ViennaCL - The Vienna Computing Library  1.7.1
Free open-source GPU-accelerated linear algebra and solver library.
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multithreaded_cg.cpp

This tutorial shows how to run multiple instances of a conjugate gradient solver, one instance per GPU.

We start with including the necessary headers:

#ifndef VIENNACL_WITH_OPENCL
#define VIENNACL_WITH_OPENCL
#endif
// include necessary system headers
#include <iostream>
//
// ublas includes
//
#include <boost/numeric/ublas/io.hpp>
#include <boost/numeric/ublas/triangular.hpp>
#include <boost/numeric/ublas/matrix_sparse.hpp>
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/numeric/ublas/matrix_proxy.hpp>
#include <boost/numeric/ublas/operation.hpp>
#include <boost/numeric/ublas/operation_sparse.hpp>
#include <boost/numeric/ublas/io.hpp>
#include <boost/numeric/ublas/lu.hpp>
// Must be set if you want to use ViennaCL algorithms on ublas objects
#define VIENNACL_WITH_UBLAS 1
//include basic scalar and vector types of ViennaCL
#include "viennacl/scalar.hpp"
#include "viennacl/vector.hpp"
#include "viennacl/compressed_matrix.hpp"
#include "viennacl/io/matrix_market.hpp"
#include "viennacl/ocl/device.hpp"
#include "viennacl/ocl/platform.hpp"
#include "viennacl/ocl/backend.hpp"
//include the generic inner product functions of ViennaCL
#include "viennacl/linalg/norm_2.hpp"
#include "viennacl/linalg/cg.hpp"
// Some helper functions for this tutorial:
#include "vector-io.hpp"
using namespace boost::numeric;

This tutorial uses Boost.Thread for threading. Other threading approaches (e.g. pthreads) also work.

#include <boost/thread.hpp>

This functor represents the work carried out in each thread. It creates the necessary objects, loads the data, and executes the CG solver.

template<typename NumericT>
class worker
{
public:
worker(std::size_t tid) : thread_id_(tid) {}

The functor interface, entry point for each thread.

void operator()()
{

Set up some ublas objects

ublas::vector<NumericT> rhs;
ublas::vector<NumericT> ref_result;
ublas::compressed_matrix<NumericT> ublas_matrix;

Read system from file. You may also assemble everything on the fly here.

if (!viennacl::io::read_matrix_market_file(ublas_matrix, "../examples/testdata/mat65k.mtx"))
{
std::cout << "Error reading Matrix file" << std::endl;
return;
}
if (!readVectorFromFile("../examples/testdata/rhs65025.txt", rhs))
{
std::cout << "Error reading RHS file" << std::endl;
return;
}
if (!readVectorFromFile("../examples/testdata/result65025.txt", ref_result))
{
std::cout << "Error reading Result file" << std::endl;
return;
}

Set up some ViennaCL objects in the respective context. It is important to place the objects in the correct context (associated with each thread)

viennacl::context ctx(viennacl::ocl::get_context(static_cast<long>(thread_id_)));
std::size_t vcl_size = rhs.size();
viennacl::compressed_matrix<NumericT> vcl_compressed_matrix(ctx);
viennacl::vector<NumericT> vcl_rhs(vcl_size, ctx);
viennacl::vector<NumericT> vcl_ref_result(vcl_size, ctx);
viennacl::copy(rhs.begin(), rhs.end(), vcl_rhs.begin());
viennacl::copy(ref_result.begin(), ref_result.end(), vcl_ref_result.begin());

Transfer ublas-matrix to ViennaCL objects sitting on the GPU:

viennacl::copy(ublas_matrix, vcl_compressed_matrix);
viennacl::vector<NumericT> vcl_result = viennacl::linalg::solve(vcl_compressed_matrix, vcl_rhs, viennacl::linalg::cg_tag());
std::stringstream ss;
ss << "Result of thread " << thread_id_ << " on device " << viennacl::ocl::get_context(static_cast<long>(thread_id_)).devices()[0].name() << ": " << vcl_result[0] << ", should: " << ref_result[0] << std::endl;
message_ = ss.str();
}
std::string message() const { return message_; }
private:
std::string message_;
std::size_t thread_id_;
};

In the main routine we create two OpenCL contexts and then use one thread per context to run the CG solver in the functor defined above.

int main()
{
//Change this type definition to double if your gpu supports that
typedef float ScalarType;
if (viennacl::ocl::get_platforms().size() == 0)
{
std::cerr << "Error: No platform found!" << std::endl;
return EXIT_FAILURE;
}

Part 1: Setup first device for first context, second device for second context:

viennacl::ocl::platform pf = viennacl::ocl::get_platforms()[0];
std::vector<viennacl::ocl::device> const & devices = pf.devices();
// Set first device to first context:
viennacl::ocl::setup_context(0, devices[0]);
// Set second device for second context (use the same device for the second context if only one device available):
if (devices.size() > 1)
viennacl::ocl::setup_context(1, devices[1]);
else
viennacl::ocl::setup_context(1, devices[0]);

Part 2: Now let two threads operate on two GPUs in parallel, each running a CG solver

worker<ScalarType> work_functor0(0);
worker<ScalarType> work_functor1(1);
boost::thread worker_thread_0(boost::ref(work_functor0));
boost::thread worker_thread_1(boost::ref(work_functor1));
worker_thread_0.join();
worker_thread_1.join();
std::cout << work_functor0.message() << std::endl;
std::cout << work_functor1.message() << std::endl;

That's it. Print a success message and exit.

std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
return EXIT_SUCCESS;
}

Full Example Code

/* =========================================================================
Copyright (c) 2010-2016, Institute for Microelectronics,
Institute for Analysis and Scientific Computing,
TU Wien.
Portions of this software are copyright by UChicago Argonne, LLC.
-----------------
ViennaCL - The Vienna Computing Library
-----------------
Project Head: Karl Rupp rupp@iue.tuwien.ac.at
(A list of authors and contributors can be found in the PDF manual)
License: MIT (X11), see file LICENSE in the base directory
============================================================================= */
#ifndef VIENNACL_WITH_OPENCL
#define VIENNACL_WITH_OPENCL
#endif
// include necessary system headers
#include <iostream>
//
// ublas includes
//
#include <boost/numeric/ublas/io.hpp>
#include <boost/numeric/ublas/triangular.hpp>
#include <boost/numeric/ublas/matrix_sparse.hpp>
#include <boost/numeric/ublas/matrix.hpp>
#include <boost/numeric/ublas/matrix_proxy.hpp>
#include <boost/numeric/ublas/operation.hpp>
#include <boost/numeric/ublas/operation_sparse.hpp>
#include <boost/numeric/ublas/io.hpp>
#include <boost/numeric/ublas/lu.hpp>
// Must be set if you want to use ViennaCL algorithms on ublas objects
#define VIENNACL_WITH_UBLAS 1
//include basic scalar and vector types of ViennaCL
#include "viennacl/scalar.hpp"
#include "viennacl/vector.hpp"
#include "viennacl/compressed_matrix.hpp"
#include "viennacl/io/matrix_market.hpp"
#include "viennacl/ocl/device.hpp"
#include "viennacl/ocl/platform.hpp"
#include "viennacl/ocl/backend.hpp"
//include the generic inner product functions of ViennaCL
#include "viennacl/linalg/norm_2.hpp"
#include "viennacl/linalg/cg.hpp"
// Some helper functions for this tutorial:
#include "vector-io.hpp"
using namespace boost::numeric;
#include <boost/thread.hpp>
template<typename NumericT>
class worker
{
public:
worker(std::size_t tid) : thread_id_(tid) {}
void operator()()
{
ublas::vector<NumericT> rhs;
ublas::vector<NumericT> ref_result;
ublas::compressed_matrix<NumericT> ublas_matrix;
if (!viennacl::io::read_matrix_market_file(ublas_matrix, "../examples/testdata/mat65k.mtx"))
{
std::cout << "Error reading Matrix file" << std::endl;
return;
}
if (!readVectorFromFile("../examples/testdata/rhs65025.txt", rhs))
{
std::cout << "Error reading RHS file" << std::endl;
return;
}
if (!readVectorFromFile("../examples/testdata/result65025.txt", ref_result))
{
std::cout << "Error reading Result file" << std::endl;
return;
}
viennacl::context ctx(viennacl::ocl::get_context(static_cast<long>(thread_id_)));
std::size_t vcl_size = rhs.size();
viennacl::compressed_matrix<NumericT> vcl_compressed_matrix(ctx);
viennacl::vector<NumericT> vcl_rhs(vcl_size, ctx);
viennacl::vector<NumericT> vcl_ref_result(vcl_size, ctx);
viennacl::copy(rhs.begin(), rhs.end(), vcl_rhs.begin());
viennacl::copy(ref_result.begin(), ref_result.end(), vcl_ref_result.begin());
viennacl::copy(ublas_matrix, vcl_compressed_matrix);
viennacl::vector<NumericT> vcl_result = viennacl::linalg::solve(vcl_compressed_matrix, vcl_rhs, viennacl::linalg::cg_tag());
std::stringstream ss;
ss << "Result of thread " << thread_id_ << " on device " << viennacl::ocl::get_context(static_cast<long>(thread_id_)).devices()[0].name() << ": " << vcl_result[0] << ", should: " << ref_result[0] << std::endl;
message_ = ss.str();
}
std::string message() const { return message_; }
private:
std::string message_;
std::size_t thread_id_;
};
int main()
{
//Change this type definition to double if your gpu supports that
typedef float ScalarType;
if (viennacl::ocl::get_platforms().size() == 0)
{
std::cerr << "Error: No platform found!" << std::endl;
return EXIT_FAILURE;
}
viennacl::ocl::platform pf = viennacl::ocl::get_platforms()[0];
std::vector<viennacl::ocl::device> const & devices = pf.devices();
// Set first device to first context:
viennacl::ocl::setup_context(0, devices[0]);
// Set second device for second context (use the same device for the second context if only one device available):
if (devices.size() > 1)
viennacl::ocl::setup_context(1, devices[1]);
else
viennacl::ocl::setup_context(1, devices[0]);
worker<ScalarType> work_functor0(0);
worker<ScalarType> work_functor1(1);
boost::thread worker_thread_0(boost::ref(work_functor0));
boost::thread worker_thread_1(boost::ref(work_functor1));
worker_thread_0.join();
worker_thread_1.join();
std::cout << work_functor0.message() << std::endl;
std::cout << work_functor1.message() << std::endl;
std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
return EXIT_SUCCESS;
}