The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Student: Harvey Dam (University of Utah)
Supervisor: Ganesh Gopalakrishnan (University of Utah)
Abstract: In modern scientific computing and machine learning systems, data movement has overtaken compute as the performance bottleneck, thus motivating the wider adoption of lossy data compression. Unfortunately, state-of-the-art floating-point array compressors such as SZ and ZFP require decompression before operations can be performed on the data. In this work, our contribution is to show that compression methods can be designed to allow efficient operations on compressed arrays without having to first decompress. In particular, compression methods that consist of only linear transformations and quantization allow certain operations on compressed arrays without decompression. We develop such a compression method, called PyBlaz, the first compression method we know that can compress arbitrary-dimensional arrays and directly operate on the compressed representation, with all stages running on GPUs.
In the poster session, I will provide details about each compression step, several compressed-spaced operations, and our ongoing performance and application experiments.
ACM-SRC Semi-Finalist: no
Poster: PDF
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