HPCwire: Parallel Programming Is Here

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June 23, 2009

Parallel Programming Is Here – Are You Ready?

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Whether you're simulating the extreme conditions inside an exploding star or designing an ergonomically innovative office chair, it's a good bet that a high performance computing (HPC) system and some brain-bending programming will be involved.

The HPC system may be a supercomputer like the 1.6 petaflop Jaguar behemoth at Oak Ridge National Laboratory, or a cluster powered by off-the-shelf multicore components. Whatever the scale of the hardware and the scope of the application, developers will have to learn how to deal with the complexities of parallel programming to get the most out of their computational resources.

The need for parallel programming is being driven by advances in multicore architectures. This rapid and accelerating technology trend is creating an array of HPC systems that range from dual and quad core systems to supercomputers and clusters with tens, hundreds and thousands of cores. These platforms perform at teraflop and petaflop speeds on terabytes of data. Capable of tackling some of today's most complex and pressing problems in engineering and science, these HPC systems are composed of a computational ecosystem that includes: scalable multicore architectures; fast, flexible, mammoth memories that can support many simultaneous threads; and high bandwidth I/O and communications.

Developers who have honed their parallel programming skills are ready to create applications that reach new levels of scalability, performance, safety and reliability. In particular, parallelism can be exploited in mechanical computer-aided engineering (MCAE) applications code for structural analysis and fluid dynamics, in computational chemistry and computational physics simulations and modeling, and industrial applications that run the gamut from oil and gas exploration to the design of high end golf equipment. For example, in the world of MCAE, Dale Layfield, engineer in Sun Microsystem's ISV Engineering organization, points to the benefits realized by applying parallelization to NASTRAN, a venerable finite element analysis (FEA) program that has been around for about 40 years.

"NASTRAN is a highly compute and I/O intensive structural analysis program," explains Layfield. "It lends itself well to being broken into smaller components and spreading those components across distributed computer clusters which substantially reduces throughput time. Distributed memory parallelism (DMP) helps eliminate the I/O bottleneck by dividing the analysis across a network of separate nodes. Multithreaded SMP (symmetric multiprocessing) allows you to make best use of the processing power within each node. SMP combined with DMP gives you the most bang for your buck."

Like NASTRAN, many of the other complex applications designed to run on HPC systems rely on parallel programming methodologies to handle the increasing number of computationally intensive jobs involving massive amounts of data and memory.

As David Conover, Chief Technologist, Mechanical Products for ANSYS notes, "Among the major benefits of parallel programming are faster turnaround time and the ability to create higher fidelity simulations and modeling to solve engineering design challenges. Engineers applying finite element methods can create models with much higher spatial resolutions and more geometric detail. And they can build models that include entire assemblies, rather than just one small component. Then they can analyze the interactions between those components at a high level of detail. Because the users are able to perform more simultaneous tasks of increased complexity, the entire engineering process is far more productive. You just can't achieve this level of functionality with applications that rely on sequential processes."

By creating larger high fidelity models with greater geometric detail and subjecting them to detailed simulations of the physical forces that they will encounter in real life, engineers can reduce the need for expensive and time-consuming physical testing -- the "build and break" approach. In addition, parallelization allows engineers to run more simulations in order to make design decisions earlier in the project lifecycle.

To achieve the speedup in applications performance, parallel programming uses threads that allow multiple operations to occur simultaneously. In an article in the May 20, 2009 HPCwire titled,"Parallel Programming: Some Fundamentals Concepts," authors Shameen Akhter and Jason Roberts, both of Intel, commented, "The entire concept of parallel programming centers on the design, development and deployment of threads within an application and the coordination between threads and their respective operations."

In short, parallel programming allows you to write scalable, flexible code that harnesses more HPC CPU resources and maximizes memory and I/O. It also allows users of the code -- whether it's you, a member of your organization's engineering or scientific staff, or a customer -- to solve problems that could not be solved using sequential programs, and solve them more quickly.

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