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/* Copyright (c) 2022, 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.
*/
/*
* See: https://www.piday.org/million/
*/
#include "MonteCarloPi.h"
#include <algorithm>
#define CUDA_DRIVER_API
#include <helper_cuda.h>
#include <iostream>
#define ROUND_UP_TO_GRANULARITY(x, n) (((x + n - 1) / n) * n)
// `ipcHandleTypeFlag` specifies the platform specific handle type this sample
// uses for importing and exporting memory allocation. On Linux this sample
// specifies the type as CU_MEM_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR meaning that
// file descriptors will be used. On Windows this sample specifies the type as
// CU_MEM_HANDLE_TYPE_WIN32 meaning that NT HANDLEs will be used. The
// ipcHandleTypeFlag variable is a convenience variable and is passed by value
// to individual requests.
#if defined(__linux__)
CUmemAllocationHandleType ipcHandleTypeFlag =
CU_MEM_HANDLE_TYPE_POSIX_FILE_DESCRIPTOR;
#else
CUmemAllocationHandleType ipcHandleTypeFlag = CU_MEM_HANDLE_TYPE_WIN32;
#endif
// Windows-specific LPSECURITYATTRIBUTES
void getDefaultSecurityDescriptor(CUmemAllocationProp *prop) {
#if defined(__linux__)
return;
#elif defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
static const char sddl[] = "D:P(OA;;GARCSDWDWOCCDCLCSWLODTWPRPCRFA;;;WD)";
static OBJECT_ATTRIBUTES objAttributes;
static bool objAttributesConfigured = false;
if (!objAttributesConfigured) {
PSECURITY_DESCRIPTOR secDesc;
BOOL result = ConvertStringSecurityDescriptorToSecurityDescriptorA(
sddl, SDDL_REVISION_1, &secDesc, NULL);
if (result == 0) {
printf("IPC failure: getDefaultSecurityDescriptor Failed! (%d)\n",
GetLastError());
}
InitializeObjectAttributes(&objAttributes, NULL, 0, NULL, secDesc);
objAttributesConfigured = true;
}
prop->win32HandleMetaData = &objAttributes;
return;
#endif
}
__global__ void monte_carlo_kernel(vec2 *xyVector, float *pointsInsideCircle,
float *numPointsInCircle,
unsigned int numPoints, float time) {
const size_t stride = gridDim.x * blockDim.x;
size_t tid = blockIdx.x * blockDim.x + threadIdx.x;
float count = 0.0f;
curandState rgnState;
curand_init((unsigned long long)time, tid, 0, &rgnState);
for (; tid < numPoints; tid += stride) {
float x = curand_uniform(&rgnState);
float y = curand_uniform(&rgnState);
x = (2.0f * x) - 1.0f;
y = (2.0f * y) - 1.0f;
xyVector[tid][0] = x;
xyVector[tid][1] = y;
// Compute the distance of this point form the center(0, 0)
float dist = sqrtf((x * x) + (y * y));
// If distance is less than the radius of the unit circle, the point lies in
// the circle.
pointsInsideCircle[tid] = (dist <= 1.0f);
count += (dist <= 1.0f);
}
atomicAdd(numPointsInCircle, count);
}
MonteCarloPiSimulation::MonteCarloPiSimulation(size_t num_points)
: m_xyVector(nullptr),
m_pointsInsideCircle(nullptr),
m_totalPointsInsideCircle(0),
m_totalPointsSimulated(0),
m_numPoints(num_points) {}
MonteCarloPiSimulation::~MonteCarloPiSimulation() {
if (m_numPointsInCircle) {
checkCudaErrors(cudaFree(m_numPointsInCircle));
m_numPointsInCircle = nullptr;
}
if (m_hostNumPointsInCircle) {
checkCudaErrors(cudaFreeHost(m_hostNumPointsInCircle));
m_hostNumPointsInCircle = nullptr;
}
cleanupSimulationAllocations();
}
void MonteCarloPiSimulation::initSimulation(int cudaDevice,
cudaStream_t stream) {
m_cudaDevice = cudaDevice;
getIdealExecutionConfiguration();
// Allocate a position buffer that contains random location of the points in
// XY cartesian plane.
// Allocate a bitmap buffer which holds information of whether a point in the
// position buffer is inside the unit circle or not.
setupSimulationAllocations();
checkCudaErrors(
cudaMalloc((float **)&m_numPointsInCircle, sizeof(*m_numPointsInCircle)));
checkCudaErrors(cudaMallocHost((float **)&m_hostNumPointsInCircle,
sizeof(*m_hostNumPointsInCircle)));
}
void MonteCarloPiSimulation::stepSimulation(float time, cudaStream_t stream) {
checkCudaErrors(cudaMemsetAsync(m_numPointsInCircle, 0,
sizeof(*m_numPointsInCircle), stream));
monte_carlo_kernel<<<m_blocks, m_threads, 0, stream>>>(
m_xyVector, m_pointsInsideCircle, m_numPointsInCircle, m_numPoints, time);
getLastCudaError("Failed to launch CUDA simulation");
checkCudaErrors(cudaMemcpyAsync(m_hostNumPointsInCircle, m_numPointsInCircle,
sizeof(*m_numPointsInCircle),
cudaMemcpyDeviceToHost, stream));
// Queue up a stream callback to compute and print the PI value.
checkCudaErrors(
cudaLaunchHostFunc(stream, this->computePiCallback, (void *)this));
}
void MonteCarloPiSimulation::computePiCallback(void *args) {
MonteCarloPiSimulation *cbData = (MonteCarloPiSimulation *)args;
cbData->m_totalPointsInsideCircle += *(cbData->m_hostNumPointsInCircle);
cbData->m_totalPointsSimulated += cbData->m_numPoints;
double piValue = 4.0 * ((double)cbData->m_totalPointsInsideCircle /
(double)cbData->m_totalPointsSimulated);
printf("Approximate Pi value for %zd data points: %lf \n",
cbData->m_totalPointsSimulated, piValue);
}
void MonteCarloPiSimulation::getIdealExecutionConfiguration() {
int warpSize = 0;
int multiProcessorCount = 0;
checkCudaErrors(cudaSetDevice(m_cudaDevice));
checkCudaErrors(
cudaDeviceGetAttribute(&warpSize, cudaDevAttrWarpSize, m_cudaDevice));
// We don't need large block sizes, since there's not much inter-thread
// communication
m_threads = warpSize;
// Use the occupancy calculator and fill the gpu as best as we can
checkCudaErrors(cudaOccupancyMaxActiveBlocksPerMultiprocessor(
&m_blocks, monte_carlo_kernel, warpSize, 0));
checkCudaErrors(cudaDeviceGetAttribute(
&multiProcessorCount, cudaDevAttrMultiProcessorCount, m_cudaDevice));
m_blocks *= multiProcessorCount;
// Go ahead and the clamp the blocks to the minimum needed for this
// height/width
m_blocks =
std::min(m_blocks, (int)((m_numPoints + m_threads - 1) / m_threads));
}
void MonteCarloPiSimulation::setupSimulationAllocations() {
CUdeviceptr d_ptr = 0U;
size_t granularity = 0;
CUmemGenericAllocationHandle cudaPositionHandle, cudaInCircleHandle;
CUmemAllocationProp allocProp = {};
allocProp.type = CU_MEM_ALLOCATION_TYPE_PINNED;
allocProp.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
allocProp.location.id = m_cudaDevice;
allocProp.win32HandleMetaData = NULL;
allocProp.requestedHandleTypes = ipcHandleTypeFlag;
// Windows-specific LPSECURITYATTRIBUTES is required when
// CU_MEM_HANDLE_TYPE_WIN32 is used. The security attribute defines the scope
// of which exported allocations may be tranferred to other processes. For all
// other handle types, pass NULL.
getDefaultSecurityDescriptor(&allocProp);
// Get the recommended granularity for m_cudaDevice.
checkCudaErrors(cuMemGetAllocationGranularity(
&granularity, &allocProp, CU_MEM_ALLOC_GRANULARITY_RECOMMENDED));
size_t xyPositionVecSize = m_numPoints * sizeof(*m_xyVector);
size_t inCircleVecSize = m_numPoints * sizeof(*m_pointsInsideCircle);
size_t xyPositionSize =
ROUND_UP_TO_GRANULARITY(xyPositionVecSize, granularity);
size_t inCircleSize = ROUND_UP_TO_GRANULARITY(inCircleVecSize, granularity);
m_totalAllocationSize = (xyPositionSize + inCircleSize);
// Reserve the required contiguous VA space for the allocations
checkCudaErrors(
cuMemAddressReserve(&d_ptr, m_totalAllocationSize, granularity, 0U, 0));
// Create the allocations as a pinned allocation on this device.
// Create an allocation to store all the positions of points on the xy plane
// and a second allocation which stores information if the corresponding
// position is inside the unit circle or not.
checkCudaErrors(
cuMemCreate(&cudaPositionHandle, xyPositionSize, &allocProp, 0));
checkCudaErrors(
cuMemCreate(&cudaInCircleHandle, inCircleSize, &allocProp, 0));
// Export the allocation to a platform-specific handle. The type of handle
// requested here must match the requestedHandleTypes field in the prop
// structure passed to cuMemCreate. The handle obtained here will be passed to
// vulkan to import the allocation.
checkCudaErrors(cuMemExportToShareableHandle(
(void *)&m_posShareableHandle, cudaPositionHandle, ipcHandleTypeFlag, 0));
checkCudaErrors(
cuMemExportToShareableHandle((void *)&m_inCircleShareableHandle,
cudaInCircleHandle, ipcHandleTypeFlag, 0));
CUdeviceptr va_position = d_ptr;
CUdeviceptr va_InCircle = va_position + xyPositionSize;
m_pointsInsideCircle = (float *)va_InCircle;
m_xyVector = (vec2 *)va_position;
// Assign the chunk to the appropriate VA range
checkCudaErrors(
cuMemMap(va_position, xyPositionSize, 0, cudaPositionHandle, 0));
checkCudaErrors(
cuMemMap(va_InCircle, inCircleSize, 0, cudaInCircleHandle, 0));
// Release the handles for the allocation. Since the allocation is currently
// mapped to a VA range with a previous call to cuMemMap the actual freeing of
// memory allocation will happen on an eventual call to cuMemUnmap. Thus the
// allocation will be kept live until it is unmapped.
checkCudaErrors(cuMemRelease(cudaPositionHandle));
checkCudaErrors(cuMemRelease(cudaInCircleHandle));
CUmemAccessDesc accessDescriptor = {};
accessDescriptor.location.id = m_cudaDevice;
accessDescriptor.location.type = CU_MEM_LOCATION_TYPE_DEVICE;
accessDescriptor.flags = CU_MEM_ACCESS_FLAGS_PROT_READWRITE;
// Apply the access descriptor to the whole VA range. Essentially enables
// Read-Write access to the range.
checkCudaErrors(
cuMemSetAccess(d_ptr, m_totalAllocationSize, &accessDescriptor, 1));
}
void MonteCarloPiSimulation::cleanupSimulationAllocations() {
if (m_xyVector && m_pointsInsideCircle) {
// Unmap the mapped virtual memory region
// Since the handles to the mapped backing stores have already been released
// by cuMemRelease, and these are the only/last mappings referencing them,
// The backing stores will be freed.
checkCudaErrors(cuMemUnmap((CUdeviceptr)m_xyVector, m_totalAllocationSize));
checkIpcErrors(ipcCloseShareableHandle(m_posShareableHandle));
checkIpcErrors(ipcCloseShareableHandle(m_inCircleShareableHandle));
// Free the virtual address region.
checkCudaErrors(
cuMemAddressFree((CUdeviceptr)m_xyVector, m_totalAllocationSize));
m_xyVector = nullptr;
m_pointsInsideCircle = nullptr;
}
}
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