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Complete implementations from "Algorithms for Modern Hardware"

Home Page: https://en.algorithmica.org/hpc

C++ 4.40% Makefile 0.03% Assembly 0.70% Python 0.04% Jupyter Notebook 93.04% Java 0.02% Julia 0.01% C 1.76% Shell 0.01% Starlark 0.01% Sage 0.01%
algorithms hpc performance computer-science

amh-code's Introduction

Algorithms for Modern Hardware

This repository contains full examples and other associated code from https://en.algorithmica.org/hpc

The book is still unfinished, and my writing process is very slow and non-sequential — sometimes the "idea → code → benchmarks → article" pipeline may take 6 months or even more — so in this repository you can get a preview on a lot of interesting things that I haven't yet properly written up and published.

Things that have improved on the state-of-the-art:

Things that match current state-of-the-art:

Various benchmarks:

At the implementation stage:

  • Ordered Trees (apply the same technique as with binary searching, but with dynamically-allocated B-tree nodes)
  • Range minimum queries (both static and dynamic)
  • Filters (Bloom, cuckoo, xor, theoretical minimum)
  • Dot product / logistic regression (newton's method, SIMD, quantization)
  • Prime number sieves (blocking plus wheel)
  • Sorting (speeding up quicksort and mergesort with SIMD and radix sort)
  • Writing series of integers (SIMD + fast mod-10)
  • Bitmaps (blocking, SIMD)

At the idea stage:

  • String searching (SIMD-based strstr and rolling hashing)
  • Using SIMD to speed up Pollard's algorithm (naive sqrt-parallelization)
  • SIMD-based random number generation and hashing

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