Beyond Straightforward Vectorization of Lightweight Data Compression Algorithms for Larger Vector Sizes
Technische Universitat Dresden, 01062 Dresden, Germany
30th GI-Workshop on Foundations of Databases, 2018
@article{pietrzyk2018beyond,
title={Beyond Straightforward Vectorization of Lightweight Data Compression Algorithms for Larger Vector Sizes},
author={Pietrzyk, Johannes and Ungeth{"u}m, Annett and Habich, Dirk and Lehner, Wolfgang},
year={2018}
}
Data as well as hardware characteristics are two key aspects for efficient data management. This holds in particular for the field of in-memory data processing. Aside from increasing main memory capacities, efficient in-memory processing benefits from novel processing concepts based on lightweight compressed data. Thus, an active research field deals with the adaptation of new hardware features such as vectorization using SIMD instructions to speedup lightweight data compression algorithms. Most of the vectorized implementations have been proposed for 128-bit vector registers. A straightforward transformation to wider vector sizes is possible. However, this straightforward way does not exploit the capabilities of newer SIMD extensions to the maximum extent as we will show in this paper. On the one hand, we present a novel implementation concept for run-length encoding using conflict-detection operations which have been introduced in Intel’s AVX-512 SIMD extension. On the other hand, we investigate different data layouts for vectorization and their impact on wider vector sizes.
July 5, 2018 by hgpu
