ViennaCL - The Vienna Computing Library: blas1.cpp

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

All Classes Namespaces Files Functions Variables Typedefs Enumerations Enumerator Friends Macros Pages
blas1.cpp

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 "viennacl/scalar.hpp"
#include "viennacl/vector.hpp"
//include the generic inner product functions of ViennaCL
#include "viennacl/linalg/inner_prod.hpp"
//include the generic norm functions of ViennaCL
#include "viennacl/linalg/norm_1.hpp"
#include "viennacl/linalg/norm_2.hpp"
#include "viennacl/linalg/norm_inf.hpp"
#include "viennacl/tools/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;
viennacl::tools::uniform_random_numbers<ScalarType> randomNumber;

## 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);
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;

## 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:

for (unsigned int i = 0; i < 10; ++i)
{
std_vec1[i] = randomNumber();
vcl_vec2(i) = randomNumber(); //also works for GPU vectors, but is MUCH slower (approx. factor 10.000) than the CPU analogue
plain_vec3[i] = randomNumber();
}

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

/* =========================================================================
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
============================================================================= */
// include necessary system headers
#include <iostream>
//include basic scalar and vector types of ViennaCL
#include "viennacl/scalar.hpp"
#include "viennacl/vector.hpp"
//include the generic inner product functions of ViennaCL
#include "viennacl/linalg/inner_prod.hpp"
//include the generic norm functions of ViennaCL
#include "viennacl/linalg/norm_1.hpp"
#include "viennacl/linalg/norm_2.hpp"
#include "viennacl/linalg/norm_inf.hpp"
#include "viennacl/tools/random.hpp"
int main()
{
//Change this type definition to double if your gpu supports that
typedef float ScalarType;
viennacl::tools::uniform_random_numbers<ScalarType> randomNumber;
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);
std::cout << "Copying a few scalars..." << std::endl;
vcl_s1 = s1;
s2 = vcl_s2;
vcl_s3 = s3;
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;
std::cout << "mixed operations" << std::endl;
vcl_s1 = s1 * vcl_s2 + s3 - vcl_s3;
std::cout << "CPU scalar s3: " << s3 << std::endl;
std::cout << "GPU scalar vcl_s3: " << vcl_s3 << std::endl;
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);
for (unsigned int i = 0; i < 10; ++i)
{
std_vec1[i] = randomNumber();
vcl_vec2(i) = randomNumber(); //also works for GPU vectors, but is MUCH slower (approx. factor 10.000) than the CPU analogue
plain_vec3[i] = randomNumber();
}
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
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
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)
s1 = viennacl::linalg::norm_1(vcl_vec1);
vcl_s2 = viennacl::linalg::norm_2(vcl_vec2);
s3 = viennacl::linalg::norm_inf(vcl_vec3);
viennacl::linalg::plane_rotation(vcl_vec1, vcl_vec2, 1.1f, 2.3f);
//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);
viennacl::swap(vcl_vec1, vcl_vec2); //swaps all entries in memory
viennacl::fast_swap(vcl_vec1, vcl_vec2); //swaps OpenCL memory handles only
vcl_vec1. clear();
vcl_vec2.clear();
std::cout << "!!!! TUTORIAL COMPLETED SUCCESSFULLY !!!!" << std::endl;
return EXIT_SUCCESS;
}