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// Ceres Solver - A fast non-linear least squares minimizer
// Copyright 2018 Google Inc. All rights reserved.
// http://ceres-solver.org/
//
// 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 Google Inc. 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 AND CONTRIBUTORS "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.
//
// Author: vitus@google.com (Michael Vitus)
#include "ceres/context_impl.h"
#include <string>
#include "ceres/internal/config.h"
#include "ceres/stringprintf.h"
#include "ceres/wall_time.h"
#ifndef CERES_NO_CUDA
#include "cublas_v2.h"
#include "cuda_runtime.h"
#include "cusolverDn.h"
#endif // CERES_NO_CUDA
namespace ceres::internal {
ContextImpl::ContextImpl() = default;
#ifndef CERES_NO_CUDA
void ContextImpl::TearDown() {
if (cusolver_handle_ != nullptr) {
cusolverDnDestroy(cusolver_handle_);
cusolver_handle_ = nullptr;
}
if (cublas_handle_ != nullptr) {
cublasDestroy(cublas_handle_);
cublas_handle_ = nullptr;
}
if (cusparse_handle_ != nullptr) {
cusparseDestroy(cusparse_handle_);
cusparse_handle_ = nullptr;
}
if (stream_ != nullptr) {
cudaStreamDestroy(stream_);
stream_ = nullptr;
}
is_cuda_initialized_ = false;
}
std::string ContextImpl::CudaConfigAsString() const {
return ceres::internal::StringPrintf(
"======================= CUDA Device Properties ======================\n"
"Cuda version : %d.%d\n"
"Device ID : %d\n"
"Device name : %s\n"
"Total GPU memory : %6.f MiB\n"
"GPU memory available : %6.f MiB\n"
"Compute capability : %d.%d\n"
"Warp size : %d\n"
"Max threads per block: %d\n"
"Max threads per dim : %d %d %d\n"
"Max grid size : %d %d %d\n"
"Multiprocessor count : %d\n"
"====================================================================",
cuda_version_major_,
cuda_version_minor_,
gpu_device_id_in_use_,
gpu_device_properties_.name,
gpu_device_properties_.totalGlobalMem / 1024.0 / 1024.0,
GpuMemoryAvailable() / 1024.0 / 1024.0,
gpu_device_properties_.major,
gpu_device_properties_.minor,
gpu_device_properties_.warpSize,
gpu_device_properties_.maxThreadsPerBlock,
gpu_device_properties_.maxThreadsDim[0],
gpu_device_properties_.maxThreadsDim[1],
gpu_device_properties_.maxThreadsDim[2],
gpu_device_properties_.maxGridSize[0],
gpu_device_properties_.maxGridSize[1],
gpu_device_properties_.maxGridSize[2],
gpu_device_properties_.multiProcessorCount);
}
size_t ContextImpl::GpuMemoryAvailable() const {
size_t free, total;
cudaMemGetInfo(&free, &total);
return free;
}
bool ContextImpl::InitCuda(std::string* message) {
if (is_cuda_initialized_) {
return true;
}
CHECK_EQ(cudaGetDevice(&gpu_device_id_in_use_), cudaSuccess);
int cuda_version;
CHECK_EQ(cudaRuntimeGetVersion(&cuda_version), cudaSuccess);
cuda_version_major_ = cuda_version / 1000;
cuda_version_minor_ = (cuda_version % 1000) / 10;
CHECK_EQ(
cudaGetDeviceProperties(&gpu_device_properties_, gpu_device_id_in_use_),
cudaSuccess);
VLOG(3) << "\n" << CudaConfigAsString();
EventLogger event_logger("InitCuda");
if (cublasCreate(&cublas_handle_) != CUBLAS_STATUS_SUCCESS) {
*message =
"CUDA initialization failed because cuBLAS::cublasCreate failed.";
cublas_handle_ = nullptr;
return false;
}
event_logger.AddEvent("cublasCreate");
if (cusolverDnCreate(&cusolver_handle_) != CUSOLVER_STATUS_SUCCESS) {
*message =
"CUDA initialization failed because cuSolverDN::cusolverDnCreate "
"failed.";
TearDown();
return false;
}
event_logger.AddEvent("cusolverDnCreate");
if (cusparseCreate(&cusparse_handle_) != CUSPARSE_STATUS_SUCCESS) {
*message =
"CUDA initialization failed because cuSPARSE::cusparseCreate failed.";
TearDown();
return false;
}
event_logger.AddEvent("cusparseCreate");
if (cudaStreamCreateWithFlags(&stream_, cudaStreamNonBlocking) !=
cudaSuccess) {
*message =
"CUDA initialization failed because CUDA::cudaStreamCreateWithFlags "
"failed.";
TearDown();
return false;
}
event_logger.AddEvent("cudaStreamCreateWithFlags");
if (cusolverDnSetStream(cusolver_handle_, stream_) !=
CUSOLVER_STATUS_SUCCESS ||
cublasSetStream(cublas_handle_, stream_) != CUBLAS_STATUS_SUCCESS ||
cusparseSetStream(cusparse_handle_, stream_) != CUSPARSE_STATUS_SUCCESS) {
*message = "CUDA initialization failed because SetStream failed.";
TearDown();
return false;
}
event_logger.AddEvent("SetStream");
is_cuda_initialized_ = true;
return true;
}
#endif // CERES_NO_CUDA
ContextImpl::~ContextImpl() {
#ifndef CERES_NO_CUDA
TearDown();
#endif // CERES_NO_CUDA
}
void ContextImpl::EnsureMinimumThreads(int num_threads) {
thread_pool.Resize(num_threads);
}
} // namespace ceres::internal