Paper Info
Title
Evaluating Multiple Streams on Heterogeneous Platforms.
Abstract
Using multiple streams can improve the overall system performance by mitigating the data transfer overhead on heterogeneous systems. Prior work focuses a lot on GPUs but little is known about the performance impact on (Intel Xeon) Phi. In this work, we apply multiple streams into six real-world applications on Phi. We then systematically evaluate the performance benefits of using multiple streams. The evaluation work is performed at two levels: the microbenchmarking level and the real-world application level. Our experimental results at the microbenchmark level show that data transfers and kernel execution can be overlapped on Phi, while data transfers in both directions are performed in a serial manner. At the real-world application level, we show that both overlappable and non-overlappable applications can benefit from using multiple streams (with an performance improvement of up to 24%). We also quantify how task granularity and resource granularity impact the overall performance. Finally, we present a set of heuristics to reduce the search space when determining a proper task granularity and resource granularity. To conclude, our evaluation work provides lots of insights for runtime and architecture designers when using multiple streams on Phi.
Year
DOI
Venue
2016
10.1142/S0129626416400028
PARALLEL PROCESSING LETTERS
Keywords
Field
DocType
Performance evaluation,multiple streams,resource partitioning,pipelining
Kernel (linear algebra),Pipeline (computing),Data transmission,Computer science,Parallel computing,Xeon,Granularity,STREAMS,Performance improvement,Distributed computing
Journal
Volume
Issue
ISSN
26
SP4
0129-6264
Citations 
PageRank 
References 
2
0.36
0
Authors
7
Name
Order
Citations
PageRank
Jianbin Fang126525.31
Peng Zhang261.47
Zhaokui Li330.70
Tao Tang4427.44
Xuhao Chen5407.43
Cheng Chen620.70
Canqun Yang718829.39