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GE Global Research has enhanced a previously developed general-purpose quantum computer simulator, improving its efficiency and increasing its functionality. Matrix multiplication operations in the simulator were optimized by taking advantage of the particular structure of the matrices, significantly reducing the number of operations and memory overhead. The remaining operations were then distributed over a cluster, allowing feasible compute times for large quantum systems. The simulator was augmented to evaluate a step-by-step comparison of a quantum algorithm's ideal execution to its real-world performance, including errors. To facilitate the study of error propagation in a quantum system, the simulator s graphical user interface was enhanced to visualize the differences at each step in the algorithm s execution. To verify the simulator s accuracy, three ion trap-based experiments were simulated. The simulator output closely matches experimentalist s results, indicating that the simulator can accurately model such devices. Finally, alternative hardware platforms were researched to further improve the simulator performance. An FPGA-based accelerator was designed and simulated, resulting in substantial performance improvements over the original simulator. Together, this research produced a highly efficient quantum computer simulator capable of accurately modeling arbitrary algorithms on any hardware device.

GE Global Research has enhanced a previously developed general-purpose quantum computer simulator, improving its efficiency and increasing its functionality. Matrix multiplication operations in the simulator were optimized by taking advantage of the particular structure of the matrices, significantly reducing the number of operations and memory overhead. The remaining operations were then distributed over a cluster, allowing feasible compute times for large quantum systems. The simulator was augmented to evaluate a step-by-step comparison of a quantum algorithm's ideal execution to its real-world performance, including errors. To facilitate the study of error propagation in a quantum system, the simulator s graphical user interface was enhanced to visualize the differences at each step in the algorithm s execution. To verify the simulator s accuracy, three ion trap-based experiments were simulated. The simulator output closely matches experimentalist s results, indicating that the simulator can accurately model such devices. Finally, alternative hardware platforms were researched to further improve the simulator performance. An FPGA-based accelerator was designed and simulated, resulting in substantial performance improvements over the original simulator. Together, this research produced a highly efficient quantum computer simulator capable of accurately modeling arbitrary algorithms on any hardware device.

Linux Audio Conference 2009 Paper: The Imperative for High-Performance Audio Computing John ffitch It is common knowledge that desktop computing power is now increasing mainly by the change to multi-core chips. This is a challenge for the software community in general, but is a particular problem for audio processing. Our needs are increasingly towards real-time and low latency. We propose a number of possible paths that need investigation, including multi-core and special accelerators, which may offer useful new musical tools. We define this as High-Performance Audio Computing, or HiPAC, in analogy to current HPC activity, and indicate some on-going work. Original URL: https://lac.linuxaudio.org/2009/cdm/Friday/09_ffitch/index.html

This paper presents a novel Edge Cloud collaborative computing framework designed to meet the low latency and high performance requirements of emerging IoT applications. As the demand for real time processing and efficient resource utilization continues to grow, traditional cloud centric or edge only architectures face critical limitations, such as high latency, limited computational power, and energy inefficiencies. The proposed framework dynamically allocates computational tasks between edge nodes and cloud servers based on factors such as latency sensitivity, computational complexity, and energy constraints. Utilizing machine learning algorithms for intelligent task scheduling and predictive workload management, the system achieves a balanced distribution of processing loads, enhancing responsiveness and scalability. Experimental evaluation through simulation and real world prototyping demonstrates significant improvements in latency reduction, throughput, task completion time, and energy efficiency compared to baseline models. The framework also incorporates secure communication protocols and modular architecture, ensuring adaptability, data privacy, and compatibility with heterogeneous IoT environments. These findings highlight the potential of Edge Cloud collaboration as a practical and scalable solution for enabling next generation IoT services with stringent performance and reliability demands. Shiva Kiran Lingishetty | Yuvaraj Madheswaran | Vineet Baniya "Edge-Cloud Collaboration: A Framework for Low-Latency and High-Performance Computing in IoT Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3 , June 2025, URL: https://www.ijtsrd.com/papers/ijtsrd79991.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/79991/edgecloud-collaboration-a-framework-for-lowlatency-and-highperformance-computing-in-iot-applications/shiva-kiran-lingishetty

This paper presents a novel Edge Cloud collaborative computing framework designed to meet the low latency and high performance requirements of emerging IoT applications. As the demand for real time processing and efficient resource utilization continues to grow, traditional cloud centric or edge only architectures face critical limitations, such as high latency, limited computational power, and energy inefficiencies. The proposed framework dynamically allocates computational tasks between edge nodes and cloud servers based on factors such as latency sensitivity, computational complexity, and energy constraints. Utilizing machine learning algorithms for intelligent task scheduling and predictive workload management, the system achieves a balanced distribution of processing loads, enhancing responsiveness and scalability. Experimental evaluation through simulation and real world prototyping demonstrates significant improvements in latency reduction, throughput, task completion time, and energy efficiency compared to baseline models. The framework also incorporates secure communication protocols and modular architecture, ensuring adaptability, data privacy, and compatibility with heterogeneous IoT environments. These findings highlight the potential of Edge Cloud collaboration as a practical and scalable solution for enabling next generation IoT services with stringent performance and reliability demands. Shiva Kiran Lingishetty | Yuvaraj Madheswaran | Vineet Baniya "Edge-Cloud Collaboration: A Framework for Low-Latency and High-Performance Computing in IoT Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-9 | Issue-3 , June 2025, URL: https://www.ijtsrd.com/papers/ijtsrd79991.pdf Paper URL: https://www.ijtsrd.com/engineering/computer-engineering/79991/edgecloud-collaboration-a-framework-for-lowlatency-and-highperformance-computing-in-iot-applications/shiva-kiran-lingishetty

The House Armed Services Committee asked the GAO (General Accounting Office) to examine the HPCC (High Performance Computing and Communications) program in terms of: (1) the effectiveness of the program's management structure in setting goals and measuring progress, and (2) how extensively private industry has been involved in the planning and execution of this program. The HPCC program aims to accelerate the research and development of high performance computers and networks and promote the use of those resources in both the federal government and the private sector. The administration is now broadening the role of the HPCC program in developing new technology in support of the NII (National Information Infrastructure), and industry participation is crucial for the creation of new products and services for the NII. The GAO recommended that the HPCC program develop a detailed technical agenda as a framework for guiding the government's investment in HPCC research. The HPCC budgets are developed without formal guidelines, and a more standardized method for spending could broaden industry support for the program. Private sector participation could be promoted by inviting industry representatives to collaborate on the development of program plans. The Assistant to the President for Science and Technology (Science Advisor) concurred with the GAO's findings and also recommended that a private sector advisory board be established. (DGM)

Hybrid finite element/finite volume based CaMEL shallow water flow solvers have been successfully extended to study wave effects on ice floes in a simplified 10 sq-km ocean domain. Our solver combines the merits of both the finite element and finite volume methods and is much more sophisticated, robust, super convergent, and is the first of its kind approach extended for studying the dynamics and dispersion of ice chunks in ocean. Apart from studying the influence of waves on rigid, non-moving ice floes, We have also successfully implemented the automatic mesh moving techniques to study the ice kinematics. We have studied the wave-ice interactions using different artificial wave forcing mechanisms impacting, movable isolated ice floes, and also uniformly placed cluster of ice floes that are free to drift.

This application of scalable high performance computing to real time embedded systems is expanding. Traditional benchmarks and notions of scalability drawn from the scientific parallel computing community are of limited relevance when timing requirements must be met within strict size, weight, and power requirements. This paper proposes a bench marking methodology for real time embedded applications, including a relevant notion of scalability. The essential point is to give equal emphasis to timing and functional specifications. We use a test bench on the machine itself to realistically impulse the function or system under test. Metrics that measure the steady state performance account for limitations of non real time system software. The key metric is the minimum processor size required to meet the real time specifications for a given problem size. The scalability of the embedded processor can then be characterized in terms of the rate of increase of this minimum machine size as the problem (and potentially the real time requirement) is scaled.

Project overview: atom-doped solid hydrogen and high energy density molecules as advanced rocket propellants; explosive annulenes as possible route to new nanostructural materials; theoretical methods, benchmarks which include centroid molecular dynamics and ab injitio electronic structure theory and results and future directions.

The final report frames the communication scheduling problems associated with realistic real-time embedded applications on scalable massively parallel processors. These applications require high sustained processing rates, high sustained message passing rates, real-time services at the processing modes, and the most significant risk area of real-time internode communication services.

The objective of this proposal is to fund the purchase of an integrated high performance computing (HPC) instrument for big data researchand education at North Carolina Agricultural and Technical State University (NC A and T). This instrument will promote innovation and translational big data research at NC A and T. It will make NC A and T (a land grant HBCU) a strong player in big data research and education. The research groups involved in this proposal consist of PI Zhan from the Department of Computer Science and other key personnel from the Departments of Computer Science, Computer Systems Technology, and Electrical and Computer Engineering at NC A and T. They have been actively conducting research on big data analytics that require such an instrument for mass data processing and storage. The HPC instrument will be a critical asset for the research groups involving big data analysis and management. It will advance various big data research and educational projects at NC A and T. The instrument will have a significant and lasting broader impact on basic data science, provide new opportunities at NC A and T, and facilitate the advancement of outstanding next-generation faculty members working across the disciplines.

High performance computing (HPC) plays a crucial enabling role for DoD scientists and engineers. The DoD HPC Modernization Program (HPCMP) critically impacts the conduct of science and technology as well as test and evaluation initiatives by integrating, deploying, and maintaining technologically superior advanced computing and communications capabilities. The HPOMP acquires these advanced capabilities as a major contribution to defense system research, design, and development. These HPO capabilities also significantly cut defense system costs by shortening the design cycle and reducing reliance on expensive and destructive live fire and model testing. In its first year of operation, the HPCMP has provided computational resources that have already yielded improved results for scientists and engineers solving DoD mission problems. The HPCMP creates a high performance computing environment that provides DoD scientists and engineers the resources needed to successfully execute their mission. They can access complete HPC facilities from their workstations to either their local sites or remote locations across the nation through the highest commercially available networks. This report contains an extensive selection of abstracts of DoD mission research successes enabled by both the new DoD HPCMP systems and a few other systems. The abstracts are chosen to illustrate the role of HPC in both basic and applied research, by both DoD laboratories and supporting academic and contractor participants, and to both warfighting and civilian sector purposes.

As a leading edge center within the High Performance Computing Modernization Program (HPCMP), the Center for Computational Science (CCS) of the Naval Research Laboratory evaluates new high performance computing (HPC) assets. The center is currently evaluating two systems - an Altix 3700 running the SUSE Linus Enterprise Server (SLES) 10 operating systems and a Cray XD1. With respect to the Altix system, Naval Research Laboratory is looking at applications requiring large memory and testing the new Intel 10.0 compilers. For the XD1, we are examining the applicability of dual core processors and Field Programmable Gate Arrays (FPGAs) to HPC applications.

This report describes research and development undertaken to develop an environment, consisting of compiler and debugger, for developing software for high performance computers that is able to exploit information about the execution of a program. In other words, this environment does more than merely optimize a program, it optimizes the program's performance on a given set of 'typical' inputs.

Many researchers have noted that scientific codes perform poorly on computer architectures involving a memory hierarchy (cache). Furthermore, a number of researchers and some vendors concluded that simply making the caches larger would not solve this problem. Alternatively, some vendors of HPC systems have opted to equip their systems with fast memory interfaces, but with a limited amount of on-chip cache and no off-chip cache. Some RISC-based HPC systems supported some sort of prefetching or streaming facility that allows one to more efficiently stream data between main memory and the processor (e.g., the Cray T3E). However, there are fundamental limitations on the benefits of these approaches which makes it difficult to see how these approaches by themselves will eliminate the Memory Wall. It has been shown that if one relies solely on this approach for the Cray T3E, one is unlikely to achieve much better than 4-6% of the machine's peak performance. Does this mean that as the speed of RISC/CISC processors increases, systems designed to process scientific data are doomed to hit the Memory Wall? The answer to that question depends on the ability of programmers to find innovative ways to take advantage of caches. This report discusses some of the techniques that can be used to overcome this hurdle allowing one to consider what types of hardware resources are required to support these techniques.

Todd Gamblin, a computer scientist at Lawrence Livermore National Laboratory, joined us to talk about Moore's Law, his work at Lawrence Livermore National Laboratory, the components of a micro-chip, and High Performance Computing.

Faster than most speedy computers. More powerful than its NASA data-processing predecessors. Able to leap large, mission-related computational problems in a single bound. Clearly, it s neither a bird nor a plane, nor does it need to don a red cape, because it s super in its own way. It's Columbia, NASA s newest supercomputer and one of the world s most powerful production/processing units. Named Columbia to honor the STS-107 Space Shuttle Columbia crewmembers, the new supercomputer is making it possible for NASA to achieve breakthroughs in science and engineering, fulfilling the Agency s missions, and, ultimately, the Vision for Space Exploration. Shortly after being built in 2004, Columbia achieved a benchmark rating of 51.9 teraflop/s on 10,240 processors, making it the world s fastest operational computer at the time of completion. Putting this speed into perspective, 20 years ago, the most powerful computer at NASA s Ames Research Center, home of the NASA Advanced Supercomputing Division (NAS), ran at a speed of about 1 gigaflop (one billion calculations per second). The Columbia supercomputer is 50,000 times faster than this computer and offers a tenfold increase in capacity over the prior system housed at Ames. What s more, Columbia is considered the world s largest Linux-based, shared-memory system. The system is offering immeasurable benefits to society and is the zenith of years of NASA/private industry collaboration that has spawned new generations of commercial, high-speed computing systems.

The use and acceptance of new high-performance, parallel computing platforms will be impeded by the absence of an infrastructure capable of supporting orders-of-magnitude improvement in hierarchical storage and high-speed I/O (Input/Output). The distribution of these high-performance platforms and supporting infrastructures across a wide-area network further compounds this problem. We describe an architectural design and phased implementation plan for a distributed, Cooperative Storage Environment (CSE) to achieve the necessary performance, user transparency, site autonomy, communication, and security features needed to support the Accelerated Strategic Computing Initiative (ASCI). ASCI is a Department of Energy (DOE) program attempting to apply terascale platforms and Problem-Solving Environments (PSEs) toward real-world computational modeling and simulation problems. The ASCI mission must be carried out through a unified, multilaboratory effort, and will require highly secure, efficient access to vast amounts of data. The CSE provides a logically simple, geographically distributed, storage infrastructure of semi-autonomous cooperating sites to meet the strategic ASCI PSE goal of highperformance data storage and access at the user desktop.

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.

It is important to put stress on a current and innovative teaching and thus leading subjects for students nowadays. This is valid for universities primary. An example of conducting the subject „HPC and Grid Architecture“ in the environment MOODLE is introduced in the article. After making research among students who had passed this subject and after making comparison with other possibilities that are available online, the output was made. Issues discussed are focused on its interesting thing, the way of lecture and availability of subject´s content.

Scientists are increasingly turning to datacenter-scale computers to produce and analyze massive arrays. Despite decades of database research that extols the virtues of declarative query processing, scientists still write, debug and parallelize imperative HPC kernels even for the most mundane queries. This impedance mismatch has been partly attributed to the cumbersome data loading process; in response, the database community has proposed in situ mechanisms to access data in scientific file formats. Scientists, however, desire more than a passive access method that reads arrays from files. This paper describes ArrayBridge, a bi-directional array view mechanism for scientific file formats, that aims to make declarative array manipulations interoperable with imperative file-centric analyses. Our prototype implementation of ArrayBridge uses HDF5 as the underlying array storage library and seamlessly integrates into the SciDB open-source array database system. In addition to fast querying over external array objects, ArrayBridge produces arrays in the HDF5 file format just as easily as it can read from it. ArrayBridge also supports time travel queries from imperative kernels through the unmodified HDF5 API, and automatically deduplicates between array versions for space efficiency. Our extensive performance evaluation in NERSC, a large-scale scientific computing facility, shows that ArrayBridge exhibits statistically indistinguishable performance and I/O scalability to the native SciDB storage engine.

Open source bioinformatics tools running under MS Windows are rare to find, and those running under Windows HPC cluster are almost non-existing. This is despite the fact that the Windows is the most popular operating system used among life scientists. Therefore, we introduce in this initiative WinBioinfTools, a toolkit containing a number of bioinformatics tools running under Windows High Performance Computing Server 2008. It is an open source code package, where users and developers can share and add to. We currently start with three programs from the area of sequence analysis: 1) CoCoNUT for pairwise genome comparison, 2) parallel BLAST for biological database search, and 3) parallel global pairwise sequence alignment. In this report, we focus on technical aspects concerning how some components of these tools were ported from Linux/Unix environment to run under Windows. We also show the advantages of using the Windows HPC Cluster 2008. We demonstrate by experiments the performance gain achieved when using a computer cluster against a single machine. Furthermore, we show the results of comparing the performance of WinBioinfTools on the Windows and Linux Cluster.

Remote data access for data analysis in high performance computing is commonly done with specialized data access protocols and storage systems. These protocols are highly optimized for high throughput on very large datasets, multi-streams, high availability, low latency and efficient parallel I/O. The purpose of this paper is to describe how we have adapted a generic protocol, the Hyper Text Transport Protocol (HTTP) to make it a competitive alternative for high performance I/O and data analysis applications in a global computing grid: the Worldwide LHC Computing Grid. In this work, we first analyze the design differences between the HTTP protocol and the most common high performance I/O protocols, pointing out the main performance weaknesses of HTTP. Then, we describe in detail how we solved these issues. Our solutions have been implemented in a toolkit called davix, available through several recent Linux distributions. Finally, we describe the results of our benchmarks where we compare the performance of davix against a HPC specific protocol for a data analysis use case.

Mobile cloud game is a solution to play high-end games in indigent thin clients with a diversity of end-user devices, and as real-time gaming, mobile cloud game hosting game engines in the cloud. Moreover, frequent change in network quality is another issue that should be limited to run the real fast cloud game. Thus, reliable software components between cloud and user devices as clients, including using artificial intelligence (AI) algorithms such as machine learning, deep learning and so on will enhance the game performance, particularly in multiplayer and real-time conditions. In this paper, we list the mobile cloud game architecture in the high-performance computing (HPC) environment, where a load of the game will be distributed between servers as cloud and clients. The server node as clouds or clients will consist of more than one server with many processors (cores) or sometimes can be recognized as distributed computing. Using HPC for cloud games will boost the game performance where the execution times will be dispersed not only in some node in servers and clients but in many cores of each server or client. The involvement of the internet of things (IoT) and ubiquitous access from heterogeneous devices will give benefit to enjoyment in the game itself.

Mobile cloud game is a solution to play high-end games in indigent thin clients with a diversity of end-user devices, and as real-time gaming, mobile cloud game hosting game engines in the cloud. Moreover, frequent change in network quality is another issue that should be limited to run the real fast cloud game. Thus, reliable software components between cloud and user devices as clients, including using artificial intelligence (AI) algorithms such as machine learning, deep learning and so on will enhance the game performance, particularly in multiplayer and real-time conditions. In this paper, we list the mobile cloud game architecture in the high-performance computing (HPC) environment, where a load of the game will be distributed between servers as cloud and clients. The server node as clouds or clients will consist of more than one server with many processors (cores) or sometimes can be recognized as distributed computing. Using HPC for cloud games will boost the game performance where the execution times will be dispersed not only in some node in servers and clients but in many cores of each server or client. The involvement of the internet of things (IoT) and ubiquitous access from heterogeneous devices will give benefit to enjoyment in the game itself.

This video was produced in 2007 to publicize AHPCRC, the Army High Performance Computing Research Center, a collaboration between the U.S. Army and a consortium of university and industry partners. It addresses the Army's most difficult scientific and engineering challenges using high performance computing, to be the world leader in rapid and innovative research, development, and engineering. Consortium members include: Stanford University, High Performance Technologies, Inc., Morgan State University, New Mexico State University, University of Texas at El Paso and NASA Ames Research Center. For more information about AHPCRC, please visit: http://www.ahpcrc.org

HPCC Information On Line 97 (NASA High Performance Computing and Communications)

Because the development of large-scale scientific/engineering application codes is an often difficult, complicated, and sometimes uncertain process, success depends on identifying and managing risk. One of the drivers of the evolution of software engineering, as a discipline, has been the desire to identify reliable, quantifiable ways to manage software development risks. The taxonomy that follows represents an attempt to organize the sources of software development risk for scientific/engineering applications around three principal aspects of the software development activity: the software development cycle, the development environment, and the programmatic environment. These taxonomic classes are divided into elements and each element is further characterized by its attributes.

Because the development of large-scale scientific/engineering application codes is an often difficult, complicated, and sometimes uncertain process, success depends on identifying and managing risk. One of the drivers of the evolution of software engineering, as a discipline, has been the desire to identify reliable, quantifiable ways to manage software development risks. The taxonomy that follows represents an attempt to organize the sources of software development risk for scientific/engineering applications around three principal aspects of the software development activity: the software development cycle, the development environment, and the programmatic environment. These taxonomic classes are divided into elements and each element is further characterized by its attributes.

This report describes the measurement, reduction, and basic analysis of transport layer protocol (transmission control protocol and user datagram protocol) data collected from large-scale tactical radio network tests. The large volume of data collected necessitates using distributed processing on high-performance-computing (HPC) clusters to reduce and analyze data in a timely fashion. Traditional methods using database ingest and queries on high-end servers cannot handle the increasing scale of current datasets, and processing times are estimated to be on the order of days versus hours using HPC. The requirement for the use of HPC was anticipated, and efforts to develop this capability resulted in a highly capable process that is expected to handle future large-scale tactical network testing.

The main goal of the project is designing in RSFQ technology a 20-GHz 8-bit-wide energy efficient processor datapath consisting of an 8-bit ALU, an 8x8-bit Register File, and an Instruction Decoder. We have had several project modifications resulted in additional tasks, such as the development of energy-efficient zero-static-power dissipation SFQ technology, the development of energy-efficient interface based on a low input voltage polarization modulating VCSELs, and the development of superconducting ferromagnetic Random Access Memory

Evaluating high-performance computing systems (HPCS) based on MPP architectures is a key part of the HPCC initiative. In order to provide HPCS designers with the insight needed to improve the next generation, one must correlate the performance measurements with the dynamic characteristics of the workload itself. This work develops an abstract model suitable for experimentally characterizing workloads based on a set of attributes that illustrate how the workloads stress the various architectural features. Meanwhile, the experimental characterization is conducted in a manner that isolates the effects of the machine loading or architecture on which the measurements are obtained, focusing only on the application. Characterization attributes are designed to reflect the applications' scalability, spatial and temporal locality of references for both instructions and data, and amount of work. Workload produced by NASA ESS Grand Challenge applications was experimentally characterized in order to explore the unique characteristics of this application domain.

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

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

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

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

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

Includes index

Trimester 1 - 2017/2018

Energy minimization of Ising spin-glasses has played a central role in statistical and solid-state physics, facilitating studies of phase transitions and magnetism. Recent proposals suggest using Ising spin-glasses for non-traditional computing as a way to harness the nature's ability to find min-energy configurations, and to take advantage of quantum tunneling to boost combinatorial optimization. Laboratory demonstrations have been unconvincing so far and lack a non-quantum baseline for definitive comparisons. In this work we (i) design and evaluate new computational techniques to simulate natural energy minimization in spin glasses and (ii) explore their application to study design alternatives in quantum adiabatic computers. Unlike previous work, our algorithms are not limited to planar Ising topologies. In one CPU-day, our branch-and-bound algorithm finds ground states on 100 spins, while our local search approximates ground states on 1, 000, 000 spins. We use this computational tool as a simulator to study the significance of hyper-couplings in the context of recently implemented adiabatic quantum computers.

Energy minimization of Ising spin-glasses has played a central role in statistical and solid-state physics, facilitating studies of phase transitions and magnetism. Recent proposals suggest using Ising spin-glasses for non-traditional computing as a way to harness the nature's ability to find min-energy configurations, and to take advantage of quantum tunneling to boost combinatorial optimization. Laboratory demonstrations have been unconvincing so far and lack a non-quantum baseline for definitive comparisons. In this work we (i) design and evaluate new computational techniques to simulate natural energy minimization in spin glasses and (ii) explore their application to study design alternatives in quantum adiabatic computers. Unlike previous work, our algorithms are not limited to planar Ising topologies. In one CPU-day, our branch-and-bound algorithm finds ground states on 100 spins, while our local search approximates ground states on 1, 000, 000 spins. We use this computational tool as a simulator to study the significance of hyper-couplings in the context of recently implemented adiabatic quantum computers.

Energy minimization of Ising spin-glasses has played a central role in statistical and solid-state physics, facilitating studies of phase transitions and magnetism. Recent proposals suggest using Ising spin-glasses for non-traditional computing as a way to harness the nature's ability to find min-energy configurations, and to take advantage of quantum tunneling to boost combinatorial optimization. Laboratory demonstrations have been unconvincing so far and lack a non-quantum baseline for definitive comparisons. In this work we (i) design and evaluate new computational techniques to simulate natural energy minimization in spin glasses and (ii) explore their application to study design alternatives in quantum adiabatic computers. Unlike previous work, our algorithms are not limited to planar Ising topologies. In one CPU-day, our branch-and-bound algorithm finds ground states on 100 spins, while our local search approximates ground states on 1, 000, 000 spins. We use this computational tool as a simulator to study the significance of hyper-couplings in the context of recently implemented adiabatic quantum computers.

Energy minimization of Ising spin-glasses has played a central role in statistical and solid-state physics, facilitating studies of phase transitions and magnetism. Recent proposals suggest using Ising spin-glasses for non-traditional computing as a way to harness the nature's ability to find min-energy configurations, and to take advantage of quantum tunneling to boost combinatorial optimization. Laboratory demonstrations have been unconvincing so far and lack a non-quantum baseline for definitive comparisons. In this work we (i) design and evaluate new computational techniques to simulate natural energy minimization in spin glasses and (ii) explore their application to study design alternatives in quantum adiabatic computers. Unlike previous work, our algorithms are not limited to planar Ising topologies. In one CPU-day, our branch-and-bound algorithm finds ground states on 100 spins, while our local search approximates ground states on 1, 000, 000 spins. We use this computational tool as a simulator to study the significance of hyper-couplings in the context of recently implemented adiabatic quantum computers.

Somatic mutations are acquired point mutations and other forms of genetic alteration in the DNA of somatic cells in the body. Unlike germline mutations, which can be passed on from one individual to another, somatic mutations are not heritable. Somatic mutation (also called genetic sequence variation) has been recognized for decades as an important mechanism for initiating the development of cancer. Now, with the advent of next generation sequencing (NGS), the study of somatic mutation has become much more accessible, enabling genome scientists to characterize somatic mutations in a comprehensive fashion across the entire genome. From recent studies, it is now apparent that other disease processes besides cancer may be influenced by somatic mutation as well, including degenerative processes and inflammatory diseases. Cancer, degenerative diseases and inflammatory processes are all of concern in the setting of spaceflight. Study of somatic mutation analysis, therefore, is an important new tool for examining some of the earliest changes in the genome that lead to disease. This approach has tremendous potential for NASA, for analysis of both model organisms and humans.

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.

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.

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.

Includes bibliographical references and index

Includes bibliographical references and index

High Performance Computing market