Please use this identifier to cite or link to this item: https://doi.org/10.1145/2145816.2145818
Title: Scalable framework for mapping streaming applications onto multi-GPU systems
Authors: Huynh, H.P.
Hagiescu, A.
Wong, W.-F. 
Goh, R.S.M.
Issue Date: 2012
Source: Huynh, H.P.,Hagiescu, A.,Wong, W.-F.,Goh, R.S.M. (2012). Scalable framework for mapping streaming applications onto multi-GPU systems. Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP : 1-10. ScholarBank@NUS Repository. https://doi.org/10.1145/2145816.2145818
Abstract: Graphics processing units leverage on a large array of parallel processing cores to boost the performance of a specific streaming computation pattern frequently found in graphics applications. Unfortunately, while many other general purpose applications do exhibit the required streaming behavior, they also possess unfavorable data layout and poor computation-to-communication ratios that penalize any straight-forward execution on the GPU. In this paper we describe an efficient and scalable code generation framework that can map general purpose streaming applications onto a multi-GPU system. This framework spans the entire core and memory hierarchy exposed by the multi-GPU system. Several key features in our framework ensure the scalability required by complex streaming applications. First, we propose an efficient stream graph partitioning algorithm that partitions the complex application to achieve the best performance under a given shared memory constraint. Next, the resulting partitions are mapped to multiple GPUs using an efficient architecture-driven strategy. The mapping balances the workload while considering the communication overhead. Finally, a highly effective pipeline execution is employed for the execution of the partitions on the multi-GPU system. The framework has been implemented as a back-end of the StreamIt programming language compiler. Our comprehensive experiments show its scalability and significant performance speedup compared with a previous stateof- the-art solution. Copyright © 2012 ACM.
Source Title: Proceedings of the ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPOPP
URI: http://scholarbank.nus.edu.sg/handle/10635/41995
ISBN: 9781450311601
DOI: 10.1145/2145816.2145818
Appears in Collections:Staff Publications

Show full item record
Files in This Item:
There are no files associated with this item.

SCOPUSTM   
Citations

11
checked on Dec 5, 2017

Page view(s)

47
checked on Dec 9, 2017

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.