Source: c-blosc Priority: optional Section: libs Maintainer: Daniel Stender Build-Depends: debhelper (>= 11), cmake, liblz4-dev (>= 0.0~r130), libsnappy-dev, zlib1g-dev, libzstd-dev, python-docutils , links , pandoc Standards-Version: 4.2.1 Homepage: http://blosc.org/ Vcs-Browser: https://salsa.debian.org/debian/c-blosc Vcs-Git: https://salsa.debian.org/debian/c-blosc.git Package: libblosc-dev Section: libdevel Architecture: any Depends: libblosc1 (= ${binary:Version}), ${misc:Depends} Description: high performance meta-compressor optimized for binary data (development files) Blosc is a high performance compressor optimized for binary data. It has been designed to transmit data to the processor cache faster than the traditional, non-compressed, direct memory fetch approach via a memcpy() OS call. Blosc is meant not only to reduce the size of large datasets on-disk or in-memory, but also to accelerate memory-bound computations. . It uses the blocking technique to reduce activity on the memory bus as much as possible. In short, this technique works by dividing datasets in blocks that are small enough to fit in caches of modern processors and perform compression / decompression there. It also leverages, if available, SIMD instructions (SSE2) and multi-threading capabilities of CPUs, in order to accelerate the compression / decompression process to a maximum. . This package contains the development files required to build programs against Blosc. Package: libblosc1 Architecture: any Depends: ${shlibs:Depends}, ${misc:Depends} Description: high performance meta-compressor optimized for binary data Blosc is a high performance compressor optimized for binary data. It has been designed to transmit data to the processor cache faster than the traditional, non-compressed, direct memory fetch approach via a memcpy() OS call. Blosc is meant not only to reduce the size of large datasets on-disk or in-memory, but also to accelerate memory-bound computations. . It uses the blocking technique to reduce activity on the memory bus as much as possible. In short, this technique works by dividing datasets in blocks that are small enough to fit in caches of modern processors and perform compression / decompression there. It also leverages, if available, SIMD instructions (SSE2) and multi-threading capabilities of CPUs, in order to accelerate the compression / decompression process to a maximum.