/**
* In this tutorial we do not need additional auxiliary functions, allowing us to start right with main():
**/
int main()
{
//Change this type definition to double if your gpu supports that
typedef float ScalarType;
/**
* <h2> Scalar Operations </h2>
*
* Although usually not very efficient because of PCI-Express latency, ViennaCL enables you to directly manipulate individual scalar values.
* As such, a viennacl::scalar<double> behaves very similar to a normal `double`.
*
* Let us define a few CPU and ViennaCL scalars:
*
**/
ScalarType s1 = ScalarType(3.1415926); //note: writing ScalarType s1 = 3.1415926; leads to warnings with some compilers if ScalarType is 'float'.
ScalarType s2 = ScalarType(2.71763);
ScalarType s3 = ScalarType(42.0);
viennacl::scalar<ScalarType> vcl_s1;
viennacl::scalar<ScalarType> vcl_s2 = ScalarType(1.0);
viennacl::scalar<ScalarType> vcl_s3 = ScalarType(1.0);
/**
* CPU scalars can be transparently assigned to GPU scalars and vice versa:
**/
std::cout << "Copying a few scalars..." << std::endl;
vcl_s1 = s1;
s2 = vcl_s2;
vcl_s3 = s3;
/**
* Operations between GPU scalars work just as for CPU scalars:
* (Note that such single compute kernels on the GPU are considerably slower than on the CPU)
**/
std::cout << "Manipulating a few scalars..." << std::endl;
std::cout << "operator +=" << std::endl;
s1 += s2;
vcl_s1 += vcl_s2;
std::cout << "operator *=" << std::endl;
s1 *= s2;
vcl_s1 *= vcl_s2;
std::cout << "operator -=" << std::endl;
s1 -= s2;
vcl_s1 -= vcl_s2;
std::cout << "operator /=" << std::endl;
s1 /= s2;
vcl_s1 /= vcl_s2;
std::cout << "operator +" << std::endl;
s1 = s2 + s3;
vcl_s1 = vcl_s2 + vcl_s3;
std::cout << "multiple operators" << std::endl;
s1 = s2 + s3 * s2 - s3 / s1;
vcl_s1 = vcl_s2 + vcl_s3 * vcl_s2 - vcl_s3 / vcl_s1;
/**
* Operations can also be mixed:
**/
std::cout << "mixed operations" << std::endl;
vcl_s1 = s1 * vcl_s2 + s3 - vcl_s3;
/**
* The output stream is overloaded as well for direct printing to e.g. a terminal:
**/
std::cout << "CPU scalar s3: " << s3 << std::endl;
std::cout << "GPU scalar vcl_s3: " << vcl_s3 << std::endl;
/**
* <h2>Vector Operations
*
* Define a few vectors (from STL and plain C) and viennacl::vectors
**/
std::vector<ScalarType> std_vec1(10);
std::vector<ScalarType> std_vec2(10);
ScalarType plain_vec3[10]; //plain C array
viennacl::vector<ScalarType> vcl_vec1(10);
viennacl::vector<ScalarType> vcl_vec2(10);
viennacl::vector<ScalarType> vcl_vec3(10);
/**
* Let us fill the CPU vectors with random values:
* (random<> is a helper function from Random.hpp)
**/
for (unsigned int i = 0; i < 10; ++i)
{
std_vec1[i] = random<ScalarType>();
vcl_vec2(i) = random<ScalarType>(); //also works for GPU vectors, but is MUCH slower (approx. factor 10.000) than the CPU analogue
plain_vec3[i] = random<ScalarType>();
}
/**
* Copy the CPU vectors to the GPU vectors and vice versa
**/
viennacl::copy(std_vec1.begin(), std_vec1.end(), vcl_vec1.begin()); //either the STL way
viennacl::copy(vcl_vec2.begin(), vcl_vec2.end(), std_vec2.begin()); //either the STL way
viennacl::copy(vcl_vec2, std_vec2); //using the short hand notation for objects that provide .begin() and .end() members
viennacl::copy(vcl_vec2.begin(), vcl_vec2.end(), plain_vec3); //copy to plain C vector
/**
* Also partial copies by providing the corresponding iterators are possible:
**/
viennacl::copy(std_vec1.begin() + 4, std_vec1.begin() + 8, vcl_vec1.begin() + 4); //cpu to gpu
viennacl::copy(vcl_vec1.begin() + 4, vcl_vec1.begin() + 8, vcl_vec2.begin() + 1); //gpu to gpu
viennacl::copy(vcl_vec1.begin() + 4, vcl_vec1.begin() + 8, std_vec1.begin() + 1); //gpu to cpu
/**
* Compute the inner product of two GPU vectors and write the result to either CPU or GPU
**/
vcl_s1 = viennacl::linalg::inner_prod(vcl_vec1, vcl_vec2);
s1 = viennacl::linalg::inner_prod(vcl_vec1, vcl_vec2);
s2 = viennacl::linalg::inner_prod(std_vec1, std_vec2); //inner prod can also be used with std::vector (computations are carried out on CPU then)
/**
* Compute norms:
**/
s1 = viennacl::linalg::norm_1(vcl_vec1);
vcl_s2 = viennacl::linalg::norm_2(vcl_vec2);
s3 = viennacl::linalg::norm_inf(vcl_vec3);
/**
* Plane rotation of two vectors:
**/
viennacl::linalg::plane_rotation(vcl_vec1, vcl_vec2, 1.1f, 2.3f);
/**
* Use viennacl::vector via the overloaded operators just as you would write it on paper:
**/
//simple expression:
vcl_vec1 = vcl_s1 * vcl_vec2 / vcl_s3;
//more complicated expression:
vcl_vec1 = vcl_vec2 / vcl_s3 + vcl_s2 * (vcl_vec1 - vcl_s2 * vcl_vec2);
/**
* Swap the content of two vectors without a temporary vector:
**/
viennacl::swap(vcl_vec1, vcl_vec2); //swaps all entries in memory
viennacl::fast_swap(vcl_vec1, vcl_vec2); //swaps OpenCL memory handles only
/**
* The vectors can also be cleared directly:
**/
vcl_vec1.clear();
vcl_vec2.clear();
/**
* That's it, the tutorial is completed.
**/
std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
return EXIT_SUCCESS;
}
int run_vector_benchmark(test_config & config, viennacl::io::parameter_database& paras)
{
typedef viennacl::scalar<ScalarType> VCLScalar;
typedef viennacl::vector<ScalarType> VCLVector;
////////////////////////////////////////////////////////////////////
//set up a little bit of data to play with:
//ScalarType std_result = 0;
ScalarType std_factor1 = static_cast<ScalarType>(3.1415);
ScalarType std_factor2 = static_cast<ScalarType>(42.0);
viennacl::scalar<ScalarType> vcl_factor1(std_factor1);
viennacl::scalar<ScalarType> vcl_factor2(std_factor2);
std::vector<ScalarType> std_vec1(BENCHMARK_VECTOR_SIZE); //used to set all values to zero
VCLVector vcl_vec1(BENCHMARK_VECTOR_SIZE);
VCLVector vcl_vec2(BENCHMARK_VECTOR_SIZE);
VCLVector vcl_vec3(BENCHMARK_VECTOR_SIZE);
viennacl::copy(std_vec1, vcl_vec1); //initialize vectors with all zeros (no need to worry about overflows then)
viennacl::copy(std_vec1, vcl_vec2); //initialize vectors with all zeros (no need to worry about overflows then)
typedef test_data<VCLScalar, VCLVector> TestDataType;
test_data<VCLScalar, VCLVector> data(vcl_factor1, vcl_vec1, vcl_vec2, vcl_vec3);
//////////////////////////////////////////////////////////
///////////// Start parameter recording /////////////////
//////////////////////////////////////////////////////////
typedef std::map< double, std::pair<unsigned int, unsigned int> > TimingType;
std::map< std::string, TimingType > all_timings;
// vector addition
std::cout << "------- Related to vector addition ----------" << std::endl;
config.kernel_name("add"); optimize_full(paras, all_timings[config.kernel_name()], vector_add<TestDataType>, config, data);
config.kernel_name("inplace_add"); optimize_full(paras, all_timings[config.kernel_name()], vector_inplace_add<TestDataType>, config, data);
config.kernel_name("mul_add"); optimize_full(paras, all_timings[config.kernel_name()], vector_mul_add<TestDataType>, config, data);
config.kernel_name("cpu_mul_add"); optimize_full(paras, all_timings[config.kernel_name()], vector_cpu_mul_add<TestDataType>, config, data);
config.kernel_name("inplace_mul_add"); optimize_full(paras, all_timings[config.kernel_name()], vector_inplace_mul_add<TestDataType>, config, data);
config.kernel_name("cpu_inplace_mul_add"); optimize_full(paras, all_timings[config.kernel_name()], vector_cpu_inplace_mul_add<TestDataType>, config, data);
config.kernel_name("inplace_div_add"); optimize_full(paras, all_timings[config.kernel_name()], vector_inplace_div_add<TestDataType>, config, data);
std::cout << "------- Related to vector subtraction ----------" << std::endl;
config.kernel_name("sub"); optimize_full(paras, all_timings[config.kernel_name()], vector_sub<TestDataType>, config, data);
config.kernel_name("inplace_sub"); optimize_full(paras, all_timings[config.kernel_name()], vector_inplace_sub<TestDataType>, config, data);
config.kernel_name("mul_sub"); optimize_full(paras, all_timings[config.kernel_name()], vector_mul_sub<TestDataType>, config, data);
config.kernel_name("inplace_mul_sub"); optimize_full(paras, all_timings[config.kernel_name()], vector_inplace_mul_sub<TestDataType>, config, data);
config.kernel_name("inplace_div_sub"); optimize_full(paras, all_timings[config.kernel_name()], vector_inplace_div_sub<TestDataType>, config, data);
std::cout << "------- Related to vector scaling (mult/div) ----------" << std::endl;
config.kernel_name("mult"); optimize_full(paras, all_timings[config.kernel_name()], vector_mult<TestDataType>, config, data);
config.kernel_name("inplace_mult"); optimize_full(paras, all_timings[config.kernel_name()], vector_inplace_mult<TestDataType>, config, data);
config.kernel_name("cpu_mult"); optimize_full(paras, all_timings[config.kernel_name()], vector_cpu_mult<TestDataType>, config, data);
config.kernel_name("cpu_inplace_mult"); optimize_full(paras, all_timings[config.kernel_name()], vector_cpu_inplace_mult<TestDataType>, config, data);
config.kernel_name("divide"); optimize_full(paras, all_timings[config.kernel_name()], vector_divide<TestDataType>, config, data);
config.kernel_name("inplace_divide"); optimize_full(paras, all_timings[config.kernel_name()], vector_inplace_divide<TestDataType>, config, data);
std::cout << "------- Others ----------" << std::endl;
config.kernel_name("inner_prod"); optimize_full(paras, all_timings[config.kernel_name()], vector_inner_prod<TestDataType>, config, data);
config.kernel_name("swap"); optimize_full(paras, all_timings[config.kernel_name()], vector_swap<TestDataType>, config, data);
config.kernel_name("clear"); optimize_full(paras, all_timings[config.kernel_name()], vector_clear<TestDataType>, config, data);
config.kernel_name("plane_rotation"); optimize_full(paras, all_timings[config.kernel_name()], vector_plane_rotation<TestDataType>, config, data);
//config.max_work_groups(32); //otherwise failures on 8500 GT
config.kernel_name("norm_1"); optimize_restricted(paras, all_timings[config.kernel_name()], vector_norm_1<TestDataType>, config, data);
config.kernel_name("norm_2"); optimize_restricted(paras, all_timings[config.kernel_name()], vector_norm_2<TestDataType>, config, data);
config.kernel_name("norm_inf"); optimize_restricted(paras, all_timings[config.kernel_name()], vector_norm_inf<TestDataType>, config, data);
//restricted optimizations:
config.kernel_name("index_norm_inf"); optimize_restricted(paras, all_timings[config.kernel_name()], vector_index_norm_inf<TestDataType>, config, data);
return 0;
}
int run_matrix_benchmark(test_config & config, viennacl::io::parameter_database& paras)
{
typedef viennacl::scalar<ScalarType> VCLScalar;
typedef viennacl::vector<ScalarType> VCLVector;
typedef viennacl::matrix<ScalarType> VCLMatrix;
////////////////////////////////////////////////////////////////////
//set up a little bit of data to play with:
//ScalarType std_result = 0;
ScalarType std_factor1 = static_cast<ScalarType>(3.1415);
ScalarType std_factor2 = static_cast<ScalarType>(42.0);
viennacl::scalar<ScalarType> vcl_factor1(std_factor1);
viennacl::scalar<ScalarType> vcl_factor2(std_factor2);
std::vector<ScalarType> std_vec1(BENCHMARK_MATRIX_SIZE); //used to set all values to zero
std::vector< std::vector<ScalarType> > stl_mat(BENCHMARK_MATRIX_SIZE); //store identity matrix here
VCLVector vcl_vec1(BENCHMARK_MATRIX_SIZE);
VCLVector vcl_vec2(BENCHMARK_MATRIX_SIZE);
VCLMatrix vcl_mat(BENCHMARK_MATRIX_SIZE, BENCHMARK_MATRIX_SIZE);
for (int i=0; i<BENCHMARK_MATRIX_SIZE; ++i)
{
stl_mat[i].resize(BENCHMARK_MATRIX_SIZE);
stl_mat[i][i] = 1.0;
}
copy(std_vec1, vcl_vec1); //initialize vectors with all zeros (no need to worry about overflows then)
copy(std_vec1, vcl_vec2); //initialize vectors with all zeros (no need to worry about overflows then)
copy(stl_mat, vcl_mat);
typedef test_data<VCLScalar, VCLVector, VCLMatrix> TestDataType;
test_data<VCLScalar, VCLVector, VCLMatrix> data(vcl_factor1, vcl_vec1, vcl_vec2, vcl_mat);
//////////////////////////////////////////////////////////
///////////// Start parameter recording /////////////////
//////////////////////////////////////////////////////////
typedef std::map< double, std::pair<unsigned int, unsigned int> > TimingType;
std::map< std::string, TimingType > all_timings;
std::cout << "------- Related to direct solution algorithms ----------" << std::endl;
config.kernel_name("trans_lower_triangular_substitute_inplace");
optimize_restricted(paras, all_timings[config.kernel_name()],
matrix_trans_lower_triangular_substitute_inplace<TestDataType>, config, data);
config.kernel_name("lower_triangular_substitute_inplace");
optimize_restricted(paras, all_timings[config.kernel_name()],
matrix_lower_triangular_substitute_inplace<TestDataType>, config, data);
config.kernel_name("unit_lower_triangular_substitute_inplace");
optimize_restricted(paras, all_timings[config.kernel_name()],
matrix_unit_lower_triangular_substitute_inplace<TestDataType>, config, data);
config.kernel_name("upper_triangular_substitute_inplace");
optimize_restricted(paras, all_timings[config.kernel_name()],
matrix_upper_triangular_substitute_inplace<TestDataType>, config, data);
config.kernel_name("trans_upper_triangular_substitute_inplace");
optimize_restricted(paras, all_timings[config.kernel_name()],
matrix_trans_upper_triangular_substitute_inplace<TestDataType>, config, data);
config.kernel_name("unit_upper_triangular_substitute_inplace");
optimize_restricted(paras, all_timings[config.kernel_name()],
matrix_unit_upper_triangular_substitute_inplace<TestDataType>, config, data);
config.kernel_name("lu_factorize");
optimize_restricted(paras, all_timings[config.kernel_name()],
matrix_lu_factorize<TestDataType>, config, data);
//other kernels:
std::cout << "------- Related to other operations ----------" << std::endl;
config.kernel_name("rank1_update");
optimize_restricted(paras, all_timings[config.kernel_name()],
matrix_rank1_update<TestDataType>, config, data);
config.kernel_name("scaled_rank1_update");
optimize_restricted(paras, all_timings[config.kernel_name()],
matrix_scaled_rank1_update<TestDataType>, config, data);
config.kernel_name("vec_mul");
optimize_restricted(paras, all_timings[config.kernel_name()],
matrix_vec_mul<TestDataType>, config, data);
config.kernel_name("trans_vec_mul");
optimize_restricted(paras, all_timings[config.kernel_name()],
matrix_trans_vec_mul<TestDataType>, config, data);
return 0;
}
int run_matrix_benchmark(test_config & config, viennacl::io::parameter_database& paras)
{
typedef viennacl::scalar<ScalarType> VCLScalar;
typedef viennacl::vector<ScalarType> VCLVector;
typedef viennacl::compressed_matrix<ScalarType> VCLMatrix;
////////////////////////////////////////////////////////////////////
//set up a little bit of data to play with:
//ScalarType std_result = 0;
ScalarType std_factor1 = static_cast<ScalarType>(3.1415);
ScalarType std_factor2 = static_cast<ScalarType>(42.0);
viennacl::scalar<ScalarType> vcl_factor1(std_factor1);
viennacl::scalar<ScalarType> vcl_factor2(std_factor2);
std::vector<ScalarType> std_vec1(BENCHMARK_MATRIX_SIZE); //used to set all values to zero
std::vector< std::map< unsigned int, ScalarType> > stl_mat(BENCHMARK_MATRIX_SIZE); //store identity matrix here
VCLVector vcl_vec1(BENCHMARK_MATRIX_SIZE);
VCLVector vcl_vec2(BENCHMARK_MATRIX_SIZE);
VCLMatrix vcl_mat(BENCHMARK_MATRIX_SIZE, BENCHMARK_MATRIX_SIZE);
for (int i=0; i<BENCHMARK_MATRIX_SIZE; ++i)
{
if (i > 10)
{
stl_mat[i][i - 10] = 1.0;
stl_mat[i][i - 7] = 1.0;
stl_mat[i][i - 4] = 1.0;
stl_mat[i][i - 2] = 1.0;
}
stl_mat[i][i] = 1.0;
if (i + 10 < BENCHMARK_MATRIX_SIZE)
{
stl_mat[i][i + 5] = 1.0;
stl_mat[i][i + 7] = 1.0;
stl_mat[i][i + 9] = 1.0;
stl_mat[i][i + 10] = 1.0;
}
}
viennacl::copy(std_vec1, vcl_vec1); //initialize vectors with all zeros (no need to worry about overflows then)
viennacl::copy(std_vec1, vcl_vec2); //initialize vectors with all zeros (no need to worry about overflows then)
viennacl::copy(stl_mat, vcl_mat);
typedef test_data<VCLScalar, VCLVector, VCLMatrix> TestDataType;
test_data<VCLScalar, VCLVector, VCLMatrix> data(vcl_factor1, vcl_vec1, vcl_vec2, vcl_mat);
//////////////////////////////////////////////////////////
///////////// Start parameter recording /////////////////
//////////////////////////////////////////////////////////
typedef std::map< double, std::pair<unsigned int, unsigned int> > TimingType;
std::map< std::string, TimingType > all_timings;
//other kernels:
std::cout << "------- Related to other operations ----------" << std::endl;
config.kernel_name("vec_mul");
optimize_full(paras, all_timings[config.kernel_name()],
matrix_vec_mul<TestDataType>, config, data);
return 0;
}
int main()
{
typedef float ScalarType;
viennacl::vector<ScalarType> vcl_vec1(10);
viennacl::vector<ScalarType> vcl_vec2(10);
viennacl::vector<ScalarType> vcl_vec3(10);
//
// Let us fill the CPU vectors with random values:
// (random<> is a helper function from Random.hpp)
//
for (unsigned int i = 0; i < 10; ++i)
{
vcl_vec1[i] = ScalarType(i);
vcl_vec2[i] = ScalarType(10 - i);
}
//
// Build expression graph for the operation vcl_vec3 = vcl_vec1 + vcl_vec2
//
// This requires the following expression graph:
//
// ( = )
// / |
// vcl_vec3 ( + )
// / |
// vcl_vec1 vcl_vec2
//
// One expression node consists of two leaves and the operation connecting the two.
// Here we thus need two nodes: One for {vcl_vec3, = , link}, where 'link' points to the second node
// {vcl_vec1, +, vcl_vec2}.
//
// The following is the lowest level on which one could build the expression tree.
// Even for a C API one would introduce some additional convenience layer such as add_vector_float_to_lhs(...); etc.
//
typedef viennacl::scheduler::statement::container_type NodeContainerType; // this is just std::vector<viennacl::scheduler::statement_node>
NodeContainerType expression_nodes(2); //container with two nodes
////// First node //////
// specify LHS of first node, i.e. vcl_vec3:
expression_nodes[0].lhs.type_family = viennacl::scheduler::VECTOR_TYPE_FAMILY; // family of vectors
expression_nodes[0].lhs.subtype = viennacl::scheduler::DENSE_VECTOR_TYPE; // a dense vector
expression_nodes[0].lhs.numeric_type = viennacl::scheduler::FLOAT_TYPE; // vector consisting of floats
expression_nodes[0].lhs.vector_float = &vcl_vec3; // provide pointer to vcl_vec3;
// specify assignment operation for this node:
expression_nodes[0].op.type_family = viennacl::scheduler::OPERATION_BINARY_TYPE_FAMILY; // this is a binary operation, so both LHS and RHS operands are important
expression_nodes[0].op.type = viennacl::scheduler::OPERATION_BINARY_ASSIGN_TYPE; // assignment operation: '='
// specify RHS: Just refer to the second node:
expression_nodes[0].rhs.type_family = viennacl::scheduler::COMPOSITE_OPERATION_FAMILY; // this links to another node (no need to set .subtype and .numeric_type)
expression_nodes[0].rhs.node_index = 1; // index of the other node
////// Second node //////
// LHS
expression_nodes[1].lhs.type_family = viennacl::scheduler::VECTOR_TYPE_FAMILY; // family of vectors
expression_nodes[1].lhs.subtype = viennacl::scheduler::DENSE_VECTOR_TYPE; // a dense vector
expression_nodes[1].lhs.numeric_type = viennacl::scheduler::FLOAT_TYPE; // vector consisting of floats
expression_nodes[1].lhs.vector_float = &vcl_vec1; // provide pointer to vcl_vec1
// OP
expression_nodes[1].op.type_family = viennacl::scheduler::OPERATION_BINARY_TYPE_FAMILY; // this is a binary operation, so both LHS and RHS operands are important
expression_nodes[1].op.type = viennacl::scheduler::OPERATION_BINARY_ADD_TYPE; // addition operation: '+'
// RHS
expression_nodes[1].rhs.type_family = viennacl::scheduler::VECTOR_TYPE_FAMILY; // family of vectors
expression_nodes[1].rhs.subtype = viennacl::scheduler::DENSE_VECTOR_TYPE; // a dense vector
expression_nodes[1].rhs.numeric_type = viennacl::scheduler::FLOAT_TYPE; // vector consisting of floats
expression_nodes[1].rhs.vector_float = &vcl_vec2; // provide pointer to vcl_vec2
// create the full statement (aka. single line of code such as vcl_vec3 = vcl_vec1 + vcl_vec2):
viennacl::scheduler::statement vec_addition(expression_nodes);
// print it
std::cout << vec_addition << std::endl;
// run it
viennacl::scheduler::execute(vec_addition);
// print vectors
std::cout << "vcl_vec1: " << vcl_vec1 << std::endl;
std::cout << "vcl_vec2: " << vcl_vec2 << std::endl;
std::cout << "vcl_vec3: " << vcl_vec3 << std::endl;
std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
return EXIT_SUCCESS;
}
/**
* 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;
}
int main(int argc, char * argv[])
{
TAU_INIT(&argc, &argv)
TAU_PROFILE_TIMER(orio_maintimer, "main()", "int (int, char **)", TAU_USER);
TAU_PROFILE_START(orio_maintimer);
TAU_PROFILE_SET_NODE(0);
std::vector<viennacl::ocl::device> devices = viennacl::ocl::platform().devices();
std::vector<cl_device_id> my_devices;
my_devices.push_back(devices[0].id());
viennacl::ocl::setup_context(0L, my_devices);
//Change this type definition to double if your gpu supports that
typedef double ScalarType;
/////////////////////////////////////////////////
///////////// Vector operations /////////////////
/////////////////////////////////////////////////
void * orio_profiler;
TAU_PROFILER_CREATE(orio_profiler, "orio_generated_code", "", TAU_USER);
for(int i = 0; i < 3; ++i) {
//
// Define a few vectors (from STL and plain C) and viennacl::vectors
//
std::vector<ScalarType> std_vec1(1000000);
std::vector<ScalarType> std_vec2(1000000);
viennacl::vector<ScalarType> vcl_vec1(1000000);
viennacl::vector<ScalarType> vcl_vec2(1000000);
viennacl::scalar<ScalarType> vcl_s1 = ScalarType(5.0);
//
// Let us fill the CPU vectors with random values:
// (random<> is a helper function from Random.hpp)
//
for (unsigned int i = 0; i < 1000000; ++i)
{
std_vec1[i] = random<ScalarType>();
std_vec2[i] = 0.0;
}
//
// Copy the CPU vectors to the GPU vectors and vice versa
//
TAU_PROFILER_START(orio_profiler);
viennacl::copy(std_vec1.begin(), std_vec1.end(), vcl_vec1.begin()); //either the STL way
viennacl::copy(std_vec2.begin(), std_vec2.end(), vcl_vec2.begin()); //either the STL way
vcl_vec2 += vcl_s1 * vcl_vec1;
viennacl::copy(vcl_vec2.begin(), vcl_vec2.end(), std_vec2.begin());
TAU_PROFILER_STOP(orio_profiler);
}
double orio_inclusive[TAU_MAX_COUNTERS];
TAU_PROFILER_GET_INCLUSIVE_VALUES(orio_profiler, &orio_inclusive);
printf("{'/*@ coordinate @*/' : %g}\n", orio_inclusive[0]);
return EXIT_SUCCESS;
}
int main()
{
//Change this type definition to double if your gpu supports that
typedef float ScalarType;
// Choose the Phi (WORKS)
viennacl::ocl::set_context_device_type(0, viennacl::ocl::accelerator_tag());
/////////////////////////////////////////////////
///////////// Scalar operations /////////////////
/////////////////////////////////////////////////
//
// Define a few CPU scalars:
//
ScalarType s1 = static_cast<ScalarType>(3.1415926);
ScalarType s2 = static_cast<ScalarType>(2.71763);
ScalarType s3 = static_cast<ScalarType>(42.0);
//
// ViennaCL scalars are defined in the same way:
//
viennacl::scalar<ScalarType> vcl_s1;
viennacl::scalar<ScalarType> vcl_s2 = static_cast<ScalarType>(1.0);
viennacl::scalar<ScalarType> vcl_s3 = static_cast<ScalarType>(1.0);
//
// CPU scalars can be transparently assigned to GPU scalars and vice versa:
//
vcl_s1 = s1;
s2 = vcl_s2;
vcl_s3 = s3;
//
// Operations between GPU scalars work just as for CPU scalars:
// (Note that such single compute kernels on the GPU are considerably slower than on the CPU)
//
s1 += s2;
vcl_s1 += vcl_s2;
s1 *= s2;
vcl_s1 *= vcl_s2;
s1 -= s2;
vcl_s1 -= vcl_s2;
s1 /= s2;
vcl_s1 /= vcl_s2;
s1 = s2 + s3;
vcl_s1 = vcl_s2 + vcl_s3;
s1 = s2 + s3 * s2 - s3 / s1;
vcl_s1 = vcl_s2 + vcl_s3 * vcl_s2 - vcl_s3 / vcl_s1;
//
// Operations can also be mixed:
//
vcl_s1 = s1 * vcl_s2 + s3 - vcl_s3;
//
// Output stream is overloaded as well:
//
std::cout << "CPU scalar s2: " << s2 << std::endl;
std::cout << "GPU scalar vcl_s2: " << vcl_s2 << std::endl;
std::vector< viennacl::ocl::device > devices = viennacl::ocl::platform().devices();
for (int i = 0; i < devices.size(); i++) {
std::cout << devices[i].info() << "\n";
}
std::cout << "SELECTED DEVICE: \n";
std::cout << viennacl::ocl::current_context().current_device().info() << "\n";
/////////////////////////////////////////////////
///////////// Vector operations /////////////////
/////////////////////////////////////////////////
//
// Define a few vectors (from STL and plain C) and viennacl::vectors
//
std::vector<ScalarType> std_vec1(10);
std::vector<ScalarType> std_vec2(10);
ScalarType plain_vec3[10]; //plain C array
viennacl::vector<ScalarType> vcl_vec1(10);
viennacl::vector<ScalarType> vcl_vec2(10);
viennacl::vector<ScalarType> vcl_vec3(10);
//
// Let us fill the CPU vectors with random values:
// (random<> is a helper function from Random.hpp)
//
for (unsigned int i = 0; i < 10; ++i)
{
std_vec1[i] = random<ScalarType>();
vcl_vec2(i) = random<ScalarType>(); //also works for GPU vectors, but is MUCH slower (approx. factor 10.000) than the CPU analogue
plain_vec3[i] = random<ScalarType>();
}
//
// Copy the CPU vectors to the GPU vectors and vice versa
//
copy(std_vec1.begin(), std_vec1.end(), vcl_vec1.begin()); //either the STL way
copy(vcl_vec2.begin(), vcl_vec2.end(), std_vec2.begin()); //either the STL way
copy(vcl_vec2, std_vec2); //using the short hand notation for objects that provide .begin() and .end() members
copy(vcl_vec2.begin(), vcl_vec2.end(), plain_vec3); //copy to plain C vector
//
// Compute the inner product of two GPU vectors and write the result to either CPU or GPU
//
vcl_s1 = viennacl::linalg::inner_prod(vcl_vec1, vcl_vec2);
s1 = viennacl::linalg::inner_prod(vcl_vec1, vcl_vec2);
//
// Compute norms:
//
s1 = viennacl::linalg::norm_1(vcl_vec1);
vcl_s2 = viennacl::linalg::norm_2(vcl_vec2);
s3 = viennacl::linalg::norm_inf(vcl_vec3);
//
// Plane rotation of two vectors:
//
viennacl::linalg::plane_rotation(vcl_vec1, vcl_vec2, 1.1f, 2.3f);
//
// Use viennacl::vector via the overloaded operators just as you would write it on paper:
//
//simple expression:
vcl_vec1 = vcl_s1 * vcl_vec2 / vcl_s3;
//more complicated expression:
vcl_vec1 = vcl_vec2 / vcl_s1 + vcl_s2 * (vcl_vec1 - vcl_s2 * vcl_vec2);
//
// Swap the content of two vectors without a temporary vector:
//
swap(vcl_vec1, vcl_vec2);
//
// That's it. Move on to the second tutorial, where dense matrices are explained.
//
std::cout << "!!!! TUTORIAL 1 COMPLETED SUCCESSFULLY !!!!" << std::endl;
exit(EXIT_SUCCESS);
return 0;
}