(
      "-device=<device_id>:            Allows to pass GPU Device ID on which "
      "the tests will be run.\n");
  printf("-verbose:                       Prints highly verbose output.\n");
}

int main(int argc, char **argv) {
  bool reportAsBandwidth = false;
  bool print_launch_transfer_results = false;
  bool print_std_deviation = false;

  if (checkCmdLineFlag(argc, (const char **)argv, "help") ||
      checkCmdLineFlag(argc, (const char **)argv, "h")) {
    usage();
    printf("&&&& %s WAIVED\n", argv[0]);
    exit(EXIT_WAIVED);
  }

  if (checkCmdLineFlag(argc, (const char **)argv, "reportAsBandwidth")) {
    reportAsBandwidth = true;
  }

  if (checkCmdLineFlag(argc, (const char **)argv,
                       "print-launch-transfer-results")) {
    print_launch_transfer_results = true;
  }

  if (checkCmdLineFlag(argc, (const char **)argv, "print-std-deviation")) {
    print_std_deviation = true;
  }

  if (checkCmdLineFlag(argc, (const char **)argv, "kernel-iterations")) {
    numKernelRuns =
        getCmdLineArgumentInt(argc, (const char **)argv, "kernel-iterations");
  }

  if (checkCmdLineFlag(argc, (const char **)argv, "verbose")) {
    verboseResults = 1;
  }

  int device_id = findCudaDevice(argc, (const char **)argv);

  matrixMultiplyPerfRunner(reportAsBandwidth, print_launch_transfer_results,
                           print_std_deviation, device_id);

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