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https://github.com/IlyaGrebnov/libbsc

High performance block-sorting data compression library
https://github.com/IlyaGrebnov/libbsc

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High performance block-sorting data compression library

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README 

        
Introduction:

-------------



This file is a part of bsc and/or libbsc, a program and a library for

lossless, block-sorting data compression.



bsc is a high performance file compressor based on lossless,

block-sorting data compression algorithms.



libbsc is a library based on bsc, it uses the same algorithms

as bsc and enables you to compress memory blocks.



Copyright (c) 2009-2024 Ilya Grebnov 



See file AUTHORS for a full list of contributors.



See the bsc and libbsc web site:

  http://libbsc.com/ for more information.



Software License:

-----------------



Copyright (c) 2009-2024 Ilya Grebnov 



Licensed under the Apache License, Version 2.0 (the "License");

you may not use this file except in compliance with the License.

You may obtain a copy of the License at



    http://www.apache.org/licenses/LICENSE-2.0



Unless required by applicable law or agreed to in writing, software

distributed under the License is distributed on an "AS IS" BASIS,

WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.

See the License for the specific language governing permissions and

limitations under the License.



Please see the file LICENSE for full copyright information and file AUTHORS

for full list of contributors.



See also the bsc and libbsc web site:

  http://libbsc.com/ for more information.



Memory usage:

-------------



bsc compresses large files in blocks. Multiple blocks can be processed in

parallel on multiple-core CPU. At decompression time, the block size used

for compression is read from the header of the compressed file. The block

size and number of blocks processed in parallel affects both the compression

ratio achieved, and the amount of memory needed for compression and decompression.

Compression and decompression requirements are the same and in bytes, can

be estimated as 16Mb + 5 x block size x number of blocks processed in parallel.



GPU memory usage for NVIDIA CUDA technology is different from CPU memory usage

and can be estimated as 20 x block size for ST, 21 x block size for forward BWT

and 7 x block size for inverse BWT.



NVIDIA GPU acceleration:

------------------------



1. libbsc uses NVIDIA CUDA technology, resulting in a performance boost on computers

with NVIDIA GPU of compute capability 5.0 or higher. Lists of supported GPUs

can be found on the NVIDIA website http://developer.nvidia.com/cuda-gpus.

You also need to install latest graphics drivers that support CUDA.



2. Individual kernels are limited to a 2-second runtime by Windows. Kernels that run for

longer than 2 seconds will trigger the Timeout Detection and Recovery (TDR) mechanism.

Detailed information on disabling the Windows TDR is available at:

http://msdn.microsoft.com/en-us/windows/hardware/gg487368.aspx#E2