MAPS: Optimizing Massively Parallel Applications Using Device-Level Memory Abstraction

February 11th, 2015

Abstract:

GPUs play an increasingly important role in high-performance computing. While developing naive code is straightforward, optimizing massively parallel applications requires deep understanding of the underlying architecture. The developer must struggle with complex index calculations and manual memory transfers. This article classifies memory access patterns used in most parallel algorithms, based on Berkeley’s Parallel “Dwarfs.” It then proposes the MAPS framework, a device-level memory abstraction that facilitates memory access on GPUs, alleviating complex indexing using on-device containers and iterators. This article presents an implementation of MAPS and shows that its performance is comparable to carefully optimized implementations of real-world applications.

Rubin, Eri, et al. ["MAPS: Optimizing Massively Parallel Applications Using Device-Level Memory Abstraction."](http://dl.acm.org/citation.cfm?id=2680544) ACM Transactions on Architecture and Code Optimization (TACO) 11.4 (2014): 44.

[Library website](http://www.cs.huji.ac.il/~talbn/maps/)

C Framework for OpenCL v2.0.0 Now Available

February 11th, 2015

After four pre-releases, the stable 2.0.0 version of cf4ocl, the C Framework for OpenCL, is now available.

Since the last beta release, a number of tests were added, and a few bug fixes have been fixed. Support for device fission and native kernels has also been implemented. A complete list of features and fixes is available at https://github.com/FakenMC/cf4ocl/releases.

Cf4ocl has been tested on Linux, OS X and Windows, and offers a pure C object-oriented framework for developing and benchmarking OpenCL projects in C. It aims to:

1. Promote the rapid development of OpenCL host programs in C (with support for C++) and avoid the tedious and error-prone boilerplate code usually required. Read the rest of this entry »

Boost.Compute v0.4 Released

December 27th, 2014

Boost.Compute is an open-source, header-only C++ library for GPGPU and parallel-computing based on OpenCL. It provides a low-level C++ wrapper over OpenCL and high-level STL-like API with containers and algorithms for the GPU. Boost.Compute is available on GitHub and its documentation can be found here. See the full announcement here: http://kylelutz.blogspot.com/2014/12/boost-compute-0.4-released.html

PARALUTION v0.8.0 released

November 14th, 2014

PARALUTION is a library for sparse iterative methods which can be performed on various parallel devices, including multi-core CPU, GPU (CUDA and OpenCL) and Intel Xeon Phi. The new 0.8.0 release provides the following extra features:

  • Complex support
  • TNS, Variable preconditioner
  • BiCGStab(l), QMRCGStab, FCG solvers
  • RS and PairWise AMG
  • SIRA eigenvalue solver
  • Replace/Extract column/row functions
  • Stencil computation

For details, visit http://www.paralution.com.

Cf4ocl Brings Object-Oriented API to OpenCL C API

October 22nd, 2014

The Cf4ocl project is a GPLv3/LGPLv3 initiative to provide an object-oriented interface to the OpenCL C API with integrated profiling, promoting the rapid development of OpenCL host programs and avoiding boilerplate code. Its main goal is to allow developers to focus on OpenCL device code. After two alpha releases, the first beta is out, and can be tested on Linux, Windows and OS X. The framework is independent of the OpenCL platform version and vendor, and includes utilities to simplify the analysis of the OpenCL environment and of kernel requirements. While the project is making progress, it doesn’t yet offer OpenGL/DirectX interoperability, support for sub-devices, and doesn’t support pipes and SVM.

Cf4ocl can be downloaded from http://fakenmc.github.io/cf4ocl/.

Release of OpenCLIPP 2.0: an OpenCL library for computer vision and image processing

October 16th, 2014

Version 2.0 of OpenCLIPP, an Open Source OpenCL library for computer vision and image processing primitives, bas been released. For more information about the library, for programming contributions and for download, please refer to the OpenCLIPP Website.

CUDPP 2.2 released

September 4th, 2014

CUDPP release 2.2 is a feature release that adds a new parallel primitive and improves some existing primitives. We have added cudppSuffixArray, a parallel skew algorithm (SA) implementation that computes the suffix array of a string. This suffix array primitive is now used in burrowsWheelerTransform, delivering better performance than CUDPP 2.1’s use of cudppStringSort. The new BWT is further used in cudppCompress, which is now faster than the original parallel compression and supports compression of text containing all possible unsigned char values. Some bugs in cudppMoveToFrontTransform and cudppStringSort have also been fixed. OS X users might also be interested in how we supported the use of OS X’s clang compiler in OS X Mavericks (10.9).

Boost.Compute v0.3 Released

July 21st, 2014

Boost.Compute is a header-only C++ library for GPGPU and parallel-computing based on OpenCL. It provides a low-level C++ wrapper over OpenCL and high-level STL-like API with containers and algorithms for the GPU. It is available on GitHub and instructions for getting started can be found in the documentation. See the full announcement here: http://kylelutz.blogspot.com/2014/07/boost-compute-v0.3-released.html

BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs

May 27th, 2014

Abstract:

Analysis of functional magnetic resonance imaging (fMRI) data is becoming ever more computationally demanding as temporal and spatial resolutions improve, and large, publicly available data sets proliferate. Moreover, methodological improvements in the neuroimaging pipeline, such as non-linear spatial normalization, non-parametric permutation tests and Bayesian Markov Chain Monte Carlo approaches, can dramatically increase the computational burden. Despite these challenges, there do not yet exist any fMRI software packages which leverage inexpensive and powerful GPUs to perform these analyses. Here, we therefore present BROCCOLI, a free software package written in OpenCL that can be used for parallel analysis of fMRI data on a large variety of hardware configurations. BROCCOLI has, for example, been tested with an Intel CPU, an Nvidia GPU, and an AMD GPU. These tests show that parallel processing of fMRI data can lead to significantly faster analysis pipelines. This speedup can be achieved on relatively standard hardware, but further speed improvements require only a modest investment in GPU hardware. BROCCOLI (running on a GPU) can perform non-linear spatial normalization to a 1 mm3 brain template in 4–6 s, and run a second level permutation test with 10,000 permutations in about a minute. These non-parametric tests are generally more robust than their parametric counterparts, and can also enable more sophisticated analyses by estimating complicated null distributions. Additionally, BROCCOLI includes support for Bayesian first-level fMRI analysis using a Gibbs sampler. The new software is freely available under GNU GPL3 and can be downloaded from github: https://github.com/wanderine/BROCCOLI.

(A. Eklund, P. Dufort, M. Villani and S. LaConte: “BROCCOLI: Software for fast fMRI analysis on many-core CPUs and GPUs”. Front. Neuroinform. 8:24, 2014. [DOI])

PARALUTION 0.7.0 released

May 27th, 2014

PARALUTION is a library for sparse iterative methods which can be performed on various parallel devices, including multi-core CPU, GPU (CUDA and OpenCL) and Intel Xeon Phi. The new 0.7.0 version provides the following new features:

  • Windows support – full windows support for all backends (CUDA, OpenCL, OpenMP)
  • Assembling function – new OpenMP parallel assembling function for sparse matrices (includes an update function for time-dependent problems)
  • Direct (dense) solvers (for very small problems)
  • (Restricted) Additive Schwarz preconditioners
  • MATLAB/Octave plug-in

To avoid OpenMP overhead for small sized problems, the library will compute in serial if the size of the matrix/vector is below a pre-defined threshold. Internally, the OpenCL backend has been modified for simplified cross platform compilation.

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