Electrical & Computer Engineering Research Works
Permanent URI for this collectionhttp://hdl.handle.net/1903/1658
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Item Empirical Speedup Study of Truly Parallel Data Compression(2013-04-20) Edwards, James A.; Vishkin, UziWe present an empirical study of novel work-optimal parallel algorithms for Burrows-Wheeler compression and decompression of strings over a constant alphabet. To validate these theoretical algorithms, we implement them on the experimental XMT computing platform developed especially for supporting parallel algorithms at the University of Maryland. We show speedups of up to 25x for compression, and 13x for decompression, versus bzip2, the de facto standard implementation of Burrows-Wheeler compression. Unlike existing approaches, which assign an entire (e.g., 900KB) block to a processor that processes the block serially, our approach is “truly parallel” as it processes in parallel the entire input. Besides the theoretical interest in solving the “right” problem, the importance of data compression speed for small inputs even at great expense of quality (compressed size of data) is demonstrated by the introduction of Google’s Snappy for MapReduce. Perhaps surprisingly, we show feasibility of holding on to quality, while even beating Snappy on speed. In turn, this work adds new evidence in support of the XMT/PRAM thesis: that an XMT-like many-core hardware/ software platform may be necessary for enabling general-purpose parallel computing. Comparison of our results to recently published work suggests 70x improvement over what current commercial parallel hardware can achieve.Item Parallel Algorithms for Burrows-Wheeler Compression and Decompression(2012-11-12) Edwards, James A.; Vishkin, UziWe present work-optimal PRAM algorithms for Burrows-Wheeler compression and decompression of strings over a constant alphabet. For a string of length n, the depth of the compression algorithm is O(log2 n), and the depth of the the corresponding decompression algorithm is O(log n). These appear to be the first polylogarithmic-time work-optimal parallel algorithms for any standard lossless compression scheme. The algorithms for the individual stages of compression and decompression may also be of independent interest: 1. a novel O(log n)-time, O(n)-work PRAM algorithm for Huffman decoding; 2. original insights into the stages of the BW compression and decompression problems, bringing out parallelism that was not readily apparent, allowing them to be mapped to elementary parallel routines that have O(log n)-time, O(n)-work solutions, such as: (i) prefix-sums problems with an appropriately-defined associative binary operator for several stages, and (ii) list ranking for the final stage of decompression.