FineStream: Fine-Grained Window-Based Stream Processing on CPU-GPU Integrated Architectures


Feng Zhang and Lin Yang, Renmin University of China; Shuhao Zhang, Technische Universität Berlin and National University of Singapore; Bingsheng He, National University of Singapore; Wei Lu and Xiaoyong Du, Renmin University of China


Accelerating SQL queries on stream processing by utilizing heterogeneous coprocessors, such as GPUs, has shown to be an effective approach. Most works show that heterogeneous processors bring significant performance improvement because of their high parallelism and computation capacity. However, the discrete memory architectures with relatively low PCI-e bandwidth and high latency have dragged down the benefits of heterogeneous coprocessors. Recently, hardware vendors propose CPU-GPU integrated architectures that integrate CPU and GPU on the same chip. This integration provides new opportunities for fine-grained cooperation be-tween CPU and GPU for optimizing SQL queries on stream processing. In this paper, we propose a data stream system, called FineStream, for efficient window-based stream pro-cessing on integrated architectures. Particularly, FineStreamperforms fine-grained workload scheduling between CPU and GPU to take advantage of both architectures, and also targets at dynamic stream query co-processing with window handling. Our experimental results show that 1) on integrated architectures, FineStream achieves an average 52% throughput improvement and 36% lower latency over the state-of-the-art stream processing engine; 2) compared to the stream processing engine on the discrete architecture, FineStream on the integrated architecture achieves 10.4x price-throughput ratio, 1.8x energy efficiency, and can enjoy lower latency benefits.

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@inproceedings {254469,
author = {Feng Zhang and Lin Yang and Shuhao Zhang and Bingsheng He and Wei Lu and Xiaoyong Du},
title = {FineStream: Fine-Grained Window-Based Stream Processing on CPU-GPU Integrated Architectures},
booktitle = {2020 {USENIX} Annual Technical Conference ({USENIX} {ATC} 20)},
year = {2020},
isbn = {978-1-939133-14-4},
pages = {633--647},
url = {},
publisher = {{USENIX} Association},
month = jul,

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