@inproceedings{84bf835ab1a541fc91442f3589163f86,
title = "Heterogeneous Intra-Pipeline Device-Parallel Aggregations",
abstract = "The rising hardware heterogeneity in modern systems emphasizes new dimensions of optimizing task execution for data processing frameworks. Specialized hardware is often expected to be the exclusive executor of some particular workload because it was designed for it or is simply the fastest option. In heterogeneous database systems, almost always, the entire operation offloading is considered. However, little attention was given to database systems with horizontal cross-device pipeline parallelization. We argue that such an approach can be applied to systems with morsel-driven parallelism and improve performance. We apply our parallelization strategy to an existing system and accelerate aggregations using two devices by up to 1.5x compared to the fastest exclusive device executor.",
keywords = "Query engine, dedicated GPUs, heterogeneous query processing",
author = "Artem Kroviakov and Petr Kurapov and Christoph Anneser and Jana Giceva",
note = "Publisher Copyright: {\textcopyright} 2024 ACM.; 20th International Workshop on Data Management on New Hardware, DaMoN 2024 ; Conference date: 10-06-2024",
year = "2024",
month = jun,
day = "9",
doi = "10.1145/3662010.3663441",
language = "English",
series = "20th International Workshop on Data Management on New Hardware, DaMoN 2024",
publisher = "Association for Computing Machinery, Inc",
booktitle = "20th International Workshop on Data Management on New Hardware, DaMoN 2024",
}