From 9cfb95ba74551a608f41380f9c46ef52b831ce74 Mon Sep 17 00:00:00 2001 From: MaZderMind Date: Fri, 25 Jul 2014 19:50:42 +0200 Subject: [PATCH] fix xml error --- pydata14/schedule.xml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pydata14/schedule.xml b/pydata14/schedule.xml index 35f1a12..4fe3984 100644 --- a/pydata14/schedule.xml +++ b/pydata14/schedule.xml @@ -661,7 +661,7 @@ false - B09/room> + B09 en Bloscpack [1] is a reference implementation and file-format for fast serialization of numerical data. It features lightweight, chunked and compressed storage, based on the extremely fast Blosc [2] metacodec and supports serialization of Numpy arrays out-of-the-box. Recently, Blosc -- being the metacodec that it is -- has received support for using the popular and widely used Snappy [3], LZ4 [4], and ZLib [5] codecs, and so, now Bloscpack supports serializing Numpy arrays easily with those codecs! In this talk I will present recent benchmarks of Bloscpack performance on a variety of artificial and real-world datasets with a special focus on the newly available codecs. In these benchmarks I will compare Bloscpack, both performance and usability wise, to alternatives such as Numpy's native offerings (NPZ and NPY), HDF5/PyTables [6], and if time permits, to novel bleeding edge solutions. Lastly I will argue that compressed and chunked storage format such as Bloscpack can be and somewhat already is a useful substrate on which to build more powerful applications such as online analytical processing engines and distributed computing frameworks. [1]: https://github.com/Blosc/bloscpack [2]: https://github.com/Blosc/c-blosc/ [3]: http://code.google.com/p/snappy/ [4]: http://code.google.com/p/lz4/ [5]: http://www.zlib.net/ [6]: http://www.pytables.org/moin Bloscpack [1] is a reference implementation and file-format for fast serialization of numerical data. It features lightweight, chunked and compressed storage, based on the extremely fast Blosc [2] metacodec and supports serialization of Numpy arrays out-of-the-box. Recently, Blosc -- being the metacodec that it is -- has received support for using the popular and widely used Snappy [3], LZ4 [4], and ZLib [5] codecs, and so, now Bloscpack supports serializing Numpy arrays easily with those codecs! In this talk I will present recent benchmarks of Bloscpack performance on a variety of artificial and real-world datasets with a special focus on the newly available codecs. In these benchmarks I will compare Bloscpack, both performance and usability wise, to alternatives such as Numpy's native offerings (NPZ and NPY), HDF5/PyTables [6], and if time permits, to novel bleeding edge solutions. Lastly I will argue that compressed and chunked storage format such as Bloscpack can be and somewhat already is a useful substrate on which to build more powerful applications such as online analytical processing engines and distributed computing frameworks. [1]: https://github.com/Blosc/bloscpack [2]: https://github.com/Blosc/c-blosc/ [3]: http://code.google.com/p/snappy/ [4]: http://code.google.com/p/lz4/ [5]: http://www.zlib.net/ [6]: http://www.pytables.org/moin