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Thoughts about Archiving Experimental Computer Science Artifacts (including Experiments)

Thoughts about Archiving Experimental Computer Science Artifacts (including Experiments). Jack W. Davidson Department of Computer Science University of Virginia. Platforms. Good News Fewer architectures in the desktop high-performance area X86, PowerPC Hardware performance counters

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Thoughts about Archiving Experimental Computer Science Artifacts (including Experiments)

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  1. Thoughts about Archiving Experimental Computer Science Artifacts (including Experiments) Jack W. DavidsonDepartment of Computer Science University of Virginia

  2. Platforms • Good News • Fewer architectures in the desktop high-performance area • X86, PowerPC • Hardware performance counters • Fewer operating systems • Linux, Windows • Accessible software infrastructures • gcc, llvm, jikes, openjdk, etc. • Available benchmark suites • SPEC, DaCapo, Parsec, etc.

  3. Platforms • Bad News • Rapid changes in microarchitecture • Same ISA, but very different microarchitecture • Hardware performance counters are often not documented correctly • GPUs becoming important • Still lots of embedded architectures—although ARM seems to be becoming dominant • All Linuxes are not the same

  4. Platforms • Bad News • Existing software infrastructures are sometimes impediments to experimentation • Cloud computing and public queue systems make controlling the environment difficult • Push to lock-down configurations because of security concerns • Embedded benchmarks, especially hard real-time benchmarks, are difficult to obtain because of proprietary concerns

  5. Virtual Machines • Good News • Virtual machines allow entire platforms (OS, software tools, etc) to be packaged and delivered • Very useful in security research where exploits are very fragile • Bad News • Hides machine details (not working on bare metal) • Longevity of VM images an issue • Licensing issues with Windows

  6. Final Thoughts • Education • Most graduate students don’t know how to do experimental computer science • Hard to teach—only learn by doing • Can we create a course that teaches fundamental concepts? • Incentives • Need good incentives for archiving • Can be in conflict with faculty entrepreneurs

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