Do More, Code Less with ArrayFire GPU Matrix Library

arrayfire_logo2This is a guest post by Chris McClanahan from Accelereyes.

ArrayFire is a fast and easy-to-use GPU matrix library developed by Accelereyes. ArrayFire wraps GPU memory into a simple “array” object, enabling developers to process vectors, matrices, and volumes on the GPU using high-level routines, without having to get involved with device kernel code.

ArrayFire Feature Highlights

  • ArrayFire provides a high-level array notation and an extensive set of functions for easily manipulating N-dimensional GPU data.
  • ArrayFire provides all basic arithmetic operations (element-wise arithmetic, trigonometric, logical operations, etc.), higher-level primitives (reductions, matrix multiply, set operations, sorting, etc.), and even domain-specific functions (image and signal processing, linear algebra, etc.).
  • ArrayFire can be used as a self-contained library, or integrated into and supplement existing CUDA code. The array object can wrap data from CUDA device pointers and existing CPU memory.
  • ArrayFire contains built-in graphics functions for data visualization. The graphics library in ArrayFire provides easy rendering of 2D and 3D data, and leverages CUDA OpenGL interoperation, so visualization is fast and efficient. Various visualization algorithms make easy to explore complex data.
  • ArrayFire offers a unique “gfor” construct that can drastically speed up conventional “for” loops over data. The gfor loop essentially auto-vectorizes the code inside, and executes all iterations of the loop simultaneously.
  • ArrayFire supports C, C++, and Fortran on top of the CUDA platform.
  • ArrayFire is built on top of a custom just-in-time (JIT) compiler for efficient GPU memory usage. The JIT back-end in ArrayFire automatically combines many operations behind the scenes, and executes them in batches to minimize GPU kernel launches.
  • Accelereyes strives to include only the best performing code in ArrayFire. This means that ArrayFire uses existing implementations of functions when they are faster—such as Thrust for sorting, CULA for linear algebra, and CUFFT for fft. Continue reading

Six Ways to SAXPY

This post is a GPU program chrestomathy. What’s a Chrestomathy, you ask?

In computer programming, a program chrestomathy is a collection of similar programs written in various programming languages, for the purpose of demonstrating differences in syntax, semantics and idioms for each language. [Wikipedia]

There are several good examples of program chrestomathies on the web, including Rosetta Code andNBabel, which demonstrates gravitational N-body simulation in multiple programming languages. In this post I demonstrate six ways to implement a simple SAXPY computation on the CUDA platform. Why is this interesting? Because it demonstrates the breadth of options you have today for programming NVIDIA GPUs, and it covers the three main approaches to GPU computing: GPU-accelerated libraries, GPU compiler directives, and GPU programming languages.

SAXPY stands for “Single-Precision A·X Plus Y”.  It is a function in the standard Basic Linear Algebra Subroutines (BLAS)library. SAXPY is a combination of scalar multiplication and vector addition, and it’s very simple: it takes as input two vectors of 32-bit floats X and Y with N elements each, and a scalar value A. It multiplies each element X[i] by A and adds the result to Y[i]. A simple C implementation looks like this.

void saxpy(int n, float a, float *x, float *y)
{
  for (int i = 0; i < n; ++i)
      y[i] = a*x[i] + y[i];
}

// Perform SAXPY on 1M elements
saxpy(1<<20, 2.0, x, y);

Continue reading

An OpenACC Example (Part 2)

In my previous post I added 3 lines of OpenACC directives to a Jacobi iteration code, achieving more than 2x speedup by running it on a GPU. In this post I’ll continue where I left off and demonstrate how we can use OpenACC directives clauses to take more explicit control over how the compiler parallelizes our code. This will provide us with significantly higher speedup.

Example Source Code

You can browse and download all source code from the examples in this post from the Parallel Forall GitHub repository. The directory for this post has subdirectories for each “step” in this post (and the next one) with their own source and Makefiles so you can see and try out the changes in each step (step 1step 2step 3). The examples use OpenACC 1.0 syntax, so you’ll need a compiler that supports it.  I’m using the PGI compiler version 12.2 with preliminary support for OpenACC.

Let’s pick up where we left off last week. Continue reading

An OpenACC Example (Part 1)

In this post I’ll continue where I left off in my introductory post about OpenACC and provide a somewhat more realistic example. This simple C/Fortran code example demonstrates a 2x speedup with the addition of just a few lines of OpenACC directives, and in the next post I’ll add just a few more lines to push that speedup over 4x.

Example Source Code

You can download all source code from the examples in this post from the Parallel Forall GitHub repository. The directory for this post has subdirectories for each “step” in this post (and the next one) with their own source and Makefiles so you can see and try out the changes in each step (step 1step 2). The examples use OpenACC 1.0 syntax. You’ll need a compiler with support for OpenACC.  I’m using the PGI compiler version 12.2 with preliminary support for OpenACC.

Jacobi Iteration

Let’s look at a somewhat more interesting example. This is still a simple program, but it gives us a bit more room to explore various OpenACC directives and options. Jacobi iteration is a standard iterative method for finding solutions to a system of linear equations.  In this post I’ll take an existing, simple sequential CPU implementation of two-dimensional Jacobi iteration and optimize it using OpenMP and OpenACC. The core computational kernel of this example is shown below. The outer while loop iterates until the solution has converged, by comparing the computed error to a specified error tolerance, tol. For benchmarking purposes, we have set the variable tol low enough that the outer loop always runs for iter_max iterations (1000). The first set of inner nested loops on lines 5 through 11 apply a 2D Laplace operator at each element of a 2D grid, and the ones on lines 14 through 18 copy the output back to the input for the next iteration.  For our benchmark, we use a grid of 4096×4096 elements.

while ( error > tol && iter < iter_max ) {
    error = 0.f;

    #pragma omp parallel for shared(m, n, Anew, A)
    for( int j = 1; j < n-1; j++) {
        for( int i = 1; i < m-1; i++ ) {
            Anew[j][i] = 0.25f * ( A[j][i+1] + A[j][i-1]
                                 + A[j-1][i] + A[j+1][i]);
            error = fmaxf( error, fabsf(Anew[j][i]-A[j][i]));
        }
    }

    #pragma omp parallel for shared(m, n, Anew, A)
    for( int j = 0; j < n-1; j++) {
        for( int i = 0; i < m-1; i++ ) {
            A[j][i] = Anew[j][i];   
        }
    }

    if(iter % 100 == 0) printf("%d, %0.6fn", iter, error);

    iter++;
}

Continue reading

OpenACC: Directives for GPUs

NVIDIA has made a lot of progress with CUDA over the past five years; we estimate that there are over 150,000 CUDA developers, and important science is being accomplished with the help of CUDA. But we have a long way to go to help everyone benefit from GPU computing. There are many programmers who can’t afford the time to learn and apply a parallel programming language. Others, many of them scientists and engineers working with huge existing code bases, can only make changes to their code that are portable across hardware and OS, and that benefit performance on multiple platforms.

This class of developers needs a higher-level approach to GPU acceleration. They need something that is easy, powerful, portable, and open. This is the motivation behind OpenACC, an open standard defining a collection of compiler directives that specify loops and regions of code in standard C, C++, and Fortran to be offloaded from a host CPU to an attached parallel accelerator, such as an NVIDIA GPU. Directives are simple (typically a single line) hints provided to the compiler. If you are a C or C++ programmer, you are probably familiar with #pragma directives.

EASY: A MORSEL OF OPENACC

Here is a really simple example using OpenACC. This loop performs a SAXPY operation. SAXPY stands for Single-precision A times X Plus YA is a scalar value and X and Y are vectors, so this is a vector scale and add operation. Here is a simple SAXPY in C with an OpenACC directive to parallelize it.

void saxpy_parallel(int n,
  float a,
  float *x,
  float *restrict y)
{
#pragma acc kernels
  for (int i = 0; i < n; ++i)
    y[i] = a*x[i] + y[i];
}

Here’s the equivalent in Fortran. Continue reading