Insights Parallel Programming on a CPU with AVX-512

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The discussion focuses on two distinct approaches to parallel programming for solving the problem of finding the best-fitting regression line for a set of points. The first approach utilizes Single-instruction multiple-thread (SIMT) programming on Nvidia GPUs, where a single instruction is executed simultaneously across numerous microprocessors. The second approach employs Single-instruction multiple data (SIMD) on x64 processors from Intel and AMD, allowing a single instruction to operate on wide registers containing vectors. The article invites comments and questions, highlighting a surprising result that challenges common assumptions about these programming methods.
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This article is the second of a two-part series that presents two distinctly different approaches to parallel programming. In the two articles, I use different approaches to solve the same problem: finding the best-fitting line (or regression line) for a set of points.
The two different approaches to parallel programming presented in this and the preceding Insights article (Parallel Programming on an NVIDIA GPU | Physics Forums) use these technologies.

Single-instruction multiple-thread (SIMT) programming is provided on the Nvidia® family of graphics processing units (GPUs). In SIMT programming, a single instruction is executed simultaneously on hundreds of microprocessors on a graphics card.
Single-instruction multiple data (SIMD) as provided on x64 processors from Intel® and AMD® (this article). In SIMD programming, a single instruction operates on wide registers that can contain vectors...

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That's an interesting, and surprising, result. Most people seem to have assumed the opposite. Thank you for this work!
 

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