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L. Friedel

The Paper "Performance Analysis and Automatic Tuning of Hash Aggregation on GPUs" was Accepted at the DaMoN Workshop, Co-Located with the SIGMOD '19

Performance Analysis and Automatic Tuning of Hash Aggregation on GPUs, Viktor Rosenfeld, Sebastian Breß, Steffen Zeuch, Tilmann Rabl, and Volker Markl . 2019. In Proceedings of the Data Management on New Hardware workshop at the ACM SIGMOD (DaMoN '19). ACM, New York, NY, USA.

Hash aggregation is an important data processing primitive which can be significantly accelerated by modern graphics processors (GPUs). Previous work derived heuristics for GPU-accelerated hash aggregation from the study of a particular GPU. In this paper, we examine the influence of different execution parameters on GPU-accelerated hash aggregation on four NVIDIA and two AMD GPUs based on six different microarchitectures. While we are able to replicate some of the previous results, our main finding is that optimal execution parameters are highly GPU-dependent. Most importantly, execution parameters optimized for a specific GPU are up to 21x slower on other GPUs. Given this hardware dependency, we present an algorithm to optimize execution parameters at runtime. On GPUs with low runtime variation, our algorithm finds execution parameters that are less than 4% slower than the optimum on average and less than 18% slower in the worst case.

A preprint-version is available here.

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