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July 21, 2006
Seven Challenges of High Performance Computing
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During our coverage of the High Performance Computing and Communication conference in March, HPCwire conducted an interview with Douglass Post, chief scientist of the DoD High Performance Computing Modernization Program, where he talked about the major challenges currently facing high performance computing. As the HPC community awaits DARPA's selection of the winners of the High Productivity Computer Systems (HPCS) Phase 2 competition, it may useful to review these challenges in order to understand some of the context of the impending decision. Below is an excerpt of this interview.
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Last year, Michael van de Vanter, Mary Zosel and I gave a paper at the International Conference on Software Engineering entitled "HPC needs a tool strategy." It's available for downloading at www.hpcmo.hpc.mil/Htdocs/HPCMO_Staff/doug_post/papers/HPCNeedsAToolStrategy.pdf. In that paper, we point out that development tools are lagging far behind what's needed. The gains in computer performance are being achieved by increasing the complexity of computer architectures. This increases the challenges associated with programming codes for these machines.
In fact, we find that most code developers consider development tools for massively parallel codes as a major risk item. There are good tools, but not enough, and the tools community has too much turnover. One major issue is that there isn't a good business model for parallel tools because the market is so small and unstable. If a tool doesn't attract enough customers, the company fails and the tool vanishes. If a tool attracts a lot of customers, the company prospers (moderately) and gets bought out. Then the tool gets focused on the priorities of the purchaser, and support for the rest of the community fades out.
Examples include the purchase of Pallas and of Kuck and Associates by Intel. Pallas developed and supported VAMPIR, a good tool for MPI, for most major massively parallel platforms. After Intel bought it, support for VAMPIR for non-Intel processors has waned, and by and large disappeared. The same thing happened with the very good Kuck and Associates C and C++ compilers for massively parallel computers. Only companies that make enough to stay in business, but not enough to be really prosperous, like Etnus, who makes the parallel debugger TotalView, and CEI, who make the visualization tool EnSight, are surviving. An exception maybe is the Portland Group, which seems to have carved out a niche in the Linux cluster environment.
Universities develop a lot of tools, but graduate students and post-docs have priorities other than software support for their university careers (like graduating and finding a real job). Vendors often develop tools for their machines, but those tools usually don't work on other platforms. Most major massively parallel codes have to run on many different platforms. The developers then need to learn to use many different sets of tools.
What is needed is a stable set of tools that work on all the relevant platforms, and give the code developers the tools they need to debug their code and optimize its performance, and give the users the tools they need to set up the problem, run the code and analyze the answers. Many different solutions have been discussed, but I think that the only solution that has a realistic chance of working is for the federal computing community to fund the development of set of tools. If the tools are developed and supported by industry, the federal government would have to subsidize the company to provide this service. It would also probably have to "own" the source code to ensure that the tool would survive the company being bought by another company, and there are other complications as well.
Another concept is a "tools consortium" with participation from the vendors. There was a tools consortium several years ago, but it died due to lack of resources. At some point, no one will buy computers they cannot use because the development tools are inadequate. Thus, the vendors have a vested interest in tools. We tried to get some interest in a joint development effort by the DARPA HPCS Phase II vendors, but without much success. The bigger vendors see tools as a source of competitive advantage. As I mentioned, the Portland Group seems to be a good provider of tools for the Linux cluster vendors who don't do their own development. This could grow if the major vendors (IBM, Cray, HP, etc.) started using the Portland tools, but I haven't seen that happening yet.
Computational tools offer society a new problem solving paradigm. They have the potential to provide, for the first time, accurate predictions of complex phenomena for realistic conditions. Before computational tools became available, predictions were generally possible only for simple model problems. Computational tools can include the effects of realistic geometries, all of the materials in the problem and all of the known physical/chemical/biological effects, and address a complete system rather than just a small of the system. Scientific and engineering computational tools offer the potential to rapidly produce optimized designs for systems, explore the limits of those designs, accelerate scientific discoveries, predict the behavior of natural systems like the weather, analyze and plan complex operations involving thousands to millions of individual entities, and analyze and organize enormous amounts of data. However, realizing this potential has many challenges.
I see at least seven major challenges in computational science and engineering, which I list below in rough order of the difficulty and importance:
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