Malcolm Yoke Hean Low

Compilers and More: OpenCL Promises and Potential

Sat, 12 Sep 2009 12:49:36 +0800

In this article, Michael Wolfe from PGI discusses different aspects of OpenCL. Given all the hype, what can we expect from OpenCL? Is it really simple? Is it portable? Will it replace other parallel programming models?

Ebook: HPC for Dummies

Thu, 10 Sep 2009 00:37:59 +0800

This special edition eBook from Sun and AMD shares details on real-world uses of HPC, explains the different types of HPC, guides you on how to choose between different suppliers, and provides benchmarks and guidelines you can use to get your system up and running. Get it here.

An Introduction to Parallel Programming - Module 1: Performance Tuning

Mon, 06 Jul 2009 13:57:39 +0800

A seven part series from Sun on the introduction to parallel programming. Part 1 is on performance tuning.

parallel_invoke() - Running Multiple Functions in Parallel using Intel Thread Building Block

Mon, 06 Apr 2009 16:46:39 +0800

From this article, running multiple functions together with Intel Thread Building block is as simple as the following:

void Function1();
void Function2();
void Function3();

void RunFunctions() {
tbb::parallel_invoke(Function1, Function2, Function3);
}

Cloud Computing for Dummy

Wed, 04 Feb 2009 22:53:38 +0800

This video in YouTube gives an easy to understand explanation of what Cloud Computing is.

Four Paths to HPC using Java

Fri, 19 Dec 2008 01:19:00 +0800

This article from JDJ gives a high-level description of four approaches using fork/join framework, Pervasive DataRush, Terracotta, and Hadoop for writing parallel applications in Java.

Parallel Programming: Three Things You Must Teach

Fri, 19 Dec 2008 00:53:14 +0800

From Intel Software College, this series of three lectures provides an introduction to parallel programming.

Module 1. Recognizing Potential Parallelism

Module 2. Shared Memory and Threads

Part 1

Part 2

Module 3. Programming with OpenMP

Part 1

Part 2

Google Code University - Introduction to Parallel Programming and MapReduce

Fri, 19 Dec 2008 00:40:19 +0800

This tutorial from the Google Code University covers the basics of parallel programming and the MapReduce programming model. The pre-requisites are significant programming experience with a language such as C++ or Java, and data structures & algorithms.

Why Lazy Functional Programming Languages are Good for Multicore

Sat, 20 Sep 2008 11:25:25 +0800

In this article, Peyton-Jones describes his interest in lazy functional programming languages, and chats about their increasing relevance in a world with rapidly increasing multi-core CPUs and clusters. "I think Haskell is increasingly well placed for this multi-core stuff, as I think people are increasingly going to look to languages like Haskell and say 'oh, that's where we can get some good ideas at least', whether or not it's the actual language or concrete syntax that they adopt.'"

CUDA, Supercomputing for the Masses

Sat, 20 Sep 2008 11:22:52 +0800

This series of articles introduces the power of CUDA -- through working code -- and to the thought process to help programmers map applications onto multi-threaded hardware (such as GPUs) to get big performance increases. Of course, not all problems can be mapped efficiently onto multi-threaded hardware, so part of the thought process will be to distinguish what will and what won't work, plus provide a common-sense idea of what might work "well-enough".

9 Reusable Parallel Data Structures and Algorithms

Wed, 17 Sep 2008 09:36:39 +0800

This article looks at nine reusable data structures and algorithms that are common to many parallel programs. Each example is accompanied by fully working, though not completely hardened, tested, and tuned, code. The list is by no means exhaustive, but it represents some of the more common patterns. Many of the examples build on each other.

Parallel Programming Made Easy

Sat, 06 Sep 2008 12:34:46 +0800

Michael Wolfe from Protland Group looks at all the current research projects aimed at making parallel programming easy. He has this to say "Every time I see someone claiming they've come up with a method to make parallel programming easy, I can't take them seriously. First, making parallel programming easy must be harder than making programming easy, and I don't think we've reached that first milestone yet."

Online course on multi-core performance from NCSA

Thu, 04 Sep 2008 09:30:02 +0800

The National Center for Supercomputing Applications (NCSA) is offering a new Web-based course, "Introduction to Multi-core Performance." This tutorial helps current and prospective users of multi-core systems understand and use the technology to accelerate their research. Multi-core processors, which hold the promise of enhanced performance and more efficient parallel processing, are a key stepping stone on the path to petascale computation. Applications that run on multi-core systems must be optimized to take full advantage of the improved performance offered by multi-core technology. To browse the course catalog, go to ci-tutor.ncsa.uiuc.edu/browse.php . To create a login and take a course, go to ci-tutor.ncsa.uiuc.edu/ .

Five Multicore Chip Startups to Watch

Sun, 31 Aug 2008 18:23:37 +0800

As semiconductor firms get around the limitations of making individual processors faster by putting more cores onto a single chip, the mindset of today's software developers and engineers mindset needs to adapt. For to really take advantage of multiple cores, a programmer needs to look at ways to make her code parallel, splitting jobs into different parts rather than the step-by step instructions delivered to single-core machines. There are also energy and communications issues that can constrain how far multicore can grow. This article give a list of startups that have the potential to stretch multicore processors to their very limit.

GPUs Help Spread Parallel Computing

Tue, 26 Aug 2008 13:09:12 +0800

Graphics processing units (GPU) are evolving to provide a diverse range of image processing functions flexibly and at high speed; as a result they are morphing into architectures appropriate for general-purpose calculating engines. The term "GPU computing" has been applied to the idea of utilizing their capabilities for high-speed processing of applications such as medical imagery processing.

New educational section launches on CUDA Zone

Sat, 16 Aug 2008 19:12:29 +0800

NVIDIA Corp. has launched "CUDA U," a new section on CUDA Zone (www.nvidia.com/cuda) that provides students, instructors, and developers educational resources for its CUDA programming environment. Now there is one place to go for CUDA instructional material, syllabuses and curricula, and information on schools and programs that offer CUDA instruction. CUDA U can be found at www.nvidia.com/object/cuda_education.

Intel Lifts the Curtain on Larrabee

Thu, 07 Aug 2008 17:00:12 +0800

Intel representatives revealed some of the architectural details of the company's much talked-about Larrabee processor. The new design is the chipmaker's first manycore x86 platform and represents what could be described as a general-purpose, x86 vector processor, combining features from both GPUs and CPUs. The architecture is the culmination of more than three years of R&D accomplished under Intel's terascale research program.

Competition in the Cloud: Yahoo, HP and Intel Join the Search for the Future of Computing Services

Tue, 05 Aug 2008 10:29:16 +0800

This week Yahoo, HP and Intel announced their contribution to what is becoming an active competition to develop the infrastructure for next generation computational services. The announcement of the Cloud Computing Test Bed is broader -- in scope and scale -- than the previous IBM-Google announcement establishing the Cluster Exploratory (CluE), and all this competition is definitely a good thing for the academics who will have several years of work on these resources.

Free e-book on Multicore from Cilk-Arts

Tue, 29 Jul 2008 16:28:36 +0800

Cilk-Arts has published a free ebook "How to Survive the Multicore Revolution (or at Least Survive the Hype)" to provide some background and context around the emergence of mainstream multicore processors; identify the key challenges facing software developers; provide an introduction to multithreading; and review several programming tools and techniques available today.

Intel Boosts Open Source Thread Library

Tue, 29 Jul 2008 13:13:28 +0800

On Tuesday at the O'Reilly Open Source Convention (OSCON), Intel announced that the latest version of its Threading Building Blocks (TBB) library, version 2.1, will be available for download this week. TBB is Intel's contribution to the growing collection of tools designed to help the millions of programmers worldwide exploit parallelism in their applications.