guoswork/frp
1
0
Fork 0
mirror of https://gitee.com/IrisVega/frp.git synced 2024-11-01 22:31:29 +08:00
Code Issues Packages Projects Releases Wiki Activity
dd8f788ca4
frp/vendor/github.com/templexxx/reedsolomon
History
fatedier dd8f788ca4 use dep instead of glide
2018-05-08 02:40:11 +08:00
..
.gitignore add packages 2017-10-25 02:29:04 +08:00
.travis.yml add packages 2017-10-25 02:29:04 +08:00
LICENSE add packages 2017-10-25 02:29:04 +08:00
matrix.go add packages 2017-10-25 02:29:04 +08:00
README.md add packages 2017-10-25 02:29:04 +08:00
rs_amd64.go add packages 2017-10-25 02:29:04 +08:00
rs_amd64.s add packages 2017-10-25 02:29:04 +08:00
rs_other.go add packages 2017-10-25 02:29:04 +08:00
rs.go add packages 2017-10-25 02:29:04 +08:00
tbl.go add packages 2017-10-25 02:29:04 +08:00

README.md

Reed-Solomon

GoDoc MIT licensed Build Status Go Report Card

Introduction:

  1. Reed-Solomon Erasure Code engine in pure Go.
  2. Super Fast: more than 10GB/s per physics core ( 10+4, 4KB per vector, Macbook Pro 2.8 GHz Intel Core i7 )

Installation

To get the package use the standard:

go get github.com/templexxx/reedsolomon

Documentation

See the associated GoDoc

Specification

GOARCH

  1. All arch are supported
  2. 0.1.0 need go1.9 for sync.Map in AMD64

Math

  1. Coding over in GF(2^8)
  2. Primitive Polynomial: x^8 + x^4 + x^3 + x^2 + 1 (0x1d)
  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
  4. mathtool/cntinverse.go : calculate how many inverse matrix will have in different RS codes config
  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

Why so fast?

These three parts will cost too much time:

  1. lookup galois-field tables
  2. read/write memory
  3. calculate inverse matrix in the reconstruct process

SIMD will solve no.1

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.

Performance

Performance depends mainly on:

  1. CPU instruction extension( AVX2 or SSSE3 or none )
  2. number of data/parity vects
  3. unit size of calculation ( see it in rs_amd64.go )
  4. size of shards
  5. speed of memory (waste so much time on read/write mem, :D )
  6. performance of CPU
  7. the way of using ( reuse memory)

And we must know the benchmark test is quite different with encoding/decoding in practice.

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.

Example of performance on my MacBook 2017 i7 2.8GHz. 10+4 (with 0.1.0).

Encoding:

Vector size Speed (MB/S)
1400B 7655.02
4KB 10551.37
64KB 9297.25
1MB 6829.89
16MB 6312.83

Reconstruct (use nil to point which one need repair):

Vector size Speed (MB/S)
1400B 4124.85
4KB 5715.45
64KB 6050.06
1MB 5001.21
16MB 5043.04

ReconstructWithPos (use a position list to point which one need repair, reuse the memory):

Vector size Speed (MB/S)
1400B 6170.24
4KB 9444.86
64KB 9311.30
1MB 6781.06
16MB 6285.34

reconstruct benchmark tests here run with inverse matrix cache, if there is no cache, it will cost more time( about 1000ns)

Who is using this?

  1. https://github.com/xtaci/kcp-go -- A Production-Grade Reliable-UDP Library for golang

Links & Thanks