The International Conference for High Performance Computing, Networking, Storage, and Analysis
The International Conference for High Performance Computing, Networking, Storage, and Analysis
Authors: Tri Nguyen and Michela Becchi (North Carolina State University)
Abstract: Data transformation tasks - such as encoding, decoding, parsing, and conversion between common data formats - are at the core of many data analytics, data processing and scientific applications. This has led to the development of custom software libraries and hardware implementations targeting popular data transformations. By accelerating specific transformations, however, these solutions suffer from lack of generality. On the other hand, a generic and programmable data processing engine might support a wide range of data transformations, but do so at the cost of reduced performance compared to custom, algorithm-specific solutions.
In this work, we aim to bridge this gap between generality and performance. To this end, we provide a compilation framework that transparently converts data transformation tasks expressed using pushdown transducers into efficient GPU code.
Best Poster Finalist (BP): no
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