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Alternative Processors: Benefits, Usage, Language, and User Involvement - Insights and Suggestions

Explore the concept of alternative processors, their benefits, and how they can be used to increase analysis throughput. Discover the language environments best suited for these processors and the importance of involving users in their programming. Gain insights into the impact of increasing parallelism and the challenges of predicting the benefit of investing in alternative processors. Also, delve into a rant about the HPC community's culture of not paying for software and a suggestion for a new problem-solving ranking system.

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Alternative Processors: Benefits, Usage, Language, and User Involvement - Insights and Suggestions

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  1. Alternative Processors PanelIDC, Tucson, Sept 10, 2008 Andrew JonesVice-President HPC Business

  2. Outline • 4 questions (posed by Robert) • 4 observations • 1 rant • and a suggestion

  3. Panel Question 1 • What is an alternative processor? • requires the programmer to re-think code/structure • promises large benefits • for an acceptable amount of pain

  4. Panel Question 2 • How should/can they be used to increase analysis throughput? • Whatever you can make work :-)

  5. Panel Question 3 • What language environment, new and/or old, should be used to take advantage of these alternative processors? • Rule 1: Keep it Simple! • (so both C and Fortran, • with simple hooks or extensions) • Rule 2: enable legacy code

  6. Panel Question 4 • Should the user be involved with the specific programming of these processors? • At first, they will have to be. • We all hope over time less so.

  7. Observation 1Accelerators or general purpose CPU’s? • Even the general purpose processors have roadmaps that start to look more like accelerators in terms of programming aspects

  8. Observation 2Technology advance • “cannot escape pace and direction of change” • John Shalf, NERSC • Looking for the easy road will only lead to a detour and delay the inevitable

  9. Observation 3Increasing parallelism in a node • increasing compute power • per unit of memory capacity • increasing compute power • per unit of memory performance

  10. Observation 3: Application ImpactIncreasing parallelism in a node • decreasing memory capacity • per unit of compute • decreasing memory performance • per unit of compute

  11. Observation 4Where do accelerators help? • Predicting whether the pain will deliver enough benefit before investing the effort is hard • Don’t seem to help with hard problems • (e.g. global, capability, etc)

  12. Rant: Why? • Why does the HPC community cling to • a culture of not paying for software? • Applications, tools, data analysis, ... • Applications are the encapsulation of the science and have a long lifetime, so why do we treat them as the poor cousin of the hardware?

  13. You get what you measure • Top 500

  14. You get what you measure • dense linear algebra compute power

  15. Suggestion • How about a ranking of problems solved? • N million unknowns solved in x hours? • It might be even easier to link this to • science or business output than HPL TFLOPS

  16. End

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