.png){kind=icon}
返回
Sampled Simulation of Task-Based Programs
.png){kind=icon}
OA
AI
DOI：10.1109/TC.2018.2860012.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Sampled simulation is a mature technique for reducing simulation time of single-threaded programs. Nevertheless, current sampling techniques do not take advantage of other execution models, like task-based execution, to provide both more accurate and faster simulation. Recent multi-threaded sampling techniques assume that the workload assigned to each thread does not change across multiple executions of a program. This assumption does not hold for dynamically scheduled task-based programming models. Task-based programming models allow the programmer to specify program segments as tasks which are instantiated many times and scheduled dynamically to available threads. Due to variation in scheduling decisions, two consecutive executions on the same machine typically result in different instruction streams processed by each thread. In this paper, we propose TaskPoint, a sampled simulation technique for dynamically scheduled task-based programs. We leverage task instances as sampling units and simulate only a fraction of all task instances in detail. Between detailed simulation intervals, we employ a novel fast-forwarding mechanism for dynamically scheduled programs. We evaluate different automatic techniques for clustering task instances and show that DBSCAN clustering combined with analytical performance modeling provides the best trade-off of simulation speed and accuracy. TaskPoint is the first technique combining sampled simulation and analytical modeling and provides a new way to trade off simulation speed and accuracy. Compared to detailed simulation, TaskPoint accelerates architectural simulation with 8 simulated threads by an average factor of 220x at an average error of 0.5 percent and a maximum error of 7.9 percent.
Keyword:
Sampled simulation
task-based
analytical performance modeling
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:
3.8
论文数:
5.4K
被引数:
9.8K
机构
引用论文
A Hybrid Prediction Model for Solar Radiation Based on Long Short-Term Memory, Empirical Mode Decomposition, and Solar Profiles for Energy Harvesting Wireless Sensor Networks.png)
EnergiesOAAI
EnergiesIF0
OAAI分享收藏
查看更多内容