Boosting Java Performance Using GPGPUsCitation formats
Standard
Boosting Java Performance Using GPGPUs. / Clarkson, James; Kotselidis, Christos; Brown, Gavin; Luján, Mikel.
Architecture of Computing Systems - ARCS 2017: 30th International Conference, Vienna, Austria, April 3--6, 2017, Proceedings. ed. / Jens Knoop; Wolfgang Karl; Martin Schulz; Koji Inoue; Thilo Pionteck. Cham : Springer Nature, 2017. p. 59-70 (Lecture Notes in Computer Science; Vol. 10172).
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
Harvard
Clarkson, J
, Kotselidis, C, Brown, G & Luján, M 2017,
Boosting Java Performance Using GPGPUs. in J Knoop, W Karl, M Schulz, K Inoue & T Pionteck (eds),
Architecture of Computing Systems - ARCS 2017: 30th International Conference, Vienna, Austria, April 3--6, 2017, Proceedings. Lecture Notes in Computer Science, vol. 10172, Springer Nature, Cham, pp. 59-70.
https://doi.org/10.1007/978-3-319-54999-6_5APA
Clarkson, J.
, Kotselidis, C., Brown, G., & Luján, M. (2017).
Boosting Java Performance Using GPGPUs. In J. Knoop, W. Karl, M. Schulz, K. Inoue, & T. Pionteck (Eds.),
Architecture of Computing Systems - ARCS 2017: 30th International Conference, Vienna, Austria, April 3--6, 2017, Proceedings (pp. 59-70). (Lecture Notes in Computer Science; Vol. 10172). Springer Nature.
https://doi.org/10.1007/978-3-319-54999-6_5Vancouver
Clarkson J
, Kotselidis C, Brown G, Luján M.
Boosting Java Performance Using GPGPUs. In Knoop J, Karl W, Schulz M, Inoue K, Pionteck T, editors, Architecture of Computing Systems - ARCS 2017: 30th International Conference, Vienna, Austria, April 3--6, 2017, Proceedings. Cham: Springer Nature. 2017. p. 59-70. (Lecture Notes in Computer Science).
https://doi.org/10.1007/978-3-319-54999-6_5Author
Clarkson, James
; Kotselidis, Christos ; Brown, Gavin ; Luján, Mikel. /
Boosting Java Performance Using GPGPUs. Architecture of Computing Systems - ARCS 2017: 30th International Conference, Vienna, Austria, April 3--6, 2017, Proceedings. editor / Jens Knoop ; Wolfgang Karl ; Martin Schulz ; Koji Inoue ; Thilo Pionteck. Cham : Springer Nature, 2017. pp. 59-70 (Lecture Notes in Computer Science).
Bibtex
@inproceedings{d1d2f71c51e542778dbd740cbd48d8bc,
title = "Boosting Java Performance Using GPGPUs",
abstract = "In this paper we describe Jacc, an experimental framework which allows developers to program GPGPUs directly from Java. The goal of Jacc, is to allow developers to benefit from using heterogeneous hardware whilst minimizing the amount of code refactoring required. Jacc utilizes two key abstractions: tasks which encapsulate all the information needed to execute code on a GPGPU; and task graphs which capture both inter-task control-flow and data dependencies. These abstractions enable the Jacc runtime system to automatically choreograph data movement and synchronization between the host and the GPGPU; eliminating the need to explicitly manage disparate memory spaces. We demonstrate the advantages of Jacc, both in terms of programmability and performance, by evaluating it against existing Java frameworks. Experimental results show an average performance speedup of 19x, using NVIDIA Tesla K20m GPU, and a 4x decrease in code complexity when compared with writing multi-threaded Java code across eight evaluated benchmarks.",
author = "James Clarkson and Christos Kotselidis and Gavin Brown and Mikel Luj{\'a}n",
year = "2017",
month = mar,
day = "4",
doi = "10.1007/978-3-319-54999-6_5",
language = "Undefined",
isbn = "978-3-319-54999-6",
series = "Lecture Notes in Computer Science",
publisher = "Springer Nature",
pages = "59--70",
editor = "Jens Knoop and Wolfgang Karl and Martin Schulz and Koji Inoue and Thilo Pionteck",
booktitle = "Architecture of Computing Systems - ARCS 2017: 30th International Conference, Vienna, Austria, April 3--6, 2017, Proceedings",
address = "United States",
}
RIS
TY - GEN
T1 - Boosting Java Performance Using GPGPUs
AU - Clarkson, James
AU - Kotselidis, Christos
AU - Brown, Gavin
AU - Luján, Mikel
PY - 2017/3/4
Y1 - 2017/3/4
N2 - In this paper we describe Jacc, an experimental framework which allows developers to program GPGPUs directly from Java. The goal of Jacc, is to allow developers to benefit from using heterogeneous hardware whilst minimizing the amount of code refactoring required. Jacc utilizes two key abstractions: tasks which encapsulate all the information needed to execute code on a GPGPU; and task graphs which capture both inter-task control-flow and data dependencies. These abstractions enable the Jacc runtime system to automatically choreograph data movement and synchronization between the host and the GPGPU; eliminating the need to explicitly manage disparate memory spaces. We demonstrate the advantages of Jacc, both in terms of programmability and performance, by evaluating it against existing Java frameworks. Experimental results show an average performance speedup of 19x, using NVIDIA Tesla K20m GPU, and a 4x decrease in code complexity when compared with writing multi-threaded Java code across eight evaluated benchmarks.
AB - In this paper we describe Jacc, an experimental framework which allows developers to program GPGPUs directly from Java. The goal of Jacc, is to allow developers to benefit from using heterogeneous hardware whilst minimizing the amount of code refactoring required. Jacc utilizes two key abstractions: tasks which encapsulate all the information needed to execute code on a GPGPU; and task graphs which capture both inter-task control-flow and data dependencies. These abstractions enable the Jacc runtime system to automatically choreograph data movement and synchronization between the host and the GPGPU; eliminating the need to explicitly manage disparate memory spaces. We demonstrate the advantages of Jacc, both in terms of programmability and performance, by evaluating it against existing Java frameworks. Experimental results show an average performance speedup of 19x, using NVIDIA Tesla K20m GPU, and a 4x decrease in code complexity when compared with writing multi-threaded Java code across eight evaluated benchmarks.
U2 - 10.1007/978-3-319-54999-6_5
DO - 10.1007/978-3-319-54999-6_5
M3 - Conference contribution
SN - 978-3-319-54999-6
T3 - Lecture Notes in Computer Science
SP - 59
EP - 70
BT - Architecture of Computing Systems - ARCS 2017: 30th International Conference, Vienna, Austria, April 3--6, 2017, Proceedings
A2 - Knoop, Jens
A2 - Karl, Wolfgang
A2 - Schulz, Martin
A2 - Inoue, Koji
A2 - Pionteck, Thilo
PB - Springer Nature
CY - Cham
ER -
