mirror of https://github.com/status-im/leopard.git
332 lines
14 KiB
Markdown
332 lines
14 KiB
Markdown
# Leopard-RS
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## Reed-Solomon Error Correction Codes for Large Data in C
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#### This software is still under active development. It may or may not work right now. I'm trying to get it done ASAP. Current latest result is that K=128 code rate 1/2 is working and benchmarks are posted here: [http://catid.mechafetus.com/news/news.php?view=399](http://catid.mechafetus.com/news/news.php?view=399)
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Leopard-RS is a fast library for Forward Error Correction.
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From a block of equally sized original data pieces, it generates recovery
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symbols that can be used to recover lost original data.
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#### Motivation:
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It gets slower as O(N Log N) in the input data size, and its inner loops are
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vectorized using the best approaches available on modern processors, using the
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fastest finite fields (8-bit or 16-bit Galois fields with Cantor basis {2}).
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It sets new speed records for MDS encoding and decoding of large data,
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achieving over 1.2 GB/s to encode with the AVX2 instruction set on a single core.
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There is another library `FastECC` by Bulat-Ziganshin that should have similar performance:
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[https://github.com/Bulat-Ziganshin/FastECC](https://github.com/Bulat-Ziganshin/FastECC)
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Example applications are data recovery software and data center replication.
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#### Encoder API:
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Preconditions:
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* The original and recovery data must not exceed 65536 pieces.
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* The recovery_count <= original_count.
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* The buffer_bytes must be a multiple of 64.
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* Each buffer should have the same number of bytes.
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* Even the last piece must be rounded up to the block size.
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```
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#include "leopard.h"
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```
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For full documentation please read `leopard.h`.
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+ `leo_init()` : Initialize library.
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+ `leo_encode_work_count()` : Calculate the number of work_data buffers to provide to leo_encode().
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+ `leo_encode()`: Generate recovery data.
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#### Decoder API:
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For full documentation please read `leopard.h`.
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+ `leo_init()` : Initialize library.
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+ `leo_decode_work_count()` : Calculate the number of work_data buffers to provide to leo_decode().
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+ `leo_decode()` : Recover original data.
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#### Benchmarks:
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On my laptop:
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```
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Leopard Encoder(8.192 MB in 128 pieces, 128 losses): Input=1242.62 MB/s, Output=1242.62 MB/s
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Leopard Decoder(8.192 MB in 128 pieces, 128 losses): Input=482.53 MB/s, Output=482.53 MB/s
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```
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#### Comparisons:
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Comparing performance from all my error correction code libraries, on my laptop:
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To summarize, a set of 128 of 64 KB data files are supplemented by about 128 redundant code pieces (encoded) meaning a code rate of 1/2. From those redundant code pieces the original set is recovered (decoded).
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The results are all from libraries I've written over the past few years. They all have the same vector-optimized inner loops, but the types of error correction codes are different.
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```
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For 64KB data chunks:
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CM256 Encoder: 64000 bytes k = 128 m = 128 : 82194.7 usec, 99.6658 MBps
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CM256 Decoder: 64000 bytes k = 128 m = 128 : 78279.5 usec, 104.651 MBps
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Longhair Encoded k=128 data blocks with m=128 recovery blocks in 81641.2 usec : 100.342 MB/s
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Longhair Decoded 128 erasures in 85000.7 usec : 96.3757 MB/s
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WH256 wirehair_encode(N = 128) in 12381.3 usec, 661.644 MB/s after 127.385 avg losses
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WH256 wirehair_decode(N = 128) average overhead = 0.025 blocks, average reconstruct time = 9868.65 usec, 830.103 MB/s
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FEC-AL Encoder(8.192 MB in 128 pieces, 128 losses): Input=518.545 MB/s, Output=518.545 MB/s, (Encode create: 3762.73 MB/s)
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FEC-AL Decoder(8.192 MB in 128 pieces, 128 losses): Input=121.093 MB/s, Output=121.093 MB/s, (Overhead = 0 pieces)
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Leopard Encoder(8.192 MB in 128 pieces, 128 losses): Input=1242.62 MB/s, Output=1242.62 MB/s
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Leopard Decoder(8.192 MB in 128 pieces, 128 losses): Input=482.53 MB/s, Output=482.53 MB/s
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```
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For 128 data pieces of input and 128 data pieces of redundancy:
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Fastest to encode: Leopard (1.2 GB/s)
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Distant second-place: WH256 (660 MB/s), FEC-AL (515 MB/s)
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Slowest encoders: Longhair, CM256
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Fastest to decode: WH256 (830 MB/s)
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Distant second-place: Leopard (482 MB/s)
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Slowest decoders: FEC-AL, CM256, Longhair
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There are a lot of variables that affect when each of these libraries should be used.
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Each one is ideal in a different situation, and no one library can be called the best overall.
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The situation tested mainly helps explore the trade-offs of WH256, FEC-AL and Leopard for code rate 1/2.
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##### CM256: Traditional O(N^2) Cauchy matrix MDS Reed-Solomon codec
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Runs at about 100 MB/s encode and decode for this case.
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This is an MDS code that uses a Cauchy matrix for structure.
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Other examples of this type would be most MDS Reed-Solomon codecs online: Jerasure, Zfec, ISA-L, etc.
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It requires SSSE3 or newer Intel instruction sets for this speed. Otherwise it runs much slower.
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This type of software gets slower as O(K*M) where K = input count and M = recovery count.
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It is practical for either small data or small recovery set up to 255 pieces.
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It is available for production use under BSD license here:
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http://github.com/catid/cm256
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(Note that the inner loops can be optimized more by applying the GF256 library.)
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##### Longhair: Binary O(N^2) Cauchy matrix MDS Reed-Solomon codec
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Runs at about 100 MB/s encode and decode for this case.
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This is an MDS code that uses a Cauchy matrix for structure.
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This one only requires XOR operations so it can run fast on low-end processors.
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Requires data is a multiple of 8 bytes.
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This type of software gets slower as O(K*M) where K = input count and M = recovery count.
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It is practical for either small data or small recovery set up to 255 pieces.
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There is no other optimized software available online for this type of error correction code. There is a slow version available in the Jerasure software library.
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It is available for production use under BSD license here:
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http://github.com/catid/longhair
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(Note that the inner loops can be optimized more by applying the GF256 library.)
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##### Wirehair: O(N) Hybrid LDPC Erasure Code
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Encodes at 660 MB/s, and decodes at 830 MB/s for ALL cases.
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This is not an MDS code. It has about a 3% chance of failing to recover and requiring one extra block of data.
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It uses mostly XOR so it only gets a little slower on lower-end processors.
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This type of software gets slower as O(K) where K = input count.
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This library incorporates some novel ideas that are unpublished. The new ideas are described in the source code.
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It is practical for data up to 64,000 pieces and can be used as a "fountain" code.
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There is no other optimized software available online for this type of error correction code. I believe there are some public (slow) implementations of Raptor codes available online for study.
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It is available for production use under BSD license here:
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http://github.com/catid/wirehair
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There's a pre-production version that needs more work here using GF256 for more speed,
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which is what I used for the benchmark:
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http://github.com/catid/wh256
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##### FEC-AL *new*: O(N^2/8) XOR Structured Convolutional Matrix Code
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Encodes at 510 MB/s. Decodes at 121 MB/s.
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This is not an MDS code. It has about a 1% chance of failing to recover and requiring one extra block of data.
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This library incorporates some novel ideas that are unpublished. The new ideas are described in the README.
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It uses mostly XOR operations so only gets about 2-4x slower on lower-end processors.
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It gets slower as O(K*M/8) for larger data, bounded by the speed of XOR.
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This new approach is ideal for streaming erasure codes; two implementations are offered one for files and another for real-time streaming reliable data.
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It is practical for data up to about 4,000 pieces and can be used as a "fountain" code.
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There is no other software available online for this type of error correction code.
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It is available for production use under BSD license here:
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http://github.com/catid/fecal
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It can also be used as a convolutional streaming code here for e.g. rUDP:
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http://github.com/catid/siamese
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##### Leopard-RS *new*: O(K Log M) FFT MDS Reed-Solomon codec
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Encodes at 1.2 GB/s, and decodes at 480 MB/s for this case.
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12x faster than existing MDS approaches to encode, and almost 5x faster to decode.
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This uses a recent result from 2014 introducing a novel polynomial basis permitting FFT over fast Galois fields.
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This is an MDS Reed-Solomon similar to Jerasure, Zfec, ISA-L, etc, but much faster.
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It requires SSSE3 or newer Intel instruction sets for this speed. Otherwise it runs much slower.
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Requires data is a multiple of 64 bytes.
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This type of software gets slower as O(K Log M) where K = input count, M = recovery count.
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It is practical for extremely large data.
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There is no other software available online for this type of error correction code.
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#### FFT Data Layout:
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We pack the data into memory in this order:
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~~~
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[Recovery Data (Power of Two = M)] [Original Data] [Zero Padding out to 65536]
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~~~
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For encoding, the placement is implied instead of actual memory layout.
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For decoding, the layout is explicitly used.
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#### Encoder algorithm:
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The encoder is described in {3}. Operations are done O(K Log M),
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where K is the original data size, and M is up to twice the
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size of the recovery set.
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Roughly in brief:
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~~~
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Recovery = FFT( IFFT(Data_0) xor IFFT(Data_1) xor ... )
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~~~
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It walks the original data M chunks at a time performing the IFFT.
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Each IFFT intermediate result is XORed together into the first M chunks of
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the data layout. Finally the FFT is performed.
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Encoder optimizations:
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* The first IFFT can be performed directly in the first M chunks.
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* The zero padding can be skipped while performing the final IFFT.
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Unrolling is used in the code to accomplish both these optimizations.
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* The final FFT can be truncated also if recovery set is not a power of 2.
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It is easy to truncate the FFT by ending the inner loop early.
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#### Decoder algorithm:
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The decoder is described in {1}. Operations are done O(N Log N), where N is up
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to twice the size of the original data as described below.
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Roughly in brief:
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~~~
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Original = -ErrLocator * FFT( Derivative( IFFT( ErrLocator * ReceivedData ) ) )
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~~~
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#### Precalculations:
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At startup initialization, FFTInitialize() precalculates FWT(L) as
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described by equation (92) in {1}, where L = Log[i] for i = 0..Order,
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Order = 256 or 65536 for FF8/16. This is stored in the LogWalsh vector.
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It also precalculates the FFT skew factors (s_i) as described by
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equation (28). This is stored in the FFTSkew vector.
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For memory workspace N data chunks are needed, where N is a power of two
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at or above M + K. K is the original data size and M is the next power
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of two above the recovery data size. For example for K = 200 pieces of
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data and 10% redundancy, there are 20 redundant pieces, which rounds up
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to 32 = M. M + K = 232 pieces, so N rounds up to 256.
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#### Online calculations:
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At runtime, the error locator polynomial is evaluated using the
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Fast Walsh-Hadamard transform as described in {1} equation (92).
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At runtime the data is explicit laid out in workspace memory like this:
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[Recovery Data (Power of Two = M)] [Original Data (K)] [Zero Padding out to N]
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Data that was lost is replaced with zeroes.
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Data that was received, including recovery data, is multiplied by the error
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locator polynomial as it is copied into the workspace.
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The IFFT is applied to the entire workspace of N chunks.
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Since the IFFT starts with pairs of inputs and doubles in width at each
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iteration, the IFFT is optimized by skipping zero padding at the end until
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it starts mixing with non-zero data.
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The formal derivative is applied to the entire workspace of N chunks.
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The FFT is applied to the entire workspace of N chunks.
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The FFT is optimized by only performing intermediate calculations required
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to recover lost data. Since it starts wide and ends up working on adjacent
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pairs, at some point the intermediate results are not needed for data that
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will not be read by the application. This optimization is implemented by
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the ErrorBitfield class.
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Finally, only recovered data is multiplied by the negative of the
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error locator polynomial as it is copied into the front of the
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workspace for the application to retrieve.
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#### Future directions:
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Note that a faster decoder is described in {3} that is O(K Log M) instead,
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which should be 2x faster than the current one. However I do not fully
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understand how to implement it for this field and could use some help.
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#### Finite field arithmetic optimizations:
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For faster finite field multiplication, large tables are precomputed and
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applied during encoding/decoding on 64 bytes of data at a time using
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SSSE3 or AVX2 vector instructions and the ALTMAP approach from Jerasure.
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Addition in this finite field is XOR, and a vectorized memory XOR routine
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is also used.
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#### References:
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This library implements an MDS erasure code introduced in this paper:
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~~~
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{1} S.-J. Lin, T. Y. Al-Naffouri, Y. S. Han, and W.-H. Chung,
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"Novel Polynomial Basis with Fast Fourier Transform and Its Application to Reed-Solomon Erasure Codes"
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IEEE Trans. on Information Theory, pp. 6284-6299, November, 2016.
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~~~
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~~~
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{2} D. G. Cantor, "On arithmetical algorithms over finite fields",
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Journal of Combinatorial Theory, Series A, vol. 50, no. 2, pp. 285-300, 1989.
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~~~
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~~~
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{3} Sian-Jheng Lin, Wei-Ho Chung, “An Efficient (n, k) Information
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Dispersal Algorithm for High Code Rate System over Fermat Fields,”
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IEEE Commun. Lett., vol.16, no.12, pp. 2036-2039, Dec. 2012.
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~~~
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Some papers are mirrored in the /docs/ folder.
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#### Credits
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Inspired by discussion with:
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Sian-Jhen Lin <sjhenglin@gmail.com> : Author of {1} {3}, basis for Leopard
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Bulat Ziganshin <bulat.ziganshin@gmail.com> : Author of FastECC
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Yutaka Sawada <tenfon@outlook.jp> : Author of MultiPar
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This software was written entirely by myself ( Christopher A. Taylor mrcatid@gmail.com ). If you find it useful and would like to buy me a coffee, consider tipping.
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