2009 International Conference On High Performance Computing Simulation

The Second International Conference on High-Performance Computing and Appli- tions (HPCA 2009) was a follow-up event of the successful HPCA 2004. It was held in Shanghai, a beautiful, active, and modern city in China, August 10–12, 2009. It served as a forum to present current work by researchers and software developers from around the world as well as to highlight activities in the high-performance c- puting area. It aimed to bring together research scientists, application pioneers, and software developers to discuss problems and solutions and to identify new issues in this area. This conference emphasized the development and study of novel approaches for high-performance computing, the design and analysis of high-performance - merical algorithms, and their scientific, engineering, and industrial applications. It offered the conference participants a great opportunity to exchange the latest research results, heighten international collaboration, and discuss future research ideas in HPCA. In addition to 24 invited presentations, the conference received over 300 contr- uted submissions from over ten countries and regions worldwide, about 70 of which were accepted for presentation at HPCA 2009. The conference proceedings contain some of the invited presentations and contributed submissions, and cover such research areas of interest as numerical algorithms and solutions, high-performance and grid c- puting, novel approaches to high-performance computing, massive data storage and processing, hardware acceleration, and their wide applications.

This proceedings volume contains a selection of papers presented at the Fourth International Conference on High Performance Scientific Computing held at the Hanoi Institute of Mathematics, Vietnamese Academy of Science and Technology (VAST), March 2-6, 2009. The conference was organized by the Hanoi Institute of Mathematics, the Interdisciplinary Center for Scientific Computing (IWR), Heidelberg, and its Heidelberg Graduate School of Mathematical and Computational Methods for the Sciences, and Ho Chi Minh City University of Technology. The contributions cover the broad interdisciplinary spectrum of scientific computing and present recent advances in theory, development of methods, and applications in practice. Subjects covered are mathematical modelling, numerical simulation, methods for optimization and control, parallel computing, software development, applications of scientific computing in physics, mechanics, biology and medicine, engineering, hydrology problems, transport, communication networks, production scheduling, industrial and commercial problems.

This book constitutes the refereed proceedings of the 4th International Workshop, PMBS 2013 in Denver, CO, USA in November 2013. The 14 papers presented in this volume were carefully reviewed and selected from 37 submissions. The selected articles broadly cover topics on massively parallel and high-performance simulations, modeling and simulation, model development and analysis, performance optimization, power estimation and optimization, high performance computing, reliability, performance analysis, and network simulations.

This book consists of eight chapters, five of which provide a summary of the tutorials and workshops organised as part of the cHiPSet Summer School: High-Performance Modelling and Simulation for Big Data Applications Cost Action on “New Trends in Modelling and Simulation in HPC Systems,” which was held in Bucharest (Romania) on September 21–23, 2016. As such it offers a solid foundation for the development of new-generation data-intensive intelligent systems. Modelling and simulation (MS) in the big data era is widely considered the essential tool in science and engineering to substantiate the prediction and analysis of complex systems and natural phenomena. MS offers suitable abstractions to manage the complexity of analysing big data in various scientific and engineering domains. Unfortunately, big data problems are not always easily amenable to efficient MS over HPC (high performance computing). Further, MS communities may lack the detailed expertise required to exploit the full potential of HPC solutions, and HPC architects may not be fully aware of specific MS requirements. The main goal of the Summer School was to improve the participants’ practical skills and knowledge of the novel HPC-driven models and technologies for big data applications. The trainers, who are also the authors of this book, explained how to design, construct, and utilise the complex MS tools that capture many of the HPC modelling needs, from scalability to fault tolerance and beyond. In the final three chapters, the book presents the first outcomes of the school: new ideas and novel results of the research on security aspects in clouds, first prototypes of the complex virtual models of data in big data streams and a data-intensive computing framework for opportunistic networks. It is a valuable reference resource for those wanting to start working in HPC and big data systems, as well as for advanced researchers and practitioners.