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Exploring Query Processing on CPU-GPU Integrated Edge Device
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AI
DOI：10.1109/TPDS.2022.3177811.png)
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Abstract
En 中文
Huge amounts of data have been generated on edge devices every day, which requires efficient data analytics and management. However, due to the limited computing capacity of these edge devices, query processing at the edge faces tremendous pressure. Fortunately, in recent years, hardware vendors have integrated heterogeneous coprocessors, such as GPUs, into the edge device, which can provide much more computing power. Furthermore, the CPU-GPU integrated edge device has shown significant benefits in a variety of situations. Therefore, the exploration of query processing on such CPU-GPU integrated edge devices becomes an urgent need. In this article, we develop a fine-grained query processing engine, called FineQuery, which can perform efficient query processing on CPU-GPU integrated edge devices. Particularly, FineQuery can take advantage of both architectural features of edge devices and query characteristics by performing fine-grained workload scheduling between the CPU and the GPU. Experiments show that on TPC-H workloads, FineQuery reduces 42.81% latency and improves 2.39x bandwidth utilization on average compared to the implementation of using only GPU or CPU. Furthermore, query processing at the edge can bring significant performance-per-cost benefits and energy efficiency. On average, FineQuery at the edge brings 21x performance-per-cost ratio and 4x energy efficiency compared with processing the data on a discrete GPU platform.
Keywords:
Graphics processing units
Query processing
Performance evaluation
Computer architecture
Databases
Structured Query Language
Engines
CPU
GPU
integrated architecture
edge device
query processing
Journal
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6
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5.2K
Citations:
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