Data-centric Combinatorial Optimization of Parallel Code

被引：0
|
作者：
Luo, Hao [1 ]
Chen, Guoyang [2 ]
Li, Pengcheng [1 ]
Ding, Chen [1 ]
Shen, Xipeng [2 ]
机构：
[1] Univ Rochester, Rochester, NY 14627 USA
[2] North Carolina State Univ, Raleigh, NC 27695 USA
来源：
ACM SIGPLAN NOTICES | 2016年 / 51卷 / 08期
基金：
美国国家科学基金会; 中国国家自然科学基金;
关键词：
Locality metrics; Locality modeling; Footprint;
D O I：
10.1145/2851141.2851182
中图分类号：
TP31 [计算机软件];
学科分类号：
081202 ; 0835 ;
摘要：
Memory performance is one essential factor for tapping into the full potential of the massive parallelism of GPU. It has motivated some recent efforts in GPU cache modeling. This paper presents a new data-centric way to model the performance of a system with heterogeneous memory resources. The new model is composable, meaning it can predict the performance difference due to placing data differently by profiling the execution just once.
引用
收藏
页码：379 / 380
页数：2
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