Utilizing the power of high-performance computing

Research into risky decision making (RDM) has become a multidisciplinary effort. Conversations cut across fields such as psychology, economics, insurance, and marketing. This broad interest highlights the necessity for collaborative investigation of RDM to understand and manipulate the situations within which it manifests. A holistic understanding of RDM has been impeded by the independent development of diverse RDM research methodologies across different fields. There is no software specific to RDM that combines paradigms and analytical tools based on recent developments in high-performance computing technologies. This paper presents a toolkit called RDMTk, developed specifically for the study of risky decision making. RDMTk provides a free environment that can be used to manage globally-based experiments while fostering collaborative research. The incorporation of machine learning and high-performance computing (HPC) technologies in the toolkit further open additional possibilities such as scalable algorithms and big data problems arising from global scale experiments.

High performance computing (HPC) is, today, a key technology to solve large problems in science, engineering, and business by utilizing tremendous computing power which has been evolving to the future. HPC Asia, which is an international conference series on HPC technologies in Asia Pacific region, and was held in the past several times in several countries in Asia regional sites to discuss the issues on high performance computing and to exchange information of research and development results in HPC.

An approach is presented for generating a performance prediction model so that both qualitative (logical correctness) and quantitative (timeliness) properties of a real‐time system can be evaluated. The architecture of a system is layered into a functional layer and a physical one. Both architectural layers are developed as executable models: the executable functional model is a Petri net and the executable physical model is a queuing net. The two‐layered executable models are then connected to develop a performance prediction model. A message‐passing pattern is generated from the Petri net using a state space analysis technique. Then, the queuing net model processes these messages preserving the pattern. Once the network delays are obtained from the queuing model, their values are inserted back into the Petri net model. Since the communication service demands are isolated from the executable functional model, the communications network can be specified independently at any preferred level of detail. This enables the executable functional model to be invariant with respect to the executable physical model resulting in additional flexibility in designing a large‐scale information system. This property, together with the synthesis technique, enables both formal and simulation methods to be used together, when each one utilizes a different self‐contained commercial‐off‐the‐shelf software application. © 2002 Wiley Periodicals, Inc. Syst Eng 6: 1–18, 2003

Modern High Performance Computing (HPC) applications, such as Earth science simulations, produce large amounts of data due to the surging of computing power, while big data applications have become more compute-intensive due to increasingly sophisticated analysis algorithms. The needs of both HPC and big data technologies for advanced HPC and big data applications create a demand for integrated system support. In this study, we introduce Scientific Data Processing (SciDP) to support both HPC and big data applications via integrated scientific data processing. SciDP can directly process scientific data stored on a Parallel File System (PFS), which is typically deployed in an HPC environment, in a big data programming environment running atop Hadoop Distributed File System (HDFS). SciDP seamlessly integrates PFS, HDFS, and the widely-used R data analysis system to support highly efficient processing of scientific data. It utilizes the merits of both PFS and HDFS for fast data transfer, overlaps computing with data accessing, and integrates R into the data transfer process. Experimental results show that SciDP accelerates analysis and visualization of a production NASA Center for Climate Simulation (NCCS) climate and weather application by 6x to 8x when compared to existing solutions.

The National Energy Research Scientific Computing Center (NERSC) is the leading scientific computing facility supporting research within the Department of Energy's Office of Science. NERSC provides high-performance computing (HPC) resources to approximately 4,000 researchers working on about 400 projects. In addition to hosting large-scale computing facilities, NERSC provides the support and expertise scientists need to effectively and efficiently use HPC systems. In February 2010, NERSC, DOE's Office of Advanced Scientific Computing Research (ASCR) and DOE's Office of Basic Energy Sciences (BES) held a workshop to characterize HPC requirements for BES research through 2013. The workshop was part of NERSC's legacy of anticipating users future needs and deploying the necessary resources to meet these demands. Workshop participants reached a consensus on several key findings, in addition to achieving the workshop's goal of collecting and characterizing computing requirements. The key requirements for scientists conducting research in BES are: (1) Larger allocations of computational resources; (2) Continued support for standard application software packages; (3) Adequate job turnaround time and throughput; and (4) Guidance and support for using future computer architectures. This report expands upon these key points and presents others. Several 'case studies' are included as significant representative samples of the needs of science teams within BES. Research teams scientific goals, computational methods of solution, current and 2013 computing requirements, and special software and support needs are summarized in these case studies. Also included are researchers strategies for computing in the highly parallel, 'multi-core' environment that is expected to dominate HPC architectures over the next few years. NERSC has strategic plans and initiatives already underway that address key workshop findings. This report includes a brief summary of those relevant to issues raised by researchers at the workshop.

High performance computing for multidisciplinary design optimization and robustness of vehicle structures

While regular algorithms, characterized by operations on dense matrices and arrays, have long been the mainstay of scientific, high-performance computing, irregular algorithms, which feature unpredictable accesses to pointer-based data structures, are becoming increasingly common in high performance computing, arising in graph analysis, data mining and visualization, among other domains. Unfortunately, the defining characteristics of irregular applications, their dynamic, unpredictable, data-dependent access patterns and data layouts, make achieving high performance on large scale systems difficult. Scaling applications to peta- and exa-scale requires carefully controlling communication and data movement and placement, an inherently difficult task when access patterns and data layouts are unpredictable! Most irregular applications that attain high performance must be painstakingly hand-written and hand-tuned, with few common principles or paradigms uniting various implementations and easing future development. Despite the increasing importance of irregular applications, there is little programmer knowledge, and even less compiler ability, devoted to optimizing them. This project aims to solve these problems. By allowing programmers to write irregular applications in high level forms, with at most a few annotations highlighting key structural properties, programmers can focus on developing their algorithms and methods. The compiler and run-time system can take on the tedious task of optimizing the application for execution at large scales, and can automatically provide efficient implementations. This will provide portability and ease maintenance for existing irregular applications, but, more importantly, open up whole new domains of computational science to large-scale, high-performance simulation codes.

High performance computing promotes the solution of large mathematical models of complex mechanical systems and lays a foundation for the theoretical analysis of complicated and difficult problems in the mechanical field. At the same time, the high reliability transmission of wireless data makes it possible for a variety of motion state mechanical systems to measure and control. In this paper, we present the energy loss model of the wet axle transmission system based on bond graph theory, and obtain the heat source boundary condition of the thermal analysis of the wet axle by calculating the mathematical model of the energy loss. Moreover, thermal network method is proposed to analysis heat accumulation and disequilibrium of multistage closed wet axle, by analyzing heat source distribution and heat transfer characteristics among machine elements, oil and air, finally the mathematic model of thermal analysis is established. The thermal numerical calculation of the thermal network for the complex mechanical system and the verification of the whole vehicle test for the results of the thermal calculation have been realized, then thermal optimization is done for wet axle heat equilibrium. Theory and experiment results indicate that heat accumulation in wet hub and heat disequilibrium of different parts are severe especially under efficient operating condition; thermal network calculation and optimization results verified by real vehicle experiment are true and valid. The research proves that high performance computer and high reliable wireless data transmission technology have promoted the optimization design of complex mechanical system.

Transient stability constrained optimal power flow (TSCOPF) is one the most computational-intensive applications in power systems. Research efforts were made to utilize high performance computing (HPC) technology to parallelize and accelerate TSCOPF solving process, but challenges still exist in performance analysis to identify bottlenecks and improve efficiency on practical HPC platforms such as Beowulf clusters. Based on first two authors' previous work [8, 12] - two-level parallel TSCOPF with reduced-space interior point method (RIPM), a systematic performance analysis approach is demonstrated in this paper. Comprehensive performance analysis procedures - wall time analysis, MPI/OpenMP profiling and tracing - is employed in order to discover performance bottlenecks. State-of-the-art performance analysis software are employed to generate and visualize performance data, providing guidelines for parallel performance enhancement. Numerical results on a 2746-bus system show effectiveness of the proposed approach and relative low overhead in solving large-scale multi-contingency TSCOPF on a Beowulf cluster.

Chapter 4 - Frameworks for Big Data Integration, Warehousing, and Analytics

We address the increasing complexity of scientific workflows in the context of high-performance computing (HPC) and their associated need for robust, adaptable, and flexible computational support systems. We explore five key trends as well as future challenges and opportunities for scientific workflows and HPC technologies.

Implementation of Parallel Dynamic Simulation on Shared-Memory vs. Distributed-Memory Environments

Users need benchmarks that are representative of real-world high-performance computing (HPC) applications in order to make good performance comparisons across platforms on a level playing field. This information can only enhance the usefulness of new HPC technology in the marketplace. SPEC/HPG (Standard Performance Evaluation Corporation/High-Performance Group) has released its first suite of codes and welcomes feedback.

In April, 1999, the National Science Foundation (NSF)-sponsored National Computational Science Alliance (the Alliance) opened its new 7000 square foot advanced technology training and demonstration facility in the Washington, DC metropolitan area. Called the Alliance Center for Collaboration, Education, Science, and Software (ACCESS), it is a new technology transfer and learning mechanism for emerging and advanced technologies, high performance computing, communications, and information technologies. The paper discusses the development of this highly flexible facility for collaborative research and education for high performance computing (HPC) science and engineering users. The leading edge site for ACCESS and the alliance is the National Center for Supercomputing Applications (NCSA) at the University of Illinois at Urbana-Champaign. ACCESS is staffed by NCSA. The paper discusses planning, designing and developing this innovative site, and meeting technological, aesthetic, spatial, and other challenges in setting up this multiple-use, multiple-partner center. It will make recommendations to those wishing to set up similar facilities.

The EUDAT project is a pan-European data initiative that started in October 2011. The project brings together a unique consortium of 25 partners - including research communities, national data and high performance computing (HPC) centres, technology providers, and funding agencies - from 13 countries. EUDAT aims to build a sustainable cross-disciplinary and cross-national data infrastructure that provides a set of shared services for accessing and preserving research data. The design and deployment of these services is being coordinated by multi-disciplinary task forces comprising representatives from research communities and data centres. This short paper presents the achievements of the project during its first year and describes the services that have been chosen to meet the requirements of the initial research communities involved in the project.