Tag: Simulation

WSC’18 Paper: IMCSim: Parameterized Performance Prediction for Implicit Monte Carlo Codes

IMCSim: Parameterized Performance Prediction for Implicit Monte Carlo Codes, Gopinath Chennupathi, Stephan Eidenbenz, Alex Long, Olena Tkachenko, Joseph Zerr, and Jason Liu. In Proceedings of the 2018 Winter Simulation Conference (WSC 2018), December 2018. (To appear).

Abstract

Monte Carlo techniques to radiation transport play a significant role in modeling complex astrophysical phenomena. In this paper, we design an application model (IMCSim) of an Implicit Monte Carlo (IMC) particle code using the Performance Prediction Toolkit (PPT), a discrete-event simulation-based modeling framework for predicting code performance on a large range of parallel platforms. We present validation results for IMCSim. We then use the fast parameter scanning that such a high-level loop-structure model of a complex code enables to predict optimal IMC parameter settings for interconnect latency hiding. We find that variations in interconnect bandwidth have a significant effect on optimal parameter values. Our results suggest potential value using IMCSim as a pre-step to substantial IMC runs to quickly identify optimal parameter values for the specific hardware platform on which IMC runs.

Bibtex

@inproceedings{imcsim,
title = {IMCSim: Parameterized Performance Prediction for Implicit Monte Carlo Codes},
author = {Chennupathi, Gopinath and Eidenbenz, Stephan and Long, Alex and Tkachenko, Olena and Zerr, Joseph and Liu, Jason},
booktitle = {Proceedings of the 2018 Winter Simulation Conference (WSC 2018)},
month = {December},
year = {2018}
}

HPCC’18 Paper: HPC Demand Response via Power Capping and Node Scaling

Enabling Demand Response for HPC Systems Through Power Capping and Node Scaling, Kishwar Ahmed, Jason Liu, and Kazutomo Yoshii. In Proceedings of the 20th IEEE International Conference on High Performance Computing and Communications (HPCC-2018), June 2018. [to appear]

Abstract

Demand response is an increasingly popular program ensuring power grid stability during a sudden surge in power demand. We expect high-performance computing (HPC) systems to be valued participants in such program for their massive power consumption. In this paper, we propose an emergency demand-response model exploiting both power capping of HPC systems and node scaling of HPC applications. First, we present power and performance prediction models for HPC systems with only power capping, upon which we propose our demand-response model. We validate the models with real-life measurements of application characteristics. Next, we present models to predict energy-to-solution for HPC applications with different numbers of nodes and power-capping values, and we validate the models. Based on the prediction models, we propose an emergency demand response participation model for HPC systems to determine optimal resource allocation based on power capping and node scaling. Finally, we demonstrate the effectiveness of our proposed demand-response model using real-life measurements and trace data. We show that our approach can reduce energy consumption with only a slight increase in the execution time for HPC applications during critical demand response periods.

Bibtex

@inproceedings{hpcc18-power,
title = {Enabling Demand Response for HPC Systems Through Power Capping and Node Scaling},
author = {Kishwar Ahmed and Jason Liu and Kazutomo Yoshii},
booktitle = {Proceedings of the 20th IEEE International Conference on High Performance Computing and Communications (HPCC'18)},
month = {June},
year = {2018}
}

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。

WSC’17 Paper: HPC Job Scheduling Simulation

Simulation of HPC Job Scheduling and Large-Scale Parallel Workloads, Mohammad Abu Obaida and Jason Liu. In Proceedings of the 2017 Winter Simulation Conference (WSC 2017), W. K. V. Chan, A. D’Ambrogio, G. Zacharewicz, N. Mustafee, G. Wainer, and E. Page, eds., December 2017. [paper]

Abstract

The paper presents a simulator designed specifically for evaluating job scheduling algorithms on large-scale HPC systems. The simulator was developed based on the Performance Prediction Toolkit (PPT), which is a parallel discrete-event simulator written in Python for rapid assessment and performance prediction of large-scale scientific applications on supercomputers. The proposed job scheduler simulator incorporates PPT’s application models, and when coupled with the sufficiently detailed architecture models, can represent more realistic job runtime behaviors. Consequently, the simulator can evaluate different job scheduling and task mapping algorithms on the specific target HPC platforms more accurately.

Bibtex

@inproceedings{wsc17-jobsched,
title = {Simulation of HPC Job Scheduling and Large-Scale Parallel Workloads}, 
author = {Mohammad Abu Obaida and Jason Liu},
booktitle = {Proceedings of the 2017 Winter Simulation Conference (WSC 2017)}, 
editor = {W. K. V. Chan and A. D’Ambrogio and G. Zacharewicz and N. Mustafee and G. Wainer and E. Page},
month = {December},
year = {2017}
}

WSC’17 Paper: HPC Simulation History

A Brief History of HPC Simulation and Future Challenges, Kishwar Ahmed, Jason Liu, Abdel-Hameed Badawy, and Stephan Eidenbenz. In Proceedings of the 2017 Winter Simulation Conference (WSC 2017), W. K. V. Chan, A. D’Ambrogio, G. Zacharewicz, N. Mustafee, G. Wainer, and E. Page, eds., December 2017. [paper]

Abstract

High-performance Computing (HPC) systems have gone through many changes during the past two decades in their architectural design to satisfy the increasingly large-scale scientific computing demand. Accurate, fast, and scalable performance models and simulation tools are essential for evaluating alternative architecture design decisions for the massive-scale computing systems. This paper recounts some of the influential work in modeling and simulation for HPC systems and applications, identifies some of the major challenges, and outlines future research directions which we believe are critical to the HPC modeling and simulation community.

Bibtex

@inproceedings{wsc17-history,
title = {A Brief History of HPC Simulation and Future Challenges}, 
author = {Kishwar Ahmed and Jason Liu and Abdel-Hameed Badawy and Stephan Eidenbenz},
booktitle = {Proceedings of the 2017 Winter Simulation Conference (WSC 2017)}, 
editor = {W. K. V. Chan and A. D’Ambrogio and G. Zacharewicz and N. Mustafee and G. Wainer and E. Page},
month = {December},
year = {2017}
}

MASCOTS’17 Paper: Energy Demand Response Scheduling

An Energy Efficient Demand-Response Model for High Performance Computing Systems, Kishwar Ahmed, Jason Liu, and Xingfu Wu. In Proceedings of the 25th IEEE International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2017), September 2017.  [paper]

Abstract

Demand response refers to reducing energy consumption of participating systems in response to transient surge in power demand or other emergency events. Demand response is particularly important for maintaining power grid transmission stability, as well as achieving overall energy saving. High Performance Computing (HPC) systems can be considered as ideal participants for demand-response programs, due to their massive energy demand. However, the potential loss of performance must be weighed against the possible gain in power system stability and energy reduction. In this paper, we explore the opportunity of demand response on HPC systems by proposing a new HPC job scheduling and resource provisioning model. More specifically, the proposed model applies power-bound energy-conservation job scheduling during the critical demand-response events, while maintaining the traditional performance-optimized job scheduling during the normal period. We expect such a model can attract willing articipation of the HPC systems in the demand response programs, as it can improve both power stability and energy saving without significantly compromising application performance. We implement the proposed method in a simulator and compare it with the traditional scheduling approach. Using trace-driven simulation, we demonstrate that the HPC demand response is a viable approach toward power stability and energy savings with only marginal increase in the jobs’ execution time.

Bibtex

@inproceedings{mascots17-energy,
  title={An Energy Efficient Demand-Response Model for High Performance Computing Systems},
  author={Ahmed, Kishwar and Liu, Jason and Wu, Xingfu},
  booktitle={Proceedings of the 25th IEEE International Symposium on Modeling, Analysis, and Simulation of Computer and Telecommunication Systems (MASCOTS 2017)},
  pages={175--186},
  month={September},
  year={2017}
}

Slides

Invited Talk: High-Performance Modeling and Simulation of Computer Networks

High-Performance Modeling and Simulation of Computer Networks

May 26, 2017

Department of Computer Science
Tsinghua University, Beijing, China
Host: Professor Zhihui Du (都志辉)

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.

My talk will focus on two novel techniques in high-performance network modeling and simulation. The first is a GPU-assisted hybrid network traffic modeling method. The hybrid approach offloads the computationally intensive bulk traffic calculations to the background onto GPU, while leaving detailed simulation of network transactions in the foreground on CPU. Our experiments show that the CPU-GPU hybrid approach can achieve significant performance improvement over the CPU-only approach.

The second technique is a distributed network emulation method based on simulation symbiosis. Mininet is a container-based emulation environment that can study networks consisted of virtual hosts and OpenFlow-enabled virtual switches on Linux. It is well-known, however, that experiments using Mininet may lose fidelity for large-scale networks and heavy traffic load. The proposed symbiotic approach uses an abstract network model to coordinate distributed Mininet instances with superimposed traffic to represent large-scale network scenarios.

Invited Talk: High-Performance Modeling and Simulation of Computer Networks

High-Performance Modeling and Simulation of Computer Networks

April 26, 2017

Laboratory of Information, Networking and Communication Sciences (LINCS), Paris, France
Host: Professor Dario Rossi

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.
My talk will focus on two novel techniques in high-performance network modeling and simulation. The first is a GPU-assisted hybrid network traffic modeling method. The hybrid approach offloads the computationally intensive bulk traffic calculations to the background onto GPU, while leaving detailed simulation of network transactions in the foreground on CPU. Our experiments show that the CPU-GPU hybrid approach can achieve significant performance improvement over the CPU-only approach.
The second technique is a distributed network emulation method based on simulation symbiosis. Mininet is a container-based emulation environment that can study networks consisted of virtual hosts and OpenFlow-enabled virtual switches on Linux. It is well-known, however, that experiments using Mininet may lose fidelity for large-scale networks and heavy traffic load. The proposed symbiotic approach uses an abstract network model to coordinate distributed Mininet instances with superimposed traffic to represent large-scale network scenarios.

Invited Talk: Extending PrimoGENI for Symbiotic Distributed Network Emulation

Extending PrimoGENI for Symbiotic Distributed Network Emulation

March 13, 2017

GENI Regional Workshop (GRW), held in conjunction with GEC25 Miami, Florida, USA

The talk includes recent development in hybrid at-scale network experimentation, which extends the previous PrimoGENI project.

[slides]