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Accelerating In-Memory Database Selections Using Latency Masking Hardware Threads

Authors:

Prerna Budhkar,

Ildar Absalyamov,

Vasileios Zois,

Walid A. Najjar,

Vassilis J. TsotrasAuthors Info & Claims

ACM Transactions on Architecture and Code Optimization (TACO), Volume 16, Issue 2

Article No.: 13, Pages 1 - 28

https://doi.org/10.1145/3310229

Published: 09 April 2019 Publication History

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Abstract

Inexpensive DRAMs have created new opportunities for in-memory data analytics. However, the major bottleneck in such systems is high memory access latency. Traditionally, this problem is solved with large cache hierarchies that only benefit regular applications. Alternatively, many data-intensive applications exhibit irregular behavior. Hardware multithreading can better cope with high latency seen in such applications. This article implements a multithreaded prototype (MTP) on FPGAs for the relational selection operator that exhibits control flow irregularity. On a standard TPC-H query evaluation, MTP achieves a bandwidth utilization of 83%, while the CPU and the GPU implementations achieve 61% and 64%, respectively. Besides being bandwidth efficient, MTP is also 14.2× and 4.2× more power efficient than CPU and GPU, respectively.

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Cited By

Dadu VNowatzki TFalsafi BFerdman MLu SWenisch T(2022)TaskStream: accelerating task-parallel workloads by recovering program structureProceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3503222.3507706(1-13)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1145/3503222.3507706
Romanous BWindh SAbsalyamov IBudhkar PHalstead RNajjar WTsotras V(2021)Efficient local locking for massively multithreaded in-memory hash-based operatorsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-020-00642-530:3(333-359)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1007/s00778-020-00642-5

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Accelerating In-Memory Database Selections Using Latency Masking Hardware Threads

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cover image ACM Transactions on Architecture and Code Optimization

ACM Transactions on Architecture and Code Optimization Volume 16, Issue 2

June 2019

317 pages

ISSN:1544-3566

EISSN:1544-3973

DOI:10.1145/3325131

Editor:
Koen De Bosschere
Ghent University, Belgium

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Publication History

Published: 09 April 2019

Accepted: 01 January 2019

Revised: 01 January 2019

Received: 01 May 2018

Published in TACO Volume 16, Issue 2

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Dadu VNowatzki TFalsafi BFerdman MLu SWenisch T(2022)TaskStream: accelerating task-parallel workloads by recovering program structureProceedings of the 27th ACM International Conference on Architectural Support for Programming Languages and Operating Systems10.1145/3503222.3507706(1-13)Online publication date: 28-Feb-2022
https://dl.acm.org/doi/10.1145/3503222.3507706
Romanous BWindh SAbsalyamov IBudhkar PHalstead RNajjar WTsotras V(2021)Efficient local locking for massively multithreaded in-memory hash-based operatorsThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-020-00642-530:3(333-359)Online publication date: 1-May-2021
https://dl.acm.org/doi/10.1007/s00778-020-00642-5

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