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Presentation Slides, PDFs, Source Code and other presenter materials are available at: https://github.com/CppCon/CppCon2018
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Leveraging SIMD (Single Instruction Multiple Data) instructions are an important part of fully utilizing modern processors. However, utilizing SIMD hardware features in C++ can be difficult as it requires an understanding of how the underlying instructions work. Furthermore, there are not yet standardized ways to express C++ in ways which can guarantee such instructions are used to increase performance effectively. 

This talk aims to demystify how SIMD instructions can benefit the performance of applications and libraries, as well as demonstrate how a C++ SIMD wrapper library can greatly ease programmers in writing efficient, cross-platform SIMD code. While one particular library will be used to demonstrate elegant SIMD programming, the concepts shown are applicable to practically every C++ SIMD library currently available (e.g. boost.simd, tsimd, VC, dimsum, etc.), as well as the proposed SIMD extensions to the C++ standard library. 

Lastly, this talk will also seek to unify the greater topic of data parallelism in C++ by connecting the SIMD parallelism concepts demonstrated to other expressions of parallelism, such as SPMD/SIMT parallelism used in GPU computing.
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Jefferson Amstutz, Software Engineer
Intel
Jeff is a Visualization Software Engineer at Intel, where he leads the open source OSPRay project. He enjoys all things ray tracing, high performance computing, clearly implemented code, and the perfect combination of git, CMake, and modern C++.
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Videos Filmed & Edited by Bash Films: http://www.BashFilms.com 

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