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February 08, 2008
The Next Challenge in High Performance Computing
-- Applications and Application Enablement
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Widening gap between platform affordability, and HPC software and applications
The availability of commodity high performance processors, fast and reliable commodity interconnection networks, modular system building blocks, and open system software has made high performance computer systems more affordable, easier to build, and leveled the playing field in HPC. Once an exclusive domain of powerful players such as national labs and large corporations, in recent times HPC has been making steady inroads into the computing radars of medium and small companies across the globe.
The need for HPC has pervaded all walks of life, from manufacturing to medicine, science and technology, and from defense to education, entertainment and national security. The emergence of commodity HPC platforms such as clusters of blades, clusters of desk top/rack mount servers, clusters of PlayStations and PC's makes the expectations from, and the interest in, the promise of HPC more intense. Take the financial institutions as an example. Until recently, a few tens or hundreds of servers working overnight to compute futures and options pricing for a large firm was considered acceptable. But with the expanding possibilities, the institutions now expect to run these analytics in real time to provide more value to their customers, asking for orders of magnitude higher compute capacity. In order to provide quicker diagnostics and better patient care, doctors wish to get rid of the hours/days of wait between an MRI/fMRI/PET scan and an off-line image analysis and provide real-time image analysis using significant amount of compute power. In the entertainment world, gamers are not satisfied with the improved graphics and life-like experience made available by today's advanced game consoles. Game providers wish to build massive compute infrastructures to simulate virtual worlds with millions of avatars to take the user experience to the next level. The list keeps growing.
However, the development of HPC applications has not kept pace with the perceived needs. Despite the increasing demand for more compute power to solve real life problems, the ever-widening gap in applications and software availability to harness the power of these platforms has been a serious bottleneck in the widespread adoption of HPC.
Factors impacting successful HPC application development
Successful HPC applications development rests on several factors including availability of parallel programming tools and libraries, sustained access to HPC platform facilities, and availability of highly skilled parallel programmers with domain expertise in the relevant scientific areas.
The developers of desk top applications and traditional server applications such as database and web based applications have plenty of mature development tools at their disposal (e.g. Eclipse). Large HPC developers (such as fortune 500 companies or National labs) use custom built in-house tools that are not available to the broader parallel application developers. Tools like TotalView scale to handle today's large clusters, however it needs to expand to support heterogeneous technologies (e.g. Cell, GPU and accelerator based solutions). Lack of an integrated, broad-based HPC development and debugging tool set is a significant barrier to entry for small and new HPC enterprises aiming to develop parallel applications.
Stringent export licensing requirements around scalable HPC systems in technology sourcing countries, have, to a large extent, made access to these platforms by scientific and technical professionals in new technology powerhouse economies such as India and China difficult. Commodity cluster technology is starting to break that barrier, but the licensing constraints remain a deterrent to wider HPC application development. For data-intensive and search-oriented HPC workloads, the cloud computing initiatives pioneered by Amazon, Google, Yahoo!, IBM and Microsoft hold the promise of convenient platform access across geographical boundaries using the utility computing model.
Since HPC and parallel programming had primarily been the playground of the privileged few until recently, organized efforts to train and develop broad-based parallel programming skills have been few and low-key at best, further isolating the demand for parallel applications from the domain experts in various fields.
The emergence of multiple hardware platforms, especially multicore and heterogenous processors from manufacturers such as Intel, IBM, AMD and NVIDIA brings additional challenges to parallel processing tools and library development. Despite pioneering work from IBM, Intel, RapidMind, several universities and the open source community, parallelizing and vectorizing compilers have a long way to go before becoming mainstream.
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