Standard
APL on GPUs : a TAIL from the Past, scribbled in Futhark. / Henriksen, Troels; Dybdal, Martin; Urms, Henrik; Kiehn, Anna Sofie; Gavin, Daniel; Abelskov, Hjalte; Elsman, Martin; Oancea, Cosmin Eugen.
Proceedings of the 5th International Workshop on Functional High-Performance Computing. Association for Computing Machinery, 2016. s. 38-43.
Publikation: Bidrag til bog/antologi/rapport › Konferencebidrag i proceedings › Forskning › fagfællebedømt
Harvard
Henriksen, T, Dybdal, M, Urms, H, Kiehn, AS, Gavin, D, Abelskov, H
, Elsman, M & Oancea, CE 2016,
APL on GPUs: a TAIL from the Past, scribbled in Futhark. i
Proceedings of the 5th International Workshop on Functional High-Performance Computing. Association for Computing Machinery, s. 38-43, International Workshop on Functional High-Performance Computing, Nara, Japan,
22/09/2016.
https://doi.org/10.1145/2975991.2975997
APA
Henriksen, T., Dybdal, M., Urms, H., Kiehn, A. S., Gavin, D., Abelskov, H.
, Elsman, M., & Oancea, C. E. (2016).
APL on GPUs: a TAIL from the Past, scribbled in Futhark. I
Proceedings of the 5th International Workshop on Functional High-Performance Computing (s. 38-43). Association for Computing Machinery.
https://doi.org/10.1145/2975991.2975997
Vancouver
Henriksen T, Dybdal M, Urms H, Kiehn AS, Gavin D, Abelskov H o.a.
APL on GPUs: a TAIL from the Past, scribbled in Futhark. I Proceedings of the 5th International Workshop on Functional High-Performance Computing. Association for Computing Machinery. 2016. s. 38-43
https://doi.org/10.1145/2975991.2975997
Author
Henriksen, Troels ; Dybdal, Martin ; Urms, Henrik ; Kiehn, Anna Sofie ; Gavin, Daniel ; Abelskov, Hjalte ; Elsman, Martin ; Oancea, Cosmin Eugen. / APL on GPUs : a TAIL from the Past, scribbled in Futhark. Proceedings of the 5th International Workshop on Functional High-Performance Computing. Association for Computing Machinery, 2016. s. 38-43
Bibtex
@inproceedings{d3b0c8b14f8b46208b1c34bed2670c5d,
title = "APL on GPUs: a TAIL from the Past, scribbled in Futhark",
abstract = "This paper demonstrates translation schemes by which programs written in a functional subset of APL can be compiled to code that is run efficiently on general purpose graphical processing units (GPGPUs). Furthermore, the generated programs can be straight-forwardly interoperated with mainstream programming environments, such as Python, for example for purposes of visualization and user interaction. Finally, empirical evaluation shows that the GPGPU translation achieves speedups up to hundreds of times faster than sequential C compiled code.",
author = "Troels Henriksen and Martin Dybdal and Henrik Urms and Kiehn, {Anna Sofie} and Daniel Gavin and Hjalte Abelskov and Martin Elsman and Oancea, {Cosmin Eugen}",
year = "2016",
doi = "10.1145/2975991.2975997",
language = "English",
pages = "38--43",
booktitle = "Proceedings of the 5th International Workshop on Functional High-Performance Computing",
publisher = "Association for Computing Machinery",
note = "null ; Conference date: 22-09-2016 Through 22-09-2016",
url = "https://sites.google.com/site/fhpcworkshops/",
}
RIS
TY - GEN
T1 - APL on GPUs
AU - Henriksen, Troels
AU - Dybdal, Martin
AU - Urms, Henrik
AU - Kiehn, Anna Sofie
AU - Gavin, Daniel
AU - Abelskov, Hjalte
AU - Elsman, Martin
AU - Oancea, Cosmin Eugen
N1 - Conference code: 5
PY - 2016
Y1 - 2016
N2 - This paper demonstrates translation schemes by which programs written in a functional subset of APL can be compiled to code that is run efficiently on general purpose graphical processing units (GPGPUs). Furthermore, the generated programs can be straight-forwardly interoperated with mainstream programming environments, such as Python, for example for purposes of visualization and user interaction. Finally, empirical evaluation shows that the GPGPU translation achieves speedups up to hundreds of times faster than sequential C compiled code.
AB - This paper demonstrates translation schemes by which programs written in a functional subset of APL can be compiled to code that is run efficiently on general purpose graphical processing units (GPGPUs). Furthermore, the generated programs can be straight-forwardly interoperated with mainstream programming environments, such as Python, for example for purposes of visualization and user interaction. Finally, empirical evaluation shows that the GPGPU translation achieves speedups up to hundreds of times faster than sequential C compiled code.
U2 - 10.1145/2975991.2975997
DO - 10.1145/2975991.2975997
M3 - Article in proceedings
SP - 38
EP - 43
BT - Proceedings of the 5th International Workshop on Functional High-Performance Computing
PB - Association for Computing Machinery
Y2 - 22 September 2016 through 22 September 2016
ER -
