Loop Fusion in Array Languages
Loop fusion in array languages addresses inefficiencies in processing arrays through optimized operations. The article discusses how combining operations can reduce memory usage and improve performance, particularly in comparison to scalar languages. It emphasizes the need for a balance between different levels of fusion to enhance efficiency without excessive memory use.
- ▪Interpreted array languages face performance issues due to unnecessary intermediate results.
- ▪Fusing operations can significantly reduce memory usage and improve cache performance.
- ▪The article suggests implementing two levels of fusion to optimize array processing.
Opening excerpt (first ~120 words) tap to expand
(github) / BQN / implementation / compile Loop fusion in array languages Interpreted array languages have a major problem. Let's say you evaluate some arithmetic on a few arrays. Perhaps the first operation adds two arrays. It will loop over them, ideally adding numbers a vector register at a time, and write the results to an array. Maybe next it will check if the result is more than 10. So it'll read vectors from the result, compare to 10, pack to bit booleans, and write to another array. Each primitive has been implemented well but the combination is already far from optimal! The first result array isn't needed: it would be much better to compare each added vector to 10 right when it's produced.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Github.
