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parallel-ml-bench reviews and mentions
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Efficient Parallelization of an Ubiquitous Sequential Computation
Altogether, this gives you O(n) work and O(log n) span, but using just a single parallel prefix kernel, which may be more efficient in practice.
For example, here's a C++ implementation: https://github.com/MPLLang/parallel-ml-bench/blob/main/cpp/l...
And, here's an implementation in a functional language: https://github.com/MPLLang/parallel-ml-bench/blob/main/mpl/b...
I'm pretty sure this generalizes, too, to abstract multiplications and additions in any field (at first glance, seems like it should but I haven't done the formal proof yet).
Anyway, it would be interesting to compare this against the solution in the arxiv paper.
- Parallel ML Benchmark Suite
- Parallel ML benchmark suite
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A note from our sponsor - InfluxDB
www.influxdata.com | 25 Apr 2024
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MPLLang/parallel-ml-bench is an open source project licensed under MIT License which is an OSI approved license.
The primary programming language of parallel-ml-bench is Standard ML.
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