Name
Abstract | ||
|---|---|---|
The high parallelism feature of scientific applications makes SIMD very suitable for streaming dataflow architectures. However, the splitting of SIMD memory requests increases the messages in on-chip networks and decreases the efficiency of streaming dataflow architectures. To process SIMD memory requests without splitting, a memory partition mechanism is proposed for SIMD in streaming dataflow architectures. The mechanism partitions input data of scientific applications into N (SIMD width) independent sub-spaces equally and groups operations on different data with same locations in N sub-spaces into SIMD operation. Moreover, the mechanism merges data with the same location in different sub-spaces to SIMD data and stores SIMD data in same SPMs to process SIMD memory requests as a whole. Simulation experimental results show that the proposed mechanism improves the performance per watt of streaming dataflow architectures by 2.5× on average on typical scientific applications. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.1109/IGCC.2016.7892619 | 2016 Seventh International Green and Sustainable Computing Conference (IGSC) |
Keywords | Field | DocType |
memory partition,SIMD,streaming dataflow,scientific applications,high-performance computing | Computer architecture,System on a chip,Data transmission,Computer science,Parallel computing,SIMD,Dataflow,Memory management,Performance per watt,Partition (number theory) | Conference |
ISBN | Citations | PageRank |
978-1-5090-5118-2 | 1 | 0.36 |
References | Authors | |
9 | 7 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Xiaowei Shen | 1 | 23 | 2.59 |
| Xiaochun Ye | 2 | 125 | 28.41 |
| Xu Tan | 3 | 25 | 3.93 |
| Da Wang | 4 | 9 | 5.62 |
| Zhimin Zhang | 5 | 54 | 11.10 |
| Zhimin Tang | 6 | 234 | 22.55 |
| FAN Dong-Rui | 7 | 222 | 38.18 |