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parallel-computing-overview [June 15, 2026 at 09:34] – Ivan Janevskiparallel-computing-overview [August 22, 2026 at 15:22] (current) – external edit 127.0.0.1
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 On the performance side, the [[roofline-model|roofline model]] is a useful frame for understanding whether a kernel is compute-bound or memory-bandwidth-bound, which determines where to focus optimization effort. For quick empirical benchmarks, [[saxpy]] — a simple vector operation — is a standard starting point for measuring memory bandwidth. On the performance side, the [[roofline-model|roofline model]] is a useful frame for understanding whether a kernel is compute-bound or memory-bandwidth-bound, which determines where to focus optimization effort. For quick empirical benchmarks, [[saxpy]] — a simple vector operation — is a standard starting point for measuring memory bandwidth.
  
-## Practice+## Example
  
 The fastest way to see parallelism pay off is to compile the same program twice — once without threading, once with — and time both. Here is a parallel sum using OpenMP: The fastest way to see parallelism pay off is to compile the same program twice — once without threading, once with — and time both. Here is a parallel sum using OpenMP:
parallel-computing-overview.1781516094.md.gz · Last modified: by Ivan Janevski