.png){kind=icon}
返回
Exploiting domain-specific properties: Compiling parallel dynamic neural network algorithms into efficient code
.png){kind=icon}
PRE
AI
DOI：10.1109/71.809571.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Domain-specific constraints can be exploited to implement compiler optimizations that are not otherwise feasible. Compilers for neural network learning algorithms can achieve near-optimal colocality of data and processes and near-optimal balancing of load over processors, even for dynamically irregular problems. This is impossible for general programs, but restricting programs to the neural algorithm domain allows for the exploitation of domain-specific properties. The operations performed by neural algorithms are broadcasts, reductions, and object-local operations only; the load distribution is regular with respect to the (perhaps irregular) network topology; changes of network topology occur only from time to time. A language, compilation techniques, and a compiler implementation on the MasPar MP-1 are described and quantitative results for the effects of various optimizations used in the compiler are shown. Conservative experiments with weight pruning algorithms yield performance improvements of 27 percent due to load balancing and 195 percent improvement is achieved due to data locality, both compared to unoptimized versions. Two other optimizations-connection allocation and selecting the number of replicates-speed programs up by about 50 percent and 100 percent, respectively. This work can be viewed as a case study in exploiting domain-specific information; some of the principles presented here may apply to other domains as well.
Keyword:
compiler optimizations
high-level parallel language
irregular problems
dynamic data structures
communication optimization
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
6
论文数:
5.2K
被引数:
1.1W
机构
暂无机构信息
引用论文
.png){kind=icon}
分享收藏
查看更多内容