Viktor Rosenfeld, Sebastian Breß, Steffen Zeuch, Tilmann Rabl, and Volker Markl
Our paper "Performance Analysis and Automatic Tuning of Hash Aggregation on GPUs" has been accepted for the DaMoN Workshop at SIGMOD Conference 2019.
Abstract
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 21× slower on other GPUs. Given this hardware dependency, we present an algorithm to optimize execution parameters at run-time. On average, our algorithm converges on a result in less than 1% of the time required for a full evaluation of the search space. In this time, it finds execution parameters that are at most 1% slower than the optimum in 90% of our experiments. In the worst case, our algorithm finds execution parameters that are at most 1.29× slower than the optimum.
