ViennaCL - The Vienna Computing Library  1.6.0
Free open-source GPU-accelerated linear algebra and solver library.
 All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Macros Pages
BLAS Level 1 Functionality

This tutorial shows how the BLAS level 1 functionality available in ViennaCL can be used. Operator overloading in C++ is used extensively to provide an intuitive syntax.

We start off with including necessary headers:

// include necessary system headers
#include <iostream>
//include basic scalar and vector types of ViennaCL
//include the generic inner product functions of ViennaCL
//include the generic norm functions of ViennaCL
// Some helper functions for this tutorial:
#include "Random.hpp"

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;

Scalar Operations

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);

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;

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; } Full Example Code

Level 1 Functionality