Publication
PystachIO: Efficient Distributed GPU Query Processing with PyTorch over Fast Networks textbackslashu0026 Fast Storage
Jigao Luo; Nils Boeschen; Muhammad El-Hindi; Carsten Binnig
In: Proceedings of the VLDB Endowment (PVLDB), Vol. 19, No. 9, Pages 2494-2507, arXiv, 2026.
Abstract
The AI hardware boom has led modern data centers to adopt HPC-
style architectures centered on distributed, GPU-centric computa-
tion. Large GPU clusters interconnected by fast RDMA networks
and backed by high-bandwidth NVMe storage enable scalable com-
putation and rapid access to storage-resident data. Tensor compu-
tation runtimes (TCRs), such as PyTorch, originally designed for
AI workloads, have recently been shown to accelerate analytical
workloads. However, prior work has primarily considered settings
where the data fits in aggregated GPU memory. In this paper, we
systematically study how TCRs can support scalable, distributed
query processing for large-scale, storage-resident OLAP workloads.
Although TCRs provide abstractions for network and storage I/O,
naive use often underutilizes GPU and I/O bandwidth due to insuf-
ficient overlap between computation and data movement. As a core
contribution, we present PystachIO, a prototype of a PyTorch-based
distributed OLAP engine that combines fast network and storage
I/O with key optimizations to maximize GPU, network, and storage
utilization. Our evaluation shows up to 3× end-to-end speedups
over existing distributed GPU-based query processing approaches.