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110 lines
3.9 KiB
Markdown
110 lines
3.9 KiB
Markdown
# Reed-Solomon
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[![GoDoc][1]][2] [![MIT licensed][3]][4] [![Build Status][5]][6] [![Go Report Card][7]][8]
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[1]: https://godoc.org/github.com/templexxx/reedsolomon?status.svg
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[2]: https://godoc.org/github.com/templexxx/reedsolomon
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[3]: https://img.shields.io/badge/license-MIT-blue.svg
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[4]: LICENSE
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[5]: https://travis-ci.org/templexxx/reedsolomon.svg?branch=master
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[6]: https://travis-ci.org/templexxx/reedsolomon
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[7]: https://goreportcard.com/badge/github.com/templexxx/reedsolomon
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[8]: https://goreportcard.com/report/github.com/templexxx/reedsolomon
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## Introduction:
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1. Reed-Solomon Erasure Code engine in pure Go.
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2. Super Fast: more than 10GB/s per physics core ( 10+4, 4KB per vector, Macbook Pro 2.8 GHz Intel Core i7 )
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## Installation
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To get the package use the standard:
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```bash
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go get github.com/templexxx/reedsolomon
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```
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## Documentation
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See the associated [GoDoc](http://godoc.org/github.com/templexxx/reedsolomon)
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## Specification
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### GOARCH
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1. All arch are supported
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2. 0.1.0 need go1.9 for sync.Map in AMD64
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### Math
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1. Coding over in GF(2^8)
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2. Primitive Polynomial: x^8 + x^4 + x^3 + x^2 + 1 (0x1d)
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3. mathtool/gentbls.go : generator Primitive Polynomial and it's log table, exp table, multiply table, inverse table etc. We can get more info about how galois field work
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4. mathtool/cntinverse.go : calculate how many inverse matrix will have in different RS codes config
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5. Both of Cauchy and Vandermonde Matrix are supported. Vandermonde need more operations for preserving the property that any square subset of rows is invertible
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### Why so fast?
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These three parts will cost too much time:
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1. lookup galois-field tables
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2. read/write memory
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3. calculate inverse matrix in the reconstruct process
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SIMD will solve no.1
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Cache-friendly codes will help to solve no.2 & no.3, and more, use a sync.Map for cache inverse matrix, it will help to save about 1000ns when we need same matrix.
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## Performance
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Performance depends mainly on:
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1. CPU instruction extension( AVX2 or SSSE3 or none )
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2. number of data/parity vects
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3. unit size of calculation ( see it in rs_amd64.go )
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4. size of shards
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5. speed of memory (waste so much time on read/write mem, :D )
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6. performance of CPU
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7. the way of using ( reuse memory)
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And we must know the benchmark test is quite different with encoding/decoding in practice.
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Because in benchmark test loops, the CPU Cache will help a lot. In practice, we must reuse the memory to make the performance become as good as the benchmark test.
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Example of performance on my MacBook 2017 i7 2.8GHz. 10+4 (with 0.1.0).
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### Encoding:
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| Vector size | Speed (MB/S) |
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|----------------|--------------|
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| 1400B | 7655.02 |
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| 4KB | 10551.37 |
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| 64KB | 9297.25 |
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| 1MB | 6829.89 |
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| 16MB | 6312.83 |
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### Reconstruct (use nil to point which one need repair):
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| Vector size | Speed (MB/S) |
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|----------------|--------------|
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| 1400B | 4124.85 |
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| 4KB | 5715.45 |
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| 64KB | 6050.06 |
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| 1MB | 5001.21 |
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| 16MB | 5043.04 |
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### ReconstructWithPos (use a position list to point which one need repair, reuse the memory):
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| Vector size | Speed (MB/S) |
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|----------------|--------------|
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| 1400B | 6170.24 |
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| 4KB | 9444.86 |
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| 64KB | 9311.30 |
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| 1MB | 6781.06 |
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| 16MB | 6285.34 |
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**reconstruct benchmark tests here run with inverse matrix cache, if there is no cache, it will cost more time( about 1000ns)**
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## Who is using this?
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1. https://github.com/xtaci/kcp-go -- A Production-Grade Reliable-UDP Library for golang
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## Links & Thanks
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* [Klauspost ReedSolomon](https://github.com/klauspost/reedsolomon)
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* [intel ISA-L](https://github.com/01org/isa-l)
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* [GF SIMD] (http://www.ssrc.ucsc.edu/papers/plank-fast13.pdf)
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* [asm2plan9s] (https://github.com/fwessels/asm2plan9s)
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