# Quick Start for BLAS

Anyone who has used R or Python's numpy extensively has been pleased with the speed of operations on large vectors of numbers. I wanted to get the speedy vector operations in a very lean program, so I looked under the hood and surveyed some of the libraries that do calculations on large vectors and matrices.

It turns out that BLAS (Basic Linear Algebra Subroutines) is everywhere. It lies beneath more sophisticated operations in libraries like LAPACK, and it has many implementations, ranging from Fortran (don't laugh---they're fast and reliable) code bases that started in the Seventies to implementations like OpenBLAS that use the latest low-level instructions from Intel's newest processors.

By gaining a basic familiarity with BLAS, you can gain access to all those new SIMD instructions, like AVX, MMX, and SSE2. There are bindings in C, Go, and many other languages.

The routines do take some getting used to, though. They have lots of parameters. The names of the parameters are very short coded strings that you have to learn to decypher. Fortunately, there are only a few tricks you need to master, and they're all explained in a few papers.

http://www.netlib.org/blas/blas2-paper.ps

There are many helpful references online that don't go into all the details discussed in the papers.

http://web.stanford.edu/class/me200c/tutorial_77/18.1_blas.html

So how to get started? I'm going to talk about a specific example, using the OpenBLAS implementation on Ubuntu 14.04 x86_64 in C.

First I install the libopenblas packages from APT.

sudo apt-get update
sudo apt-get install libblas-doc libopenblas-{dev,base}


A simple Makefile will build the C program. Even though Ubuntu used the "alternatives" mechanism to make OpenBLAS appear as libblas.so, I like to use the more explicit "-lopenblas" linker option. Note that there has to be a real tab character before "gcc" in the Makefile contents.

dist: dist.c
gcc -Wall -O3 -o $@$< -lopenblas


The C program starts off like this:

#include <stdio.h>
#include <stdlib.h>
#include <cblas.h>

int main(void)
{
const int p = 10;
float *a = malloc(p * sizeof *a);
float *b = malloc(p * sizeof *b);
float *c = malloc(p * sizeof *c);
float *orig = malloc(p * sizeof *orig);
int i;

if (!(a && b && c && orig)) {
perror("malloc");
exit(1);
}

for (i = 0; i < p; ++i) {
b[i] = i;
a[i] = 2 * i;
c[i] = 0;
}
cblas_scopy(p, b, 1, orig, 1);


There are several items of note in this example:

1. The arrays used by BLAS are regular, space-efficient, C arrays. They could just as well have been allocated on the stack.
2. You use BLAS as "cblas", and the routines have a cblas_ prefix.
3. There is an additional s prefix before the part of the function name that describes what it does, copy. That s stands for "single-precision floating point." There are other prefixes for doubles or complex numbers.

The parameters in the original example are,

name description argument
N number of assignments to make p
X the source array y
incX the increment in X 1
Y the destination array orig
incY the increment in Y 1

You might also be wondering, "What are the ones for in the parameter list?" They're for specifying what the increment should be on their respective arrays in the parameter list. I doubt you'll use them much, but here's an illustrative example.

incdemo.c

#include <stdio.h>
#include <cblas.h>

int main(void)
{
float a[] = { 1, 2, 3, 4, 5, 6 };
float b[] = { 0, 0, 0, 0, 0, 0 };
const int n = sizeof a / sizeof a[0];
int i;

cblas_scopy(n/2, a, 2, b, 2);   /* copy every other element */
for (i = 0; i < n; ++i)
printf("%f ", b[i]);
putchar('\n');

return 0;
}


See how every other element remains zero in the run below:

bash$gcc -Wall -O3 incdemo.c -lopenblas bash$ ./a.out
1.000000 0.000000 3.000000 0.000000 5.000000 0.000000
bash$ That clears up what the parameters are for, but let's get back to the original example and focus on adding and multiplying vectors together. Continuing the original example, here below is the first arithmetical vector operation. The first time I heard the name of this routine, it sounded really funny cblas_saxpy(p, -1, x, 1, y, 1);  The name, "saxpy", means single-precision floating point alpha times x plus y. The alpha is a scalar that is multiplied with every element in x. The result is added element-wise to y. I want to subtract these two vectors, so I use -1 as alpha. What if we want to square the result? Hmm... There's no obvious element-wise vector-vector multiply function to call. And it seems kind of clunky to use a matrix when it would be mostly empty space. Happily, the creators of BLAS had a plan. They chose to create a small number of functions that are useful in several kinds of circumstances. There is a notion of "banded matrices", where only the diagonal (or the diagonals above and below it) are non-zero. These banded matrices are represented efficiently. For a single diagonal, you can just use an array of numbers with the values of the diagonal. So by using a banded matrix for one vector and a vector for the other, you can multiply two vectors element wise. The routine to use here is cblas_sgbmv, as shown below. It does the following operation. \begin{equation*} y \gets \alpha*A*x + \beta*y \end{equation*} Here that is again in monospace, in case you read this before nikola fixes mathjax or I figure out what user error causes that math to fail to render in this blog. y <- alpha*A*x + beta*y  Note that there are a large number of parameters to use for tweaking the way the function works. cblas_sgbmv(CblasRowMajor, CblasNoTrans, /* Don't transpose A */ p, /* M is the number of rows in A */ p, /* N is the number of columns in A */ 0, /* KL: bands below the diagonal */ 0, /* KU: bands above the diagonal */ 1, /* alpha: the scalar multiplier */ b, /* A: just the diagonal of A in this case */ 1, /* LDA: 1st dimension of A */ b, /* X: the vector to multiply */ 1, /* incX */ 1, /* beta: the scalar multiplier for */ c, /* Y: the results are stored here */ 1); /* incY */  I find it handy to have the cblas.h file open for reference while writing a call like this. I guess one gets used to this kind of thing. Maybe not. Usually one just uses numpy, julia, or something similarly convenient, so maybe there's not enough time to get used to it. Anyway, it could be worse, and that's a relatively small price to pay for portable programs that get the absolute best in performance. The rest of the file is easy:  for (i = 0; i < p; ++i) { printf("b(%f) - a(%f) = %f\n", orig[i], a[i], b[i]); printf("(b-a)^2 = %f\n", c[i]); } free(a); free(b); free(c); free(orig); return 0; }  And running it looks like this: bash$ ./dist
b(0.000000) - a(0.000000) = 0.000000
(b-a)^2 = 0.000000
b(1.000000) - a(2.000000) = -1.000000
(b-a)^2 = 1.000000
b(2.000000) - a(4.000000) = -2.000000
(b-a)^2 = 4.000000
b(3.000000) - a(6.000000) = -3.000000
(b-a)^2 = 9.000000
b(4.000000) - a(8.000000) = -4.000000
(b-a)^2 = 16.000000
b(5.000000) - a(10.000000) = -5.000000
(b-a)^2 = 25.000000
b(6.000000) - a(12.000000) = -6.000000
(b-a)^2 = 36.000000
b(7.000000) - a(14.000000) = -7.000000
(b-a)^2 = 49.000000
b(8.000000) - a(16.000000) = -8.000000
(b-a)^2 = 64.000000
b(9.000000) - a(18.000000) = -9.000000
(b-a)^2 = 81.000000
bash\$


That's great, and now we have seen how to add and multiply vectors, but to calculate the distance is even easier than using the explicit steps like the ones we've seen so far. Check out cblas_snrm2 for Euclidean distance and cblas_sasum for Manhattan distance.

Hopefully some of these examples has given you a head start in working with BLAS tools and code.