[R-pkgs] major release ff 2.0 (large atomic objects)

From: Jens Oehlschlägel <jens.oehlschlaegel_at_truecluster.com>
Date: Mon, 04 Aug 2008 10:12:56 +0200


Dear R community,

ff Version 2.0 is available on CRAN. Based on paging concepts from version 1.0, 2.0 is a major redesign of this package for handling large datasets. We have implemented numerous enhancements and performance improvements to make this package suitable as a 'base' package for large data processing.

The ff package provides atomic data structures that are stored on disk but behave (almost) as if they were in RAM by transparently mapping only a section (pagesize) in main memory - the effective virtual memory consumption per ff object.

In addition to the 'double' data type, ff objects now have support for 'logical', 'raw' and 'integer' atomic datatypes, plus close-to-atomic types like 'factor', 'POSIXct' or custom close-to-atomic types. In addition to fast vector access, ff now has native support for matrices and arrays with flexible dimorder (major column-order, major row-order and generalizations for arrays).

While the raw data still gets stored on binary flat files in native encoding, 'ff' objects have been extended to carry their meta information as physical and virtual attributes. ff objects have well-defined hybrid copying semantics, which gives rise to certain performance improvements through virtualization.

The new ff objects can be stored and reopened across R sessions. Flat files can be shared by multiple 'ff' R objects (using different data en/de-coding schemes) in the same process or from multiple R processes to exploit parallelism. A wide choice of finalizer options allows to work with 'permanent' files as well as creating/removing 'temporary' ff files completely transparent to the user. On certain OS/Filesystem combinations, the creation process of large atomic data sets has been speed-up dramatically using sparse file allocation.

Several access optimization techniques such as Hybrid Index Preprocessing and Virtualization are implemented to achieve good performance even with large datasets, for example virtual matrix transpose without touching a single byte on disk.

Further, to reduce disk I/O, the atomic data gets stored native and compact on binary flat files i.e. logicals take up exactly 2 bits to represent TRUE, FALSE and NA.

Beyond basic access functions, the ff package also provides compatibility functions that facilitate writing code for ff and ram objects and support for batch processing on ff objects (e.g. as.ram, as.ff, ffapply).

A package that supports convenient processing of large ff objects is in the making. R.ff will make the bigger part of R's basic functions available for ff objects through method dispatch and/or an evaluator that handles expressions which contain ff objects.

NOTE: A professional extension is available from the authors, which integrates

      additional high-performance features neatly into the ff package. 
      The extension allows  efficient handling of symmetric matrices 
      and supports more packed data types: 
      boolean (1 bit), quad (2 bit unsigned), nibble (4 bit unsigned)

, byte (1 byte signed with NAs), ubyte (1 byte unsigned)
, short (2 byte signed with NAs), ushort (2 byte unsigned)
, single (4 byte float with NAs).
For example 'quad' allows efficient storage of genomic data as an 'A','T','G','C' factor. The unsigned types support 'circular' arithmetic.

P.S. If you are interested in ff 2.0 you might want to visit our presentation August 5th at JSM "High-Performance Processing of Large Data Sets via Memory Mapping: A Case Study in R And C++" or the official package presentation at UseR!2008 in Dortmund scheduled for August 13th.

The ff authors
Daniel Adler <dadler_at_uni-goettingen.de>
Christian Gläser <christian_glaeser_at_gmx.de> Oleg Nenadic <onenadi_at_uni-goettingen.de> Jens Oehlschlägel <Jens.Oehlschlaegel_at_truecluster.com> Walter Zucchini <wzucchi_at_uni-goettingen.de>



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