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Understanding SIMD: Infinite complexity of trivial problems
A deep dive into the complexities of optimizing code for SIMD instruction sets across multiple platforms.
Recently, the four of us, Ash Vardanian (@ashvardanian), Evan Ovadia (@verdagon), Daniel Lemiere (@lemire), and Chris Lattner (@clattner_llvm) talked about what's holding developers back from effectively using hyper-scalar operations more, and how we can create better abstractions for writing optimal software for CPUs. Here, we share what we learned from years of implementing SIMD kernels in the SimSIMD library, which currently powers vector math in dozens of Database Management Systems (DBMS) products and AI companies–with software deployed on well over 100 million devices. In Part 2 of this series, we'll show how Mojo makes it easy to write similarly performant algorithms using built-in language features, so subscribe to the RSS feed to get notified when that comes!
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