Pushing Big Data into Accelerators: Can the JVM Saturate Our Hardware?

Research output: Chapter in Book/Conference proceedings/Edited volumeConference contributionScientificpeer-review

3 Citations (Scopus)

Abstract

Advancements in the field of big data have led into an increasing interest in accelerator-based computing as a solution for computationally intensive problems. However, many prevalent big data frameworks are built and run on top of the Java Virtual Machine (JVM), which does not explicitly offer support for accelerated computing with e.g. GPGPU or FPGA. One major challenge in combining JVM-based big data frameworks with accelerators is transferring data from objects that reside in JVM managed memory to the accelerator. In this paper, a rigorous analysis of possible solutions is presented to address this challenge. Furthermore, a tool is presented which generates the required code for four alternative solutions and measures the attainable data transfer speed, given a specific object graph. This can give researchers and designers a fast insight about whether the interface between JVM and accelerator can saturate the computational resources of their accelerator. The benchmarking tool was run on a POWER8 system, for which results show that depending on the size of the objects and collections size, an approach based on the Java Native Interface can achieve between 0.9 and 12 GB/s, ByteBuffers can achieve between 0.7 and 3.3 GB/s, the Unsafe library can achieve between 0.8 and 16 GB/s and finally an approach access the data directly can achieve between 3 and 67 GB/s. From our measurements, we conclude that the HotSpot VM does not yet have standardized interfaces by design that can saturate common bandwidths to accelerators seen today or in the future, although one of the approaches presented in this paper can overcome this limitation.
Original languageEnglish
Title of host publicationHigh Performance Computing
Subtitle of host publicationISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS, Revised Selected Papers
EditorsJ.M. Kunkel, R. Yokota, M. Taufer, J. Shalf
Place of PublicationCham
PublisherSpringer
Pages220-236
Number of pages16
ISBN (Electronic)978-3-319-67630-2
ISBN (Print)978-3-319-67629-6
DOIs
Publication statusPublished - 2017
EventISC High Performance 2017 International Conference on High Performance Computing: ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS - Frankfurt, Germany
Duration: 18 Jun 201726 Jun 2017

Publication series

NameLecture Notes in Computer Science
PublisherSpringer International Publishing AG
Volume10524
ISSN (Print)0302-9743

Conference

ConferenceISC High Performance 2017 International Conference on High Performance Computing
CountryGermany
CityFrankfurt
Period18/06/1726/06/17

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    Peltenburg, J. W., Hesam, A., & Al-Ars, Z. (2017). Pushing Big Data into Accelerators: Can the JVM Saturate Our Hardware? In J. M. Kunkel, R. Yokota, M. Taufer, & J. Shalf (Eds.), High Performance Computing: ISC High Performance 2017 International Workshops, DRBSD, ExaComm, HCPM, HPC-IODC, IWOPH, IXPUG, P^3MA, VHPC, Visualization at Scale, WOPSSS, Revised Selected Papers (pp. 220-236). (Lecture Notes in Computer Science; Vol. 10524). Springer. https://doi.org/10.1007/978-3-319-67630-2_18