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This report describes the High Performance Computing and Communications (HPCC) initiative of the Federal Coordinating Council for Science, Engineering, and Technology. This program is supportive of and coordinated with the National Information Infrastructure Initiative. Now halfway through its 5-year effort, the HPCC program counts among its achievements more than a dozen high-performance computing centers in operation nationwide. Traffic on federally funded networks and the number of local and regional networks connected to these centers continues to double each year. Teams of researchers have made substantial progress in adapting software for use on high-performance computer systems; and the base of researchers, educators, and students trained in HPCC technologies has grown substantially. The five HPCC program components in operation at present are: (1) scalable computing systems ranging from affordable workstations to large-scale high-performance systems; (2) the National Research and Education Network; (3) the Advanced Software Technology and Algorithms program; (4) the Information Infrastructure Technology and Applications program; and (5) the Basic Research and Human Resources program. Components of each are reviewed. Twenty-five tables present program information, with 72 illustrations. (SLD)

This report presents the United States research and development program for 1993 for high performance computing and computer communications (HPCC) networks. The first of four chapters presents the program goals and an overview of the federal government's emphasis on high performance computing as an important factor in the nation's scientific and technological advancement, economic strength, and national security. Needs and benefits, program description, goals, strategy, and the program management approach are discussed in this chapter. Chapter two describes HPCC program components, including High Performance Computing Systems (HPCS), Advanced Software Technology and Algorithms (ASTA), the National Research and Education Network (NREN), and Basic Research and Human Resources (BRHR). Program development and agency budgets are addressed in chapter three, which includes discussions of program planning, evaluation criteria, agency budgets, and descriptions of programs of the Defense Advanced Research Projects Agency, the National Science Foundation, the Department of Energy, the National Aeronautics and Space Administration, the National Institutes of Health, the National Oceanic and Atmospheric Administration, the Environmental Protection Agency, and the National Institute of Standards and Technology. Chapter four, "Grand Challenge and Supporting Technology Case Studies," provides examples of high performance computing and computer communications technologies designed to represent the diversity and significance of application areas. Included are Magnetic Recording Technology, Rational Drug Design, High Speed Civil Transports, Catalysis, Fuel Combustion, Ocean Modeling, Ozone Depletion, Digital Anatomy, Air Pollution, Design of Protein Structures, Venus Imaging, and Technology Links Research to Education. A glossary of terms concludes the report. (DB)

This report offers advice on the strengths and weaknesses of the High Performance Computing and Communications (HPCC) initiative, one of five presidential initiatives launched in 1992 and coordinated by the Federal Coordinating Council for Science, Engineering, and Technology. The HPCC program has the following objectives: (1) to extend U.S. technological leadership in high performance computing and communications; (2) to provide wide dissemination and application of the technologies; and (3) to spur gains in U.S. productivity and industrial competitiveness. Because of the importance of the HPCC initiative to the national well-being, the President's Council of Advisors in Science and Technology (PCAST) established a special panel to advise PCAST on the strengths and weaknesses of the program. The Panel's observations and recommendations relating to four central issues: the program strategy; balance among technology, applications, and infrastructure; program management; and program vision make up the main body of this report. (ALF)

This article is from Source Code for Biology and Medicine , volume 7 . Abstract Background: There is a significant demand for creating pipelines or workflows in the life science discipline that chain a number of discrete compute and data intensive analysis tasks into sophisticated analysis procedures. This need has led to the development of general as well as domain-specific workflow environments that are either complex desktop applications or Internet-based applications. Complexities can arise when configuring these applications in heterogeneous compute and storage environments if the execution and data access models are not designed appropriately. These complexities manifest themselves through limited access to available HPC resources, significant overhead required to configure tools and inability for users to simply manage files across heterogenous HPC storage infrastructure. Results: In this paper, we describe the architecture of a software system that is adaptable to a range of both pluggable execution and data backends in an open source implementation called Yabi. Enabling seamless and transparent access to heterogenous HPC environments at its core, Yabi then provides an analysis workflow environment that can create and reuse workflows as well as manage large amounts of both raw and processed data in a secure and flexible way across geographically distributed compute resources. Yabi can be used via a web-based environment to drag-and-drop tools to create sophisticated workflows. Yabi can also be accessed through the Yabi command line which is designed for users that are more comfortable with writing scripts or for enabling external workflow environments to leverage the features in Yabi. Configuring tools can be a significant overhead in workflow environments. Yabi greatly simplifies this task by enabling system administrators to configure as well as manage running tools via a web-based environment and without the need to write or edit software programs or scripts. In this paper, we highlight Yabi's capabilities through a range of bioinformatics use cases that arise from large-scale biomedical data analysis. Conclusion: The Yabi system encapsulates considered design of both execution and data models, while abstracting technical details away from users who are not skilled in HPC and providing an intuitive drag-and-drop scalable web-based workflow environment where the same tools can also be accessed via a command line. Yabi is currently in use and deployed at multiple institutions and is available at http://ccg.murdoch.edu.au/yabi.

The human nervous system is a massively parallel processor of information. The vast numbers of neurons, synapses and circuits is daunting to those seeking to understand the neural basis of consciousness and intellect. Pervading obstacles are lack of knowledge of the detailed, three-dimensional (3-D) organization of even a simple neural system and the paucity of large scale, biologically relevant computer simulations. We use high performance graphics workstations and supercomputers to study the 3-D organization of gravity sensors as a prototype architecture foreshadowing more complex systems. Scaled-down simulations run on a Silicon Graphics workstation and scale-up, three-dimensional versions run on the Cray Y-MP and CM5 supercomputers.

The computer technologies have rapidly developed in both software and hardware field. The complexity of software is increasing as per the market demand because the manual systems are going to become automation as well as the cost of hardware is decreasing. High Performance Computing (HPC) is very demanding technology and an attractive area of computing due to huge data processing in many applications of computing. The paper focus upon different applications of HPC and the types of HPC such as Cluster Computing, Grid Computing and Cloud Computing. It also studies, different classifications and applications of above types of HPC. All these types of HPC are demanding area of computer science. This paper also done comparative study of grid, cloud and cluster computing based on benefits, drawbacks, key areas of research, characterstics, issues and challenges.

The Active Nodal Task Seeking(ANTS) approach to the design of multicomputer systems is named for its basic component: an Active Nodal Task- Seeker(ANT). In this system, there is no load balancing or load sharing, instead, each ANT computing node is actively finding out how it can contribute to the execution of the needed tasks. A run-time partition is established such that some of the ANT computing nodes are under exhaustive diagnosis at any given time. An ANTs multicomputer system can achieve a mean time to failure of more than 20 years with just 8 computing nodes and 3 buses, while the minimum requirements are 3 computing nodes and 1 bus, and with a worst case computing node failure rate of 5 x 0.0001 per hour. This work has been motivated by the need to develop high-performance multicomputer systems for radar, active and passive sonar, and electronic warfare that can provide ultra-dependable performance for more than 20 years without field repairs. We argue that high performance is also an attribute of an ANTS computing system, because the overhead of dynamic task scheduling is reduced and because efficient use is made of the available processing resources.

Challenges by Domain * Air: Persistent air dominance is at risk * Increasingly effective air defenses * Proliferation of 5th generation fighters, cheap cruise missiles, and UASs * Light-speed war possibilities are terrifying * Space: Now a contested domain * Increasingly important * Increasingly vulnerable * Cyber: Cyber warfare has begun * We don't control the battlespace * We rely on it more and more * We can't find the enemy.

A method is presented to use the massively parallel environment of High Performance Computing (HPC) to more rapidly compute the reliability prediction of military ground vehicles. Current work, and future plans are discussed. Challenges already surmounted are indicated, as are those still to be met.

This is the second publication from the Office of Technology Assessment's assessment on information technology and research, which was requested by the House Committee on Science and Technology and the Senate Committee on Commerce, Science, and Transportation. The first background paper, "High Performance Computing & Networking for Science," published in 1989, framed the outstanding issues; this background paper focuses on the federal role in supporting a national high-performance computing initiative. Chapter 1, "High-Performance Computing and Information Infrastructure for Science and Engineering," discusses the goals of the initiative, the government's role, the structure of federal policy, major strategic concerns, and long-range planning needs. Chapter 2, "Policy Considerations for High-Performance Computing," describes the difficulties and barriers to advancing computer technology, providing access to resources, and expanding and improving usage. The purposes of these centers are also discussed in this section. Chapter 3, "High-Performance Computers: Technology and Challenges," discusses the research and development process and the evolution of computer technology. Brief descriptions of national and other high-performance computer facilities are appended. (KR)

The purpose of this book is to teach new programmers and scientists about the basics of High Performance Computing. Too many parallel and high performance computing books focus on the architecture, theory and computer science surrounding HPC. This book speaks to the practicing chemistry student, physicist, or biologist who need to write and run their programs as part of their research. High Performance Computing, originally published by OâReillyâbut out of print since 2003, has been republished on Connexions. Book author Charles Severance, with his editor Mike Loukides, worked with OâReilly to release the book under a CC-BY license, then coordinated with the Connexions staff to republish it. The book is now freely available on the web and in PDF. Printed copies are available on-demand for the cost of printing and shipping. The CC-BY license also makes it possible for the entire contents of the book to be remixed and republished by anyone.

The purpose of this book is to teach new programmers and scientists about the basics of High Performance Computing. Too many parallel and high performance computing books focus on the architecture, theory and computer science surrounding HPC. This book speaks to the practicing chemistry student, physicist, or biologist who need to write and run their programs as part of their research. High Performance Computing, originally published by OâReillyâbut out of print since 2003, has been republished on Connexions. Book author Charles Severance, with his editor Mike Loukides, worked with OâReilly to release the book under a CC-BY license, then coordinated with the Connexions staff to republish it. The book is now freely available on the web and in PDF. Printed copies are available on-demand for the cost of printing and shipping. The CC-BY license also makes it possible for the entire contents of the book to be remixed and republished by anyone.

The purpose of this book is to teach new programmers and scientists about the basics of High Performance Computing. Too many parallel and high performance computing books focus on the architecture, theory and computer science surrounding HPC. This book speaks to the practicing chemistry student, physicist, or biologist who need to write and run their programs as part of their research. High Performance Computing, originally published by OâReillyâbut out of print since 2003, has been republished on Connexions. Book author Charles Severance, with his editor Mike Loukides, worked with OâReilly to release the book under a CC-BY license, then coordinated with the Connexions staff to republish it. The book is now freely available on the web and in PDF. Printed copies are available on-demand for the cost of printing and shipping. The CC-BY license also makes it possible for the entire contents of the book to be remixed and republished by anyone.

The NASA Center for Computational Sciences (NCCS) at the Goddard Space Flight Center provides high performance highly parallel processors, mass storage, and supporting infrastructure to a community of computational Earth and space scientists. Long running (days) and highly parallel (hundreds of CPUs) jobs are common in the workload. NCCS management structures batch queues and allocates resources to optimize system use and prioritize workloads. NCCS technical staff use a locally developed discrete event simulation tool to model the impacts of evolving workloads, potential system upgrades, alternative queue structures and resource allocation policies.

The National Aeronautics and Space Administration's HPCC program is part of a new Presidential initiative aimed at producing a 1000-fold increase in supercomputing speed and a 100-fold improvement in available communications capability by 1997. As more advanced technologies are developed under the HPCC program, they will be used to solve NASA's 'Grand Challenge' problems, which include improving the design and simulation of advanced aerospace vehicles, allowing people at remote locations to communicate more effectively and share information, increasing scientist's abilities to model the Earth's climate and forecast global environmental trends, and improving the development of advanced spacecraft. NASA's HPCC program is organized into three projects which are unique to the agency's mission: the Computational Aerosciences (CAS) project, the Earth and Space Sciences (ESS) project, and the Remote Exploration and Experimentation (REE) project. An additional project, the Basic Research and Human Resources (BRHR) project exists to promote long term research in computer science and engineering and to increase the pool of trained personnel in a variety of scientific disciplines. This document presents an overview of the objectives and organization of these projects as well as summaries of individual research and development programs within each project.

The National Aeronautics and Space Administration's HPCC program is part of a new Presidential initiative aimed at producing a 1000-fold increase in supercomputing speed and a 100-fold improvement in available communications capability by 1997. As more advanced technologies are developed under the HPCC program, they will be used to solve NASA's 'Grand Challenge' problems, which include improving the design and simulation of advanced aerospace vehicles, allowing people at remote locations to communicate more effectively and share information, increasing scientist's abilities to model the Earth's climate and forecast global environmental trends, and improving the development of advanced spacecraft. NASA's HPCC program is organized into three projects which are unique to the agency's mission: the Computational Aerosciences (CAS) project, the Earth and Space Sciences (ESS) project, and the Remote Exploration and Experimentation (REE) project. An additional project, the Basic Research and Human Resources (BRHR) project exists to promote long term research in computer science and engineering and to increase the pool of trained personnel in a variety of scientific disciplines. This document presents an overview of the objectives and organization of these projects as well as summaries of individual research and development programs within each project.

FSHPC_Gray - Light_High-Performance Computing in the Federal Market

The fingerprint is one kind of biometric. This biometric unique data have to be processed well and secure. The problem gets more complicated as data grows. This work is conducted to process image fingerprint data with a memetic algorithm, a simple and reliable algorithm. In order to achieve the best result, we run this algorithm in a parallel environment by utilizing a multi-thread feature of the processor. We propose a high-performance computing memetic algorithm (HPCMA) to process a 7200 image fingerprint dataset which is divided into fifteen specimens based on its characteristics based on the image specification to get the detail of each image. A combination of each specimen generates a new data variation. This algorithm runs in two different operating systems, Windows 7 and Windows 10 then we measure the influence of data size on processing time, speed up, and efficiency of HPCMA with simple linear regression. The result shows data size is very influencing to processing time more than 90%, to speed up more than 30%, and to efficiency more than 19%.

The fingerprint is one kind of biometric. This biometric unique data have to be processed well and secure. The problem gets more complicated as data grows. This work is conducted to process image fingerprint data with a memetic algorithm, a simple and reliable algorithm. In order to achieve the best result, we run this algorithm in a parallel environment by utilizing a multi-thread feature of the processor. We propose a high-performance computing memetic algorithm (HPCMA) to process a 7200 image fingerprint dataset which is divided into fifteen specimens based on its characteristics based on the image specification to get the detail of each image. A combination of each specimen generates a new data variation. This algorithm runs in two different operating systems, Windows 7 and Windows 10 then we measure the influence of data size on processing time, speed up, and efficiency of HPCMA with simple linear regression. The result shows data size is very influencing to processing time more than 90%, to speed up more than 30%, and to efficiency more than 19%.

The fingerprint is one kind of biometric. This biometric unique data have to be processed well and secure. The problem gets more complicated as data grows. This work is conducted to process image fingerprint data with a memetic algorithm, a simple and reliable algorithm. In order to achieve the best result, we run this algorithm in a parallel environment by utilizing a multi-thread feature of the processor. We propose a high-performance computing memetic algorithm (HPCMA) to process a 7200 image fingerprint dataset which is divided into fifteen specimens based on its characteristics based on the image specification to get the detail of each image. A combination of each specimen generates a new data variation. This algorithm runs in two different operating systems, Windows 7 and Windows 10 then we measure the influence of data size on processing time, speed up, and efficiency of HPCMA with simple linear regression. The result shows data size is very influencing to processing time more than 90%, to speed up more than 30%, and to efficiency more than 19%.

The fingerprint is one kind of biometric. This biometric unique data have to be processed well and secure. The problem gets more complicated as data grows. This work is conducted to process image fingerprint data with a memetic algorithm, a simple and reliable algorithm. In order to achieve the best result, we run this algorithm in a parallel environment by utilizing a multi-thread feature of the processor. We propose a high-performance computing memetic algorithm (HPCMA) to process a 7200 image fingerprint dataset which is divided into fifteen specimens based on its characteristics based on the image specification to get the detail of each image. A combination of each specimen generates a new data variation. This algorithm runs in two different operating systems, Windows 7 and Windows 10 then we measure the influence of data size on processing time, speed up, and efficiency of HPCMA with simple linear regression. The result shows data size is very influencing to processing time more than 90%, to speed up more than 30%, and to efficiency more than 19%.

The Affordable High Performance Computing (AHPC) project demonstrated that high-performance computing based on a distributed network of computer workstations is a cost-effective alternative to vector supercomputers for running CPU and memory intensive design and analysis tools. The AHPC project created an integrated system called a Network Supercomputer. By connecting computer work-stations through a network and utilizing the workstations when they are idle, the resulting distributed-workstation environment has the same performance and reliability levels as the Cray C90 vector Supercomputer at less than 25 percent of the C90 cost. In fact, the cost comparison between a Cray C90 Supercomputer and Sun workstations showed that the number of distributed networked workstations equivalent to a C90 costs approximately 8 percent of the C90.

Government Publishing Office U.S. Congress Senate Committee on Commerce, Science, and Transportation HIGH-PERFORMANCE COMPUTING Date(s) Held: 2006-07-19 109th Congress, 2nd Session GPO Document Source: CHRG-109shrg71158 Superintendents of Documents ID: Y 4.C 73/7 Witnesses: Burt, Stanley K., Ph.D., Director, Advanced Biomedical Computing Center Garrett, Michael, Director, Airplane Performance, Boeing Commercial Airplanes Jehn, Christopher, Vice President, Government Programs, Cray Inc Lombardo, Joseph M., Director, National Supercomputing Center for Energy and the Environment, University of Nevada, Las Vegas Szykman, Dr. Simon, Director, National Coordination Office for Networking and Information Technology Research and Development Waters, Jack, Executive Vice President/Chief Technology Officer, Level (3) Communications Wladawsky-Berger, Dr. Irving, Vice President, Technical Strategy and Innovation, International Business Machines Corporation (IBM) Related Items: United States House Bill 28 (109th Congress) United States Senate Bill 2109 (109th Congress) United States Senate Bill 2802 (109th Congress) Public Law 102-194 Public Law 105-305

This hearing before the Senate Subcommittee on Science, Technology, and Space focuses on S. 272, the High-Performance Computing and Communications Act of 1991, a bill that provides for a coordinated federal research and development program to ensure continued U.S. leadership in this area. Performance computing is defined as representing the leading edge of technological advancement in computing. This report includes the text of S. 272 as well as opening statements presented by Senators Gore, Pressler, Kasten, and Robb, and by the Chairman, Senator Hollings. Also included are statements from Allan D. Bromley, Director of the Office of Science and Technology Policy; Tracey Gray, president of marketing, government systems division, US Sprint Communications Co.; Malvin H. Kalos, director of the Cornell Theory Center; Donald Langenberg, chancellor of the University of Maryland system; David C. Nagel, vice president of advanced technology, Apple Computer, Inc.; and John S. Wold, executive director, Lilly research laboratories, Eli Lilly and Co., who was accompanied by Riaz Abdulla, manager of a high-performance computing program. Additional articles, letters, and statements are included from the American Library Association; the Association of Research Libraries; Jack L. Brock, Jr., Director of the Government Information and Financial Management Issues, Information Management Division, Government Accounting Office (GAO); the Computing Research Association; John Patrick Crecine, president of the Georgia Institute of Technology; and Kenneth M. King, president of EDUCOM. (DB)

The U.S. Army is exploring a variety of armament propulsion options for indirect- and direct-fire weapons to be employed in the legacy force and for the Future Combat System (FCS). Development of propelling charges for these weapons requires state-of-the-art physics-based modeling codes. The gun propulsion-modeling environment has traditionally been one in which independently developed and upgraded computer codes were used to analyze different propulsion techniques. with no single maintainable code able to evaluate several candidate system. This situation rendered comparison of ballistic performance for various weapon options cumbersome and inconclusive. In direct response to this situation. the U.S. Army Research Laboratory (ARL) has developed a next-generation computer scaleable. three-dimensional, multi-phase, computational fluid dynamics code for armament propulsion (interior ballistics) modeling.

The focus was to understand and model the composite manufacturing process called Resin Transfer Molding (RTM) and develop numerical simulations to move towards virtual manufacturing in an attempt to reduce the prototype costs. Experiments were carried out to understand and isolate the important features of this process and mathematical models were developed to create a process model. These models were incorporated in numerical simulations to make them useful for manufacturing of complex structures. Numerical procedures were also developed in the process to make the simulations stable and faster. The results of numerical simulations were compared with two elaborate experimental programs, a five sided box and a cone, to verify the authenticity of the numerical simulation and the mathematical models. Different versions of the numerical simulation developed are being used at ARL, Aberdeen and by TACOM. This report gives a brief summary of the work and the details of the work are reported as appendices. (MM)

The objective of part 1 of the research effort was to perform three capability demonstrations on a brokerage infrastructure in a commercial cloud computing environment, to engage multiple computing service providers and application software vendors and collect comparative metrics to evaluate the return-on-investment (ROI) argument for high performance computing within the DoD supply chain and to evaluate the startup costs saved of using a broker. The demos were:1)AltaSim Ghost on Ohio Supercomputer Center, with an estimated ROI of 252X and brokerage startup cost savings of 90% and ten months of time, 2)ANSYS on R Systems, with an estimated ROI of 14.7X and brokerage startup costs savings of 91% and seven weeks of time, 3)Mathematica and Blender on Amazon EC2, with an estimated ROI of 27.4X and brokerage startup cost savings of 96% and one month of time. The objective of part 2 of this research effort was to investigate the use of probabilistic computer architectures to solve computational challenge problems at orders of magnitude lower cost and increased processing capability relative to conventional computer architectures. 1000x+ speedups with 10-30x less power usage using a software-reprogrammable probabilistic video processor were documented.

Under sponsorship from various Department of Defense (DoD) organizations and a cooperative Research and Development Agreement (CRADA) with the U.S. Research Development and Engineering Command, Army Research Laboratory (RDECOM-ARL) HyPerComp has significantly advanced the state of the art of time-domain, and broad band electromagnetic simulations. The TEMPUS (Time-Domain ElectroMagnetic Parallel Unstructured Simulator) environment is a complete self contained code suite that includes computer aided design (CAD) geometry creation and repair unstructured gridding for fell-scale targets with general materials scalable parallel code architecture higher-order accurate discontinuous Galerkin solvers for Maxwell's equations and post processing utilities for solution visualization and exaction of final results like bistatic and monostatic radar cross section (RCS) synthetic aperture radar (SAR) images and high-range-resolution (HRR) profiles. The high-performance TEMPUS environment is well suited for modeling a variety of targets and electromagnetic problems of interest to the U.S. Army such as: 1) high- speed projectiles with subtle surface discontinuities ridges and/or fins 2) ground-based targets such as tanks and scud missile launchers and 3) foliage penetration and ground interaction for target under trees (TUT). Some of the physics-based phenomenological features that govern the electromagnetic response of general targets are: a) specular reflection b) creeping waves c) traveling waves) slow moving surface waves e) edge diffraction f) singular currents at surface discontinuities g) resonating gaps and cavities and h) general material response.

Under sponsorship from various Department of Defense (DoD) organizations and a cooperative Research and Development Agreement (CRADA) with the U.S. Research Development and Engineering Command, Army Research Laboratory (RDECOM-ARL) HyPerComp has significantly advanced the state of the art of time-domain, and broad band electromagnetic simulations. The TEMPUS (Time-Domain ElectroMagnetic Parallel Unstructured Simulator) environment is a complete self contained code suite that includes computer aided design (CAD) geometry creation and repair unstructured gridding for fell-scale targets with general materials scalable parallel code architecture higher-order accurate discontinuous Galerkin solvers for Maxwell's equations and post processing utilities for solution visualization and exaction of final results like bistatic and monostatic radar cross section (RCS) synthetic aperture radar (SAR) images and high-range-resolution (HRR) profiles. The high-performance TEMPUS environment is well suited for modeling a variety of targets and electromagnetic problems of interest to the U.S. Army such as: 1) high- speed projectiles with subtle surface discontinuities ridges and/or fins 2) ground-based targets such as tanks and scud missile launchers and 3) foliage penetration and ground interaction for target under trees (TUT). Some of the physics-based phenomenological features that govern the electromagnetic response of general targets are: a) specular reflection b) creeping waves c) traveling waves) slow moving surface waves e) edge diffraction f) singular currents at surface discontinuities g) resonating gaps and cavities and h) general material response.

High Performance Computing market

High Performance Computing market

High Performance Computing market

High-performance computing refers to the use of advanced computing technologies, especially supercomputers, to solve highly complex, numerically intensive problems in the shortest possible time. These scientific problems-such as understanding global climate change or analyzing molecular structure-are collectively called the grand challenges. The federal High Performance Computing and Communications Initiative is a research and development effort that seeks to significantly accelerate the avallability and utilization of high performance computers and networks in order to better address these challenges. At $275 million in fiscal year 1993, the Advanced Research Projects Agency (ARPA) has the largest budget of any single agency participating in the $800-million federal initiative.' Given the importance of this initiative and ARPA'S dominant role in it, the House Armed Services Committee asked GAO to assess the program, particularly (1) the agency's distribution of advanced computers to research sites, (2) its interaction with the research community, and (3) the balance between hardware and software investments in the ARPA program.

Integration and test of the GEMINI instrument package. Most of the work performed under this task supported development of the GEneralized Multi-wavelength INfrared Imager (GEMINI) for the Maui Space Surveillance Site. GEMINI, not to be confused with the National Science Foundation's Gemini Telescopes Project, is a one-of-a-kind sensor package built for USAF Space Command operational use in conjunction with its space object identification (SOI mission. GEMINI hosts a variety of sensors for use with several algorithms for reconstructing high-resolution satellite and astronomical images from data degraded by atmospheric turbulence. The first GEMINI operating mode made available to Space Command delivers high resolution images using a speckle imaging technique. Schafer contributed to the project by implementing and developing parallel speckle imaging recovery and calibration algorithms and by designing computer simulations to model real-world detector effects and predict available resolution. Schafer also conducted an observational evaluation of the speckle imaging algorithm. A distinctive feature of the GEMINI data acquisition/reduction system is the provision for near real-time (NRT) video feedback of reconstructed imagery to the GEMINI operator. NRT feedback allows the operator to select camera exposure times and optical filters based on the visual quality of reconstructed data. These operating parameters can thus be optimized while data is being acquired, potentially eliminating the need for subsequent observations of the same object. For example. raw image data of a bright satellite can yield a poor reconstruction if most of the light is concentrated in a strong, localized glint. Selection of a different filter may reduce the glint effect in the reconstruction, which would be apparent to the operator using an NRT imaging system.

The primary objectives of the Phase II of the project are: (a) implement the context-aware parallel random number generator (CPRNG), developed in Phase I of this project, (b) implement the interstream correlation (ISC) test so that the quality of the random numbers (RNs) used by applications are evaluated and quality metrics are reported on demand. Both objectives have been accomplished. Beyond these objectives, additional design and implementation contributions have been accomplished. A flexible CPRNG-ISC Test (CIT) framework was developed and implemented so that a third party tester such as Dieharder or TestU01 can be run along with ISC test to corroborate or compare ISC test results with those from the well-known single-stream test batteries. The CPRNG Library facilitates implementation and use of other random number generators within the test framework easily.

Well-designed network traffic collection provides a wealth of information regarding what a network-centric system is doing. Analysis tools examine the data and readily help answer what, but why and how may not be attainable, even with advanced analytics. Collecting device status information can augment network traffic information to help answer why and how. To answer these questions, the Aberdeen Test Center developed an active device status collector and collaborated with the US Army Research Laboratory to develop a high-performance-computing solution for correlating device status data with network traffic data.

Members of this project have studied the academic and industrial uses of High Performance Computing (HPC) and have conducted site visits at a number of Army R&D HPC user and provider sites, including the Medical Research and Materiel Command (MRMC), Edgewood Chemical Biological Center (ECBC), the Army Research Lab's Major Shared Resource Center (MSRC), the Developmental Test Command (DTC), Research, Development, and Engineering Command's (RDECOM's) Communications- Electronics Research, Development, and Engineering (RD&E) Center (CERDEC), Scalable Network Technologies, Inc., and the Director for Command, Control, Communication, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) for Future Combat Systems (FCS) at Boeing, Huntington Beach, California. Because of the breadth of possible uses of HPC in Army R&D, our client asked us to concentrate on two HPC application areas: biotechnology and biomedicine, and modeling and simulation of the complex battlefield wireless network communication systems. We did so, and concluded that these areas are of great importance and merit focused funding and attention.

Members of this project have studied the academic and industrial uses of High Performance Computing (HPC) and have conducted site visits at a number of Army R&D HPC user and provider sites, including the Medical Research and Materiel Command (MRMC), Edgewood Chemical Biological Center (ECBC), the Army Research Lab's Major Shared Resource Center (MSRC), the Developmental Test Command (DTC), Research, Development, and Engineering Command's (RDECOM's) Communications- Electronics Research, Development, and Engineering (RD&E) Center (CERDEC), Scalable Network Technologies, Inc., and the Director for Command, Control, Communication, Computers, Intelligence, Surveillance, and Reconnaissance (C4ISR) for Future Combat Systems (FCS) at Boeing, Huntington Beach, California. Because of the breadth of possible uses of HPC in Army R&D, our client asked us to concentrate on two HPC application areas: biotechnology and biomedicine, and modeling and simulation of the complex battlefield wireless network communication systems. We did so, and concluded that these areas are of great importance and merit focused funding and attention.

With microprocessors hitting the power wall, alternative architectures are receiving substantial attention. Two factors make FPGAs promising, particularly for high performance computing applications: (i) the potential for thousand-fold parallelism, and (ii) the embedding of control into logic, which enables payload to be delivered every cycle. In this talk we will describe work in building FPGA-based accelerators for computational biology and bioinformatics. We find that, while there is potential for enormous speedup, achieving it demands both selection of appropriate applications and unusually careful design. Beginning with the applications: these include processing of biological sequences (both comparison and analysis), computations involving of microarray (gene chip) data, modeling of molecular interaction, and molecular dynamics simulations. Although low-precision computations are most amenable to acceleration, we also examine applications that typically use floating point. Success with these applications comes from specific design methods. These methods fall into four broad categories: restructuring applications to make use of a different processor model; taking advantage of fine-grained parallelism in computations and memory access; using non-standard arithmetic types and operations that map well into FPGA fabric; and finally, reconsidering application design issues. The latter include methods for ensuring that applications are flexible, scalable, and at least somewhat portable. ©2007 Microsoft Corporation. All rights reserved.

The grant was provided by AFOSR to establish a SAR High Performance Laboratory. With this support, the following items were purchased for the SAR HPC laboratory: 16 Origin-2000 300 MHz Processors; 8 GBytes of RAM; 108 GBytes of Fiber Channel Hard Drive; OCTANE Workstation with a 300 MHz RS-12000 Processor, and Enhanced SSE Graphics. During the past two summers, the PI also collaborated with the members of the Air Force's Rome Laboratory (Contact: Mark Linderman) and Wright Patterson Laboratory (Steve Worrell, Ed Zelnio and Mike Bryant) on various implementation aspects of SAR signal and image processing algorithms on Shared Memory Processors (SMP) and Distributed Memory (DM) HP Cs. The results of these collaborations are documented in the attached three papers.

High performance computing has historically played an important role in the ability of the United States to develop and deploy a wide range of national security capabilities, such as stealth aircraft, sonar arrays, and high-energy rocket fuels. Therefore, it is critical that the United States stay ahead technically in this important area. Export controls on computer hardware are one of several strategies used to ensure U.S. superiority in high performance computing. However, rapid advances in computer technology have limited the government's ability to prevent the export of high performance computing hardware to potential adversaries. Furthermore, controls that once restricted the export of high-end supercomputers now restrict the export of low- and mid-range servers. Denial of access to growing third-world markets, burgeoning foreign computer manufacturing capability, and increasing foreign demand for computers give rise to industry concerns over the potential loss of American dominance in the world computer market. These issues are seen as a threat to both U.S. economic security and national defense. In light of these concerns, the Deputy Under Secretary of Defense (Science and Technology) was asked to conduct a study to develop alternative export control strategies for high performance computing. The goal of this study was to develop strategies that protect national security interests in high performance computing while not endangering U.S. dominance of the computer industry. This report summarizes the findings of this work, completed in November 2000. The current hardware control strategy is based on a measure of computer performance known as MTOPS, millions of theoretical operations per second. This metric, implemented in 1991, served as an effective basis for export controls until the late 1990s.

This report encapsulated many provisions by the MHPCC to the Advanced Early Warning (AEW) community including compute time, online radar and IR databases, Khoros toolboxes, and MATLAB 1 parallelization.

Viewgraphs from presentation on the use of streaming and dynamic compilers for high performance embedded computing.

The final report develops realistic benchmarks to assess the applicability of current memory massively parallel processors (MPPs) for real-time embedded applications, such as synthetic aperture radar (SAR) and space-time adaptive processing (STAP). A scalable real-time mapping of a generic two-dimensional processing chain applicable to SAR and STAP is developed and analyzed.

In its grandest sense, Project CASIE explored the development of a computational system capable of high level perception and problem solving that reflects the cognitive processes of the human brain. Most specifically, it concentrated on better understanding and modeling intuition and insight in a computational fashion. The goal was to address the fundamental problem of modeling and solving communities of tasks from a cognitive point of view through multiple problem solving agents working cooperatively or competitively on different subtasks at multiple levels of granularity.

The panelists talk about high performance computing with Colin Cornaby.

The panelists talk about high performance computing with Colin Cornaby.

Nowadays, the prevalence of computing systems in our lives is so ubiquitous that we live in a cyber-physical world dominated by computer systems, from pacemakers to cars and airplanes. These systems demand for more computational performance to process large amounts of data from multiple data sources with guaranteed processing times. Actuating outside of the required timing bounds may cause the failure of the system, being vital for systems like planes, cars, business monitoring, e-trading, etc. High-Performance and Time-Predictable Embedded Computing presents recent advances in software architecture and tools to support such complex systems, enabling the design of embedded computing devices which are able to deliver high-performance whilst guaranteeing the application required timing bounds. Technical topics discussed in the book include:  Parallel embedded platforms Programming models Mapping and scheduling of parallel computations Timing and schedulability analysis Runtimes and operating systemsThe work reflected in this book was done in the scope of the European project P SOCRATES, funded under the FP7 framework program of the European Commission. High-performance and time-predictable embedded computing is ideal for personnel in computer/communication/embedded industries as well as academic staff and master/research students in computer science, embedded systems, cyber-physical systems and internet-of-things.

Knowing the future weather on the battlefield with high certainty can result in a higher advantage over the adversary. To create this advantage for the United States, the U.S. Navy utilizes the Coupled Ocean/Atmosphere Mesoscale Prediction System (COAMPS) to create high spatial resolution, regional, numerical weather prediction (NWP) forecasts. To compute a forecast, COAMPS runs on high performance computing (HPC) systems. These HPC systems are large, dedicated supercomputers with little ability to scale or move. This makes these systems vulnerable to outages without a costly, equally powerful secondary system. Recent advancements in cloud computing and virtualization technologies provide a method for high mobility and scalability without sacrificing performance. This research used standard benchmarks in order to quantitatively compare a virtual machine (VM) to a native HPC cluster. The benchmark tests showed that the VM was feasible platform for executing HPC applications. Then we ran the COAMPS NWP on a VM within a cloud infrastructure to prove the ability to run a HPC application in a virtualized environment. The VM COAMPS model run performed better than the native HPC machine model run. These results show that VM and cloud computing technologies can be used to run HPC applications for the Department of Defense

Supercomputers have existed since the 1960s, but there has been renewed interest in High Performance Computing Market  in the last decade. Developments in artificial intelligence (AI) and the exponential growth in data mean that businesses need access to computers capable of performing complex workloads. Every day, businesses, governments, and individuals generate huge volumes of data.