Compile-Time Sparse Matrices - Optimizing Memory and Performance
Offered By: Meeting Cpp via YouTube
Course Description
Overview
Explore compile-time sparse matrices in C++ linear algebra libraries through this conference talk from Meeting C++ 2023. Delve into the limitations of classical libraries that provide dense matrices and sparse matrices with run-time sparseness. Learn how to incorporate sparseness information into matrix classes at compile-time, reducing memory overhead while maintaining run-time efficiency. Discover techniques for storing only necessary entries, making matrices memory-efficient. Understand how this approach enables automatic removal of unnecessary operations by the compiler, resulting in performance comparable to or even surpassing hand-written sparse-matrix code. Gain insights into combining memory efficiency with run-time efficiency for a "free lunch" in C++ linear algebra implementations.
Syllabus
Compile-time sparse matrices - Daniel Withopf - Meeting C++ 2023
Taught by
Meeting Cpp
Related Courses
Stanford Seminar - MIPS Open, Wave ComputingStanford University via YouTube Loop Analysis and Vectorization in Julia - JuliaCon 2020
The Julia Programming Language via YouTube Intrinsic Functions and Vector Processing Extensions for SIMD Parallel Operations in C++
javidx9 via YouTube Intrinsics - Low-Level Engine Development with Burst - Unite Copenhagen
Unity via YouTube Aggregating Ticks to Manage Scale in Sea of Thieves - Unreal Fest Europe 2019 - Unreal Engine
Unreal Engine via YouTube