Compressed Number Representations for High-Performance Computing
DescriptionThrough simulation, observation, and experiments, far more data is being generated today than can reasonably be stored to disk and later analyzed without any form of data reduction. Moreover, with deepening memory hierarchies, dwindling per-core memory bandwidth, and increasing heterogeneity, even on-node data movement between memory and registers makes for a significant performance bottleneck and primary source of power consumption. Hence, it is becoming increasingly important that the bits being moved and stored in numerical computations are free of redundancy and represent valuable information rather than error.

This talk gives an overview of zfp, a compressed number representation and multi-dimensional array container that mitigates the challenges of data movement using high-speed, lossy (but optionally error-bounded) compression. zfp reduces I/O time and off-line storage by 1-2 orders of magnitude depending on accuracy requirements, as dictated by user-set error tolerances. Unique among data compressors, zfp also supports constant-time read/write random access to individual array elements from compressed storage. zfp's compressed arrays appear to the user like conventional uncompressed arrays and can often be integrated into existing applications with minimal code changes. When used in numerical computations, zfp arrays provide a fine-grained knob on precision while achieving accuracy comparable to IEEE floating point at half the storage or less, reducing both memory footprint and bandwidth. Several application use cases are presented that demonstrate reduced storage, increased accuracy, and improved performance.
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TimeSunday, 13 November 20221:30pm - 2:30pm CST
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