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blaze / Getting Started


This short tutorial serves the purpose to give a quick overview of the way mathematical expressions have to be formulated in Blaze. Starting with Vector Types, the following long tutorial covers the most important aspects of the Blaze math library.


A First Example

Blaze is written in such a way that using mathematical expressions is as close to mathematical textbooks as possible and therefore as intuitive as possible. In nearly all cases the seemingly easiest solution is the right solution and most users experience no problems when trying to use Blaze in the most natural way. The following example gives a first impression of the formulation of a vector addition in Blaze:

#include <iostream>
#include <blaze/Math.h>

using blaze::StaticVector;
using blaze::DynamicVector;

// Instantiation of a static 3D column vector. The vector is directly initialized as
//    ( 4 -2  5 )
StaticVector<int,3UL> a{ 4, -2, 5 };

// Instantiation of a dynamic 3D column vector. Via the subscript operator the values are set to
//    ( 2  5 -3 )
DynamicVector<int> b( 3UL );
b[0] = 2;
b[1] = 5;
b[2] = -3;

// Adding the vectors a and b
DynamicVector<int> c = a + b;

// Printing the result of the vector addition
std::cout << "c =\n" << c << "\n";

Note that the entire Blaze math library can be included via the blaze/Math.h header file. Alternatively, the entire Blaze library, including both the math and the entire utility module, can be included via the blaze/Blaze.h header file. Also note that all classes and functions of Blaze are contained in the blaze namespace.

Assuming that this program resides in a source file called FirstExample.cpp, it can be compiled by using for instance the GNU C++ compiler:

g++ -ansi -O3 -DNDEBUG -mavx -o FirstExample FirstExample.cpp

Note the definition of the NDEBUG preprocessor symbol. In order to achieve maximum performance, it is necessary to compile the program in release mode, which deactivates all debugging functionality inside Blaze. It is also strongly recommended to specify the available architecture specific instruction set (as for instance the AVX instruction set, which if available can be activated via the -mavx flag). This allows Blaze to optimize computations via vectorization.

When running the resulting executable FirstExample, the output of the last line of this small program is

c =
6
3
2

An Example Involving Matrices

Similarly easy and intuitive are expressions involving matrices:

#include <blaze/Math.h>

using namespace blaze;

// Instantiating a dynamic 3D column vector
DynamicVector<int> x{ 4, -1, 3 };
x[0] =  4;
x[1] = -1;
x[2] =  3;

// Instantiating a dynamic 2x3 row-major matrix, preinitialized with 0. Via the function
// call operator three values of the matrix are explicitly set to get the matrix
//   ( 1  0  4 )
//   ( 0 -2  0 )
DynamicMatrix<int> A( 2UL, 3UL, 0 );
A(0,0) =  1;
A(0,2) =  4;
A(1,1) = -2;

// Performing a dense matrix/dense vector multiplication
DynamicVector<int> y = A * x;

// Printing the resulting vector
std::cout << "y =\n" << y << "\n";

// Instantiating a static column-major matrix. The matrix is directly initialized as
//   (  3 -1 )
//   (  0  2 )
//   ( -1  0 )
StaticMatrix<int,3UL,2UL,columnMajor> B{ { 3, -1 }, { 0, 2 }, { -1, 0 } };

// Performing a dense matrix/dense matrix multiplication
DynamicMatrix<int> C = A * B;

// Printing the resulting matrix
std::cout << "C =\n" << C << "\n";

The output of this program is

y =
16
2

C =
( -1 -1 )
(  0  4 )

A Complex Example

The following example is much more sophisticated. It shows the implementation of the Conjugate Gradient (CG) algorithm (http://en.wikipedia.org/wiki/Conjugate_gradient) by means of the Blaze library:

images/cg.jpg

In this example it is not important to understand the CG algorithm itself, but to see the advantage of the API of the Blaze library. In the Blaze implementation we will use a sparse matrix/dense vector multiplication for a 2D Poisson equation using NxN unknowns. It becomes apparent that the core of the algorithm is very close to the mathematical formulation and therefore has huge advantages in terms of readability and maintainability, while the performance of the code is close to the expected theoretical peak performance:

const size_t NN( N*N );

blaze::CompressedMatrix<double,rowMajor> A( NN, NN );
blaze::DynamicVector<double,columnVector> x( NN, 1.0 ), b( NN, 0.0 ), r( NN ), p( NN ), Ap( NN );
double alpha, beta, delta;

// ... Initializing the sparse matrix A

// Performing the CG algorithm
r = b - A * x;
p = r;
delta = (r,r);

for( size_t iteration=0UL; iteration<iterations; ++iteration )
{
   Ap = A * p;
   alpha = delta / (p,Ap);
   x += alpha * p;
   r -= alpha * Ap;
   beta = (r,r);
   if( std::sqrt( beta ) < 1E-8 ) break;
   p = r + ( beta / delta ) * p;
   delta = beta;
}

Hopefully this short tutorial gives a good first impression of how mathematical expressions are formulated with Blaze. The following long tutorial, starting with Vector Types, will cover all aspects of the Blaze math library, i.e. it will introduce all vector and matrix types, all possible operations on vectors and matrices, and of course all possible mathematical expressions.


Previous: Configuration and Installation ---- Next: Vectors

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