SIGSIM-PADS’18 Paper: Parallel Application Performance Prediction

Parallel Application Performance Prediction Using Analysis Based Models and HPC Simulations, Mohammad Abu Obaida, Jason Liu, Gopinath Chennupati, Nandakishore Santhi, and Stephan Eidenbenz. In Proceedings of the 2018 SIGSIM Principles of Advanced Discrete Simulation (SIGSIM-PADS’18), May 2018. [paper]

Abstract

Parallel application performance models provide valuable insight about the performance in real systems. Capable tools providing fast, accurate, and comprehensive prediction and evaluation of high-performance computing (HPC) applications and system architectures have important value. This paper presents PyPassT, an analysis based modeling framework built on static program analysis and integrated simulation of target HPC architectures. More specifically, the framework analyzes application source code written in C with OpenACC directives and transforms it into an application model describing its computation and communication behavior (including CPU and GPU workloads, memory accesses, and message-passing transactions). The application model is then executed on a simulated HPC architecture for performance analysis. Preliminary experiments demonstrate that the proposed framework can represent the runtime behavior of benchmark applications with good accuracy.

Bibtex

@inproceedings{pads18-hpcpred,
title = {Parallel Application Performance Prediction Using Analysis Based Models and HPC Simulations},
author = {Mohammad Abu Obaida and Jason Liu and Gopinath Chennupati and Nandakishore Santhi and Stephan Eidenbenz},
booktitle = {Proceedings of the 2018 SIGSIM Principles of Advanced Discrete Simulation (SIGSIM-PADS’18)},
pages = {49--59},
month = {May},
year = {2018},
doi = {10.1145/3200921.3200937}
}

Slides

Invited Talk: Hybrid Network Modeling and Simulation for Scale

Hybrid Network Modeling and Simulation for Scale (大规模网络混合建模与仿真技术)

2nd Global Future Network Development Summit
第二届全球未来网络发展峰会
Nanjing, China
May 11-12, 2018

Abstract

Modeling and simulation (M&S) plays an important role in the design analysis and performance evaluation of complex systems. Many of these systems, such as computer networks, involve a large number of interrelated components and processes. Complex behaviors emerge as these components and processes inter-operate across multiple scales at various granularities. M&S must be able to provide sufficiently accurate results while coping with the scale and complexity. This talk will focus on some novel techniques in high-performance network modeling and simulation. One is hybrid network traffic modeling, which can offload the computationally intensive bulk traffic calculations to the background onto GPU, while leaving detailed simulation of network transactions in the foreground on CPU. The other is distributed network emulation with simulation symbiosis, which uses an abstract network model to coordinate distributed emulation instances with superimposed traffic model to represent large-scale network scenarios.

中文摘要:建模与模拟(Modeling and Simulation, M&S)在复杂系统的设计分析与性能评估中发挥着重要作用。这些系统中的很多(如计算机网络)涉及大量相关的组件和流程。随着这些组件和流程在不同粒度的多个尺度上进行交互操作,复杂行为就会相应出现。 M&S必须能够在应对规模和复杂性的同时提供足够准确的结果。这次演讲将集中讨论高性能网络建模和模拟中的一些新技术。一种是混合网络流量建模,它可以将计算密集型的大量流量计算转移到GPU后台上,同时在CPU前台留下网络事务的详细模拟。另一种是采用与模拟协同的分布式网络仿真,它采用抽象网络模型、通过叠加的流量模型来协调分布式仿真实例,以表示大规模的网络场景。