status-im
/
leopard
mirror of https://github.com/status-im/leopard.git
2
0
Fork 0
Code Issues Projects Releases Wiki Activity

Update README.md

This commit is contained in:
Christopher A. Taylor 2017-06-04 21:31:32 -07:00 committed by GitHub
parent 48bb280858
commit 45bbcb4bfa
1 changed files with 13 additions and 10 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

23
README.md
View File

@ -15,16 +15,6 @@ fastest finite fields (8-bit or 16-bit Galois fields with Cantor basis {2}).
It sets new speed records for MDS encoding and decoding of large data, It sets new speed records for MDS encoding and decoding of large data,
achieving over 1.2 GB/s to encode with the AVX2 instruction set on a single core. achieving over 1.2 GB/s to encode with the AVX2 instruction set on a single core.
There is another library `FastECC` by Bulat-Ziganshin that should have similar performance:
[https://github.com/Bulat-Ziganshin/FastECC](https://github.com/Bulat-Ziganshin/FastECC).
Both libraries implement the same high-level algorithm in {3}, while Leopard implements the
newer polynomial basis GF(2^r) approach outlined in {1}, and FastECC uses complex finite fields
modulo special primes. There are trade-offs that may make either approach preferable based
on the application:
+ Older processors do not support SSSE3 and FastECC supports these processors better.
+ FastECC supports data sets above 65,536 pieces as it uses 32-bit finite field math.
+ Leopard does not require expanding the input or output data to make it fit in the field, so it can be more space efficient.
Example applications are data recovery software and data center replication. Example applications are data recovery software and data center replication.
@ -79,6 +69,19 @@ More benchmark results are available here:
[https://github.com/catid/leopard/blob/master/Benchmarks.md](https://github.com/catid/leopard/blob/master/Benchmarks.md) [https://github.com/catid/leopard/blob/master/Benchmarks.md](https://github.com/catid/leopard/blob/master/Benchmarks.md)
#### Comparisons:
There is another library `FastECC` by Bulat-Ziganshin that should have similar performance:
[https://github.com/Bulat-Ziganshin/FastECC](https://github.com/Bulat-Ziganshin/FastECC).
Both libraries implement the same high-level algorithm in {3}, while Leopard implements the
newer polynomial basis GF(2^r) approach outlined in {1}, and FastECC uses complex finite fields
modulo special primes. There are trade-offs that may make either approach preferable based
on the application:
+ Older processors do not support SSSE3 and FastECC supports these processors better.
+ FastECC supports data sets above 65,536 pieces as it uses 32-bit finite field math.
+ Leopard does not require expanding the input or output data to make it fit in the field, so it can be more space efficient.
#### FFT Data Layout: #### FFT Data Layout:
We pack the data into memory in this order: We pack the data into memory in this order: