‍This guest blog post is by Ash Vardanian, founder of Unum. He's focused on building high-performance computing and storage systems as part of

Understanding SIMD: Infinite Complexity of Trivial Problems

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2024-11-25 17:30:15

‍This guest blog post is by Ash Vardanian, founder of Unum. He's focused on building high-performance computing and storage systems as part of the open-source Unum Cloud project.

Modern CPUs have an incredible superpower: hyper-scalar operations, made available through single instruction, multiple data (SIMD) parallel processing. Instead of doing one operation at a time, a single core can do up to 4, 8, 16, or even 32 operations in parallel. In a way, a modern CPU is like a mini GPU, able to perform a lot of simultaneous calculations. Yet, because it’s so tricky to write parallel operations, almost all that potential remains untapped, resulting in code that only does one operation at a time

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.

Here, we share our learnings distilled from years of implementing SIMD kernels in the SimSIMD library, which currently powers vector math in dozens of DBMS products and AI companies with software being deployed on well over 100 million devices.

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