High Bandwidth Memory on FPGAs: A Data Analytics Perspective
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Date
2020
Publication Type
Conference Paper
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yes
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Abstract
FPGA-based data processing in datacenters is increasing in popularity due to the demands of modern workloads and the resulting need for specialization in hardware. Driven by this trend, vendors are rapidly adapting reconfigurable devices to suit data and compute intensive workloads. Inclusion of High Bandwidth Memory (HBM) in FPGA devices is a recent example. HBM promises overcoming the bandwidth bottleneck, often faced by FPGA-based accelerators due to their throughput oriented design. In this paper, we study the usage and benefits of HBM on FPGAs from a data analytics perspective. We consider three workloads that are often performed in analytics oriented databases and implement them on FPGA showing in which cases they benefit from HBM: range selection, hash join, and stochastic gradient descent for linear model training. We integrate our designs into a columnar database (MonetDB) and show the trade-offs arising from the integration related to data movement and partitioning. In certain cases, FPGA+HBM based solutions are able to surpass the highest performance provided by either a 2-socket POWER9 system or a 14-core XeonE5 by up to 1.8x (selection), 12.9x (join), and 3.2x (SGD).
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Publication status
published
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Editor
Book title
2020 30th International Conference on Field-Programmable Logic and Applications (FPL)
Journal / series
Volume
Pages / Article No.
1 - 8
Publisher
IEEE
Event
30th International Conference on Field-Programmable Logic and Applications (FPL 2020) (virtual)
Edition / version
Methods
Software
Geographic location
Date collected
Date created
Subject
High Bandwidth Memory (HBM); FPGA; Database; Advanced analytics
Organisational unit
03506 - Alonso, Gustavo / Alonso, Gustavo
Notes
Due to the Coronavirus (COVID-19) the conference was conducted virtually.