FusionCL: a machine-learning based approach for OpenCL kernel fusion to increase system performance-Reference-Cited by-同舟云学术

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FusionCL: a machine-learning based approach for OpenCL kernel fusion to increase system performance

Published:2021-06-03 Issue:10 Volume:103 Page:2171-2202
ISSN:0010-485X
Container-title:Computing
language:en
Short-container-title:Computing

Author:

Khalid Yasir Noman,Aleem Muhammad^ORCID,Ahmed Usman,Prodan Radu,Islam Muhammad Arshad,Iqbal Muhammad Azhar

Publisher

Springer Science and Business Media LLC

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Numerical Analysis,Theoretical Computer Science,Software

Link

https://link.springer.com/content/pdf/10.1007/s00607-021-00958-2.pdf

Reference46 articles.

1. Rausch T, Rashed R, Dustdar S (2021) Optimized container scheduling for data-intensive serverless edge computing. Futur Gener Comput Syst 114:259–271. https://doi.org/10.1016/j.future.2020.07.017

2. Khalid YN, Aleem M, Ahmed U, Islam MA, Iqbal MA (2019) Troodon: a machine-learning based load-balancing application scheduler for CPU–GPU system. J Parallel Distrib Comput 132:79–94. https://doi.org/10.1016/J.JPDC.2019.05.015

3. Rohr D et al (2014) An energy-efficient multi-GPU supercomputer. In: 2014 IEEE international conference on high performance computing and communications, 2014 IEEE 6th international symposium on cyberspace safety and security, 2014 IEEE 11th international conference on embedded software and system (HPCC, CSS, ICESS). pp 42–45. https://doi.org/10.1109/HPCC.2014.14

4. Jog A et al (2015) Anatomy of GPU memory system for multi-application execution. In: Proceedings of the 2015 international symposium on memory systems. pp 223–234. https://www.cs.utexas.edu/~skeckler/pubs/MEMSYS_2015_Anatomy.pdf. Accessed 31 Jul 2019

5. Papadimitriou M, Markou E, Fumero J, Stratikopoulos A, Blanaru F, Kotselidis C (2021) Multiple-tasks on multiple-devices (MTMD): exploiting concurrency in heterogeneous managed runtimes. In: Proceedings of the 17th ACM SIGPLAN/SIGOPS international conference on virtual execution environments. pp 125–138. https://doi.org/10.1145/3453933.3454019

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