void viennacl_gmres(double * result, //output vector
mwIndex * cols, //input vector holding column jumpers
mwIndex * rows, //input vector holding row indices
double *entries,
double *rhs,
mwSize num_cols,
mwSize nnzmax
)
{
viennacl::vector<double> vcl_rhs(num_cols);
viennacl::vector<double> vcl_result(num_cols);
viennacl::compressed_matrix<double> vcl_matrix(num_cols, num_cols);
//convert from column-wise storage to row-wise storage
std::vector< std::map< unsigned int, double > > stl_matrix(num_cols);
for (mwIndex j=0; j<num_cols; ++j)
{
for (mwIndex i = cols[j]; i<cols[j+1]; ++i)
stl_matrix[rows[i]][j] = entries[i];
}
//now copy matrix to GPU:
copy(stl_matrix, vcl_matrix);
copy(rhs, rhs + num_cols, vcl_rhs.begin());
stl_matrix.clear(); //clean up this temporary storage
//solve it:
vcl_result = solve(vcl_matrix,
vcl_rhs,
viennacl::linalg::gmres_tag(1e-8, 30, 20)); //relative tolerance of 1e-8, krylov space of dimension 30, 20 restarts max.
///////////// copy back to CPU: ///////////////////
copy(vcl_result.begin(), vcl_result.end(), result);
return;
}
int main(int argc, char * argv[])
{
// create matrice & vectors -----------------------------------------------------
int n_blocks = 1000;
int w_sub_a = 8;
int h_sub_a = 8;
int w_A = 1 * w_sub_a;
int h_A = 1 * h_sub_a;
boost::numeric::ublas::vector<float> b(w_A);
boost::numeric::ublas::vector<float> c(h_A);
boost::numeric::ublas::compressed_matrix<float> A(h_A, w_A);
viennacl::compressed_matrix<float> vcl_A(h_A, w_A);
// initialize matrice & vectors -----------------------------------------------------
for( int i = 0; i < w_A; i++ )
{
b[i] = rand();
}
// for( int i = 0; i < n_blocks; i++ )
{
int i = 0;
A(i*h_sub_a,i*h_sub_a) = rand();
A(i*h_sub_a,i*h_sub_a+1) = rand();
A(i*h_sub_a+1,i*h_sub_a) = rand();
A(i*h_sub_a+1,i*h_sub_a+1) = rand();
}
//
// Set up some ViennaCL objects
//
//viennacl::vector<float> vcl_b(static_cast<unsigned int>(b.size()));
//viennacl::vector<float> vcl_c(static_cast<unsigned int>(c.size()));
viennacl::matrix<float> vcl_matrix(static_cast<unsigned int>(c.size()), static_cast<unsigned int>(b.size()));
// run with native code ---------------------------------------------------
boost::timer ntimer;
for( int i = 0; i< n_blocks; i++ )
c=boost::numeric::ublas::prod(A,b);
std::cout<<"[native] time: " << ntimer.elapsed() <<" seconds\n";
// run with opencl code -----------------------------------------------------
copy(A, vcl_A);
boost::timer ctimer;
# pragma omp parallel for \
default ( shared )
for( int i = 0; i < n_blocks; i++ )
{
viennacl::vector<float> vcl_b(w_A);
viennacl::vector<float> vcl_c(h_A);
copy(b, vcl_b);
vcl_c=viennacl::linalg::prod(vcl_A,vcl_b);
copy(vcl_c,c );
}
std::cout<<"[native] time: " << ctimer.elapsed() <<" seconds\n";
// clean up memory -------------------------------------------------------
return 0;
}
/**
* With this let us go right to main():
**/
int main()
{
typedef float ScalarType;
/**
* <h2>Part 1: Set up a custom context</h2>
*
* The following is rather lengthy because OpenCL is a fairly low-level framework.
* For comparison, the subsequent code explicitly performs the OpenCL setup that is done
* in the background within the 'custom_kernels'-tutorial
**/
//manually set up a custom OpenCL context:
std::vector<cl_device_id> device_id_array;
//get all available devices
viennacl::ocl::platform pf;
std::cout << "Platform info: " << pf.info() << std::endl;
std::vector<viennacl::ocl::device> devices = pf.devices(CL_DEVICE_TYPE_DEFAULT);
std::cout << devices[0].name() << std::endl;
std::cout << "Number of devices for custom context: " << devices.size() << std::endl;
//set up context using all found devices:
for (std::size_t i=0; i<devices.size(); ++i)
{
device_id_array.push_back(devices[i].id());
}
std::cout << "Creating context..." << std::endl;
cl_int err;
cl_context my_context = clCreateContext(0, cl_uint(device_id_array.size()), &(device_id_array[0]), NULL, NULL, &err);
VIENNACL_ERR_CHECK(err);
//create two Vectors:
unsigned int vector_size = 10;
std::vector<ScalarType> vec1(vector_size);
std::vector<ScalarType> vec2(vector_size);
std::vector<ScalarType> result(vector_size);
//
// fill the operands vec1 and vec2:
//
for (unsigned int i=0; i<vector_size; ++i)
{
vec1[i] = static_cast<ScalarType>(i);
vec2[i] = static_cast<ScalarType>(vector_size-i);
}
//
// create memory in OpenCL context:
//
cl_mem mem_vec1 = clCreateBuffer(my_context, CL_MEM_READ_WRITE | CL_MEM_COPY_HOST_PTR, vector_size * sizeof(ScalarType), &(vec1[0]), &err);
VIENNACL_ERR_CHECK(err);
cl_mem mem_vec2 = clCreateBuffer(my_context, CL_MEM_READ_WRITE | CL_MEM_COPY_HOST_PTR, vector_size * sizeof(ScalarType), &(vec2[0]), &err);
VIENNACL_ERR_CHECK(err);
cl_mem mem_result = clCreateBuffer(my_context, CL_MEM_READ_WRITE | CL_MEM_COPY_HOST_PTR, vector_size * sizeof(ScalarType), &(result[0]), &err);
VIENNACL_ERR_CHECK(err);
//
// create a command queue for each device:
//
std::vector<cl_command_queue> queues(devices.size());
for (std::size_t i=0; i<devices.size(); ++i)
{
queues[i] = clCreateCommandQueue(my_context, devices[i].id(), 0, &err);
VIENNACL_ERR_CHECK(err);
}
//
// create and build a program in the context:
//
std::size_t source_len = std::string(my_compute_program).length();
cl_program my_prog = clCreateProgramWithSource(my_context, 1, &my_compute_program, &source_len, &err);
err = clBuildProgram(my_prog, 0, NULL, NULL, NULL, NULL);
/* char buffer[1024];
cl_build_status status;
clGetProgramBuildInfo(my_prog, devices[1].id(), CL_PROGRAM_BUILD_STATUS, sizeof(cl_build_status), &status, NULL);
clGetProgramBuildInfo(my_prog, devices[1].id(), CL_PROGRAM_BUILD_LOG, sizeof(char)*1024, &buffer, NULL);
std::cout << "Build Scalar: Err = " << err << " Status = " << status << std::endl;
std::cout << "Log: " << buffer << std::endl;*/
VIENNACL_ERR_CHECK(err);
//
// create a kernel from the program:
//
const char * kernel_name = "elementwise_prod";
cl_kernel my_kernel = clCreateKernel(my_prog, kernel_name, &err);
VIENNACL_ERR_CHECK(err);
//
// Execute elementwise_prod kernel on first queue: result = vec1 .* vec2;
//
err = clSetKernelArg(my_kernel, 0, sizeof(cl_mem), (void*)&mem_vec1);
VIENNACL_ERR_CHECK(err);
err = clSetKernelArg(my_kernel, 1, sizeof(cl_mem), (void*)&mem_vec2);
VIENNACL_ERR_CHECK(err);
err = clSetKernelArg(my_kernel, 2, sizeof(cl_mem), (void*)&mem_result);
VIENNACL_ERR_CHECK(err);
err = clSetKernelArg(my_kernel, 3, sizeof(unsigned int), (void*)&vector_size);
VIENNACL_ERR_CHECK(err);
std::size_t global_size = vector_size;
std::size_t local_size = vector_size;
err = clEnqueueNDRangeKernel(queues[0], my_kernel, 1, NULL, &global_size, &local_size, 0, NULL, NULL);
VIENNACL_ERR_CHECK(err);
//
// Read and output result:
//
err = clEnqueueReadBuffer(queues[0], mem_vec1, CL_TRUE, 0, sizeof(ScalarType)*vector_size, &(vec1[0]), 0, NULL, NULL);
VIENNACL_ERR_CHECK(err);
err = clEnqueueReadBuffer(queues[0], mem_result, CL_TRUE, 0, sizeof(ScalarType)*vector_size, &(result[0]), 0, NULL, NULL);
VIENNACL_ERR_CHECK(err);
std::cout << "vec1 : ";
for (std::size_t i=0; i<vec1.size(); ++i)
std::cout << vec1[i] << " ";
std::cout << std::endl;
std::cout << "vec2 : ";
for (std::size_t i=0; i<vec2.size(); ++i)
std::cout << vec2[i] << " ";
std::cout << std::endl;
std::cout << "result: ";
for (std::size_t i=0; i<result.size(); ++i)
std::cout << result[i] << " ";
std::cout << std::endl;
/**
* <h2>Part 2: Reuse Custom OpenCL Context with ViennaCL</h2>
*
* To let ViennaCL reuse the previously created context, we need to make it known to ViennaCL \em before any ViennaCL objects are created.
* We inject the custom context as the context with default id '0' when using viennacl::ocl::switch_context().
**/
viennacl::ocl::setup_context(0, my_context, device_id_array, queues);
viennacl::ocl::switch_context(0); //activate the new context (only mandatory with context-id not equal to zero)
/**
* Check that ViennaCL really uses the new context:
**/
std::cout << "Existing context: " << my_context << std::endl;
std::cout << "ViennaCL uses context: " << viennacl::ocl::current_context().handle().get() << std::endl;
/**
* Wrap existing OpenCL objects into ViennaCL:
**/
viennacl::vector<ScalarType> vcl_vec1(mem_vec1, vector_size);
viennacl::vector<ScalarType> vcl_vec2(mem_vec2, vector_size);
viennacl::vector<ScalarType> vcl_result(mem_result, vector_size);
viennacl::scalar<ScalarType> vcl_s = 2.0;
std::cout << "Standard vector operations within ViennaCL:" << std::endl;
vcl_result = vcl_s * vcl_vec1 + vcl_vec2;
std::cout << "vec1 : ";
std::cout << vcl_vec1 << std::endl;
std::cout << "vec2 : ";
std::cout << vcl_vec2 << std::endl;
std::cout << "result: ";
std::cout << vcl_result << std::endl;
/**
* We can also reuse the existing elementwise_prod kernel.
* Therefore, we first have to make the existing program known to ViennaCL
* For more details on the three lines, see tutorial 'custom-kernels'
**/
std::cout << "Using existing kernel within the OpenCL backend of ViennaCL:" << std::endl;
viennacl::ocl::program & my_vcl_prog = viennacl::ocl::current_context().add_program(my_prog, "my_compute_program");
viennacl::ocl::kernel & my_vcl_kernel = my_vcl_prog.add_kernel(my_kernel, "elementwise_prod");
viennacl::ocl::enqueue(my_vcl_kernel(vcl_vec1, vcl_vec2, vcl_result, static_cast<cl_uint>(vcl_vec1.size()))); //Note that std::size_t might differ between host and device. Thus, a cast to cl_uint is necessary here.
std::cout << "vec1 : ";
std::cout << vcl_vec1 << std::endl;
std::cout << "vec2 : ";
std::cout << vcl_vec2 << std::endl;
std::cout << "result: ";
std::cout << vcl_result << std::endl;
/**
* Since a linear piece of memory can be interpreted in several ways,
* we will now create a 3x3 row-major matrix out of the linear memory in mem_vec1/
* The first three entries in vcl_vec2 and vcl_result are used to carry out matrix-vector products:
**/
viennacl::matrix<ScalarType> vcl_matrix(mem_vec1, 3, 3);
vcl_vec2.resize(3); //note that the resize operation leads to new memory, thus vcl_vec2 is now at a different memory location (values are copied)
vcl_result.resize(3); //note that the resize operation leads to new memory, thus vcl_vec2 is now at a different memory location (values are copied)
vcl_result = viennacl::linalg::prod(vcl_matrix, vcl_vec2);
std::cout << "result of matrix-vector product: ";
std::cout << vcl_result << std::endl;
/**
* Any further operations can be carried out in the same way.
* Just keep in mind that any resizing of vectors or matrices leads to a reallocation of the underlying memory buffer, through which the 'wrapper' is lost.
**/
std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
return EXIT_SUCCESS;
}