In the spirit of yearend, top 10 lists, here is one for parallel computing. A version appeared earlier this year in a paper with Tim Mattson, who’s talked about this in other forums and gets full credit for its collation; it reflects thinking by some of the folks here, and is worth posting for discussion. Note the list is for parallel computing, so encompasses hardware, tools, and design considerations as well as programming. In priority order:
1. Finding concurrency in a program - how to help programmers “think parallel”?
2. Scheduling tasks at the right granularity onto the processors of a parallel machine.
3. The data locality problem: associating data with tasks and doing it in a way that our target audience will be able to use correctly.
4. Scalability support in hardware: bandwidth and latencies to memory plus interconnects between processing elements.
5. Scalability support in software: libraries, scalable algorithms, and adaptive runtimes to map high level software onto platform details.
6. Synchronization constructs (and protocols) that enable programmers write programs free from deadlock and race conditions.
7. Tools, API’s and methodologies to support the debugging process.
8. Error recovery and support for fault tolerance.
9. Support for good software engineering practices: composability, incremental parallelism, and code reuse.
10. Support for portable performance. What are the right models (or abstractions) so programmers can write code once and expect it to execute well on the important parallel platforms?
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