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At least 1,549 records · Page 86

All sources NASA NTRS DOE OSTI

NASA NTRS1985

Study of one- and two-dimensional filtering and deconvolution algorithms for a streaming array computer

The development of vector processing computers of the streaming array architectures has made possible a dramatic decrease in the time required for the solution of problems, i.e., for those algorithms which readily lend themselves to sequential operations on long vectors. There has concurrently been rapid growth in the applications of the techniques generally known as mathematical digital filtering, also called signal analysis, digital signal processing, time series analysis, and digital image processing. The best known applications of these techniques is to seismic data and two-dimensional images, data types consisting of very large collections of numbers. A major limitation for these cases is the size and speed of the computer available. Therefore, a move to the streaming array architecture can result in marked improvement in the data analysis techniques which may be employed.

Ioup, G. E.↗

DOE OSTI2021

Position Papers for the ASCR Workshop on Cybersecurity and Privacy for Scientific Computing Ecosystems

At the request of the Department of Energy's (DOE) Office of Advanced Scientific Computing Research (ASCR), this program committee has been tasked with organizing a workshop to identify basic research needs in cybersecurity and privacy to better support DOE's science and energy mission. As part of the process, the program committee is soliciting community input in the form of position papers to help identify significant use cases, facility issues, and other barriers to enabling verifiably trustworthy computational science while preserving data confidentiality as appropriate for scientific workflows of interest to DOE. The program committee will review these position papers and based on the fit of their area of expertise and interest, selected contributors will have the opportunity to participate in the workshop currently planned as a virtual event November 3-5th, 2021. The thrust areas that will be explored by this workshop are the following: (1) Algorithms for secure, scalable, privacy-enhancing technologies and frameworks, including: Federated AI/ML, Differential privacy, Randomized algorithms, Adversarial modeling & simulation, Graph algorithms, and Formal methods; (2) Platforms to support the entire scientific-computing ecosystem, including edge computing for large-scale experiments, focusing on heterogeneous systems and distributed systems, including: Heterogeneous computing systems, Distributed computing systems, and Secure data architectures; and (3) Data workflows to allow agile use of data while preserving integrity and privacy, making the important properties verifiable either at runtime or post-computation, including: Integrity and provenance and Data management infrastructure. Topics that are out-of-scope for the workshop include discussing specific proposed solutions or areas that are clearly out of DOE's fundamental and applied-sciences mission scope, e.g., cryptography, enterprise security, and general-operations technology.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1986

Simultaneous iterations algorithm for general eigenvalue problems on parallel processors

The method of simultaneous iteration with shift is extended to extraction of m-eigenpairs of a general eigenvalue problem of large order n in a parallel processing environment. The algorithm combines the power method and the Jacobi technique, and reduces to performing four basic operations. Parallel implementation of the algorithm is discussed in detail. The analysis accounts for computation and communication costs, and utilizes a parallel processing architecture of the ensemble type. Expressions for the computational efficiency and speedup are defined as a function of the problem and hardware parameters. Selected representative problems exhibit efficiencies ranging from 60 to 98 percent.

Utku, S.↗

NASA NTRSDate not supplied

NASA Small Spacecraft and Distributed Systems Program - Recent and Upcoming Technology Demonstrations and Development Efforts

NASA’s Small Spacecraft & Distributed Systems (SSDS) program strengthens U.S. ability to conduct unique missions by rapidly developing and demonstrating capabilities for SmallSat exploration, science, and commercial space. In collaboration with NASA Centers, other government agencies, commercial industry, and academia, SSDS advances next generation SmallSat technologies like power, processing, propulsion, communications, autonomous navigation, architectures (swarms), and applications (AI/ML/Edge Computing)—to extend missions beyond LEO into cislunar and planetary space. Various investment mechanisms exist for SSDS to select and fund projects that will ultimately advance NASA’s Moon to Mars Architecture. Presented here are the latest achievements and findings from recently completed SSDS projects, along with updates from ongoing efforts and planned future work. Successful missions like Starling and CAPSTONE continue to demonstrate their capability after several years on-orbit. Advancements in next generation swarm configurations are being implemented by Starling for space traffic monitoring and management applications. Findings from recent SSDS flight projects are discussed: DiskSat, a unique SmallSat platform alternative to canisterized nanosatellites, launched December 2025 and is gathering data; the PTD series of missions concluded in December 2025. Current SSDS efforts are focused on addressing NASA Shortfalls relating to rendezvous and proximity operations, neuromorphic computing, and space situational awareness.

Roger C Hunter↗

NASA NTRSDate not supplied

NASA Small Spacecraft and Distributed Systems: Recent and Upcoming Technology Demonstrations and Development Efforts

Roger Hunter↗

DOE OSTI2017

Trinity Supercomputer Now Fully Operational

Now one of the fastest supercomputers in the United States, the Trinity Supercomputer Phase II project was completed during the summer of 2017, and the computer became fully operational during an unclassified “open science” run. The computer has now transitioned to classified mode. The Trinity supercomputer is designed to provide increased computational capability for the NNSA Nuclear Security Enterprise in support of increasing geometric and physics fidelities in nuclear weapons simulation codes, while maintaining expectations for total time to solution. The capabilities of Trinity are required for supporting the NNSA Stockpile Stewardship program’s certification and assessments to ensure that the nation’s nuclear stockpile is safe, secure and effective. With an expected operational lifetime through 2020, Trinity is an approximately 41.5 petaflop (41.5 million billion calculations per second, peak performance) supercomputer based on the Cray XC40 architecture. It consists of 19,420 compute nodes utilizing two processor architectures, Intel Xeon Haswell and Intel Xeon Phi Knights Landing. It is a water-cooled system with a power requirement of about 10 megawatts. The Trinity project is managed and operated by Los Alamos National Laboratory and Sandia National Laboratories under the Alliance for Computing at Extreme Scale (ACES) partnership. The system is located at the Nicholas Metropolis Center for Modeling and Simulation at Los Alamos and covers approximately 5,200 square feet of floor space.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1980

Automated quantitative muscle biopsy analysis system

An automated system to aid the diagnosis of neuromuscular diseases by producing fiber size histograms utilizing histochemically stained muscle biopsy tissue. Televised images of the microscopic fibers are processed electronically by a multi-microprocessor computer, which isolates, measures, and classifies the fibers and displays the fiber size distribution. The architecture of the multi-microprocessor computer, which is iterated to any required degree of complexity, features a series of individual microprocessors P.sub.n each receiving data from a shared memory M.sub.n-1 and outputing processed data to a separate shared memory M.sub.n+1 under control of a program stored in dedicated memory M.sub.n.

Castleman, Kenneth R.↗

DOE OSTI2018

Scaling Structured Multigrid to 500K+ Cores Through Coarse-Grid Redistribution

The effcient solution of sparse, linear systems resulting from the discretization of partial differential equations is crucial to the performance of many physics-based simulations. The algorithmic optimality of multilevel approaches for common discretizations makes them a good candidate for an efficient parallel solver. Yet, modern architectures for high-performance computing systems continue to challenge the parallel scalability of multilevel solvers. While algebraic multigrid methods are robust for solving a variety of problems, the increasing importance of data locality and cost of data movement in modern architectures motivates the need to carefully exploit structure in the problem. Robust logically structured variational multigrid methods, such as Black Box Multigrid (BoxMG), maintain structure throughout the multigrid hierarchy. This avoids indirection and increased coarse- grid communication costs typical in parallel algebraic multigrid. Nevertheless, the parallel scalability of structured multigrid is challenged by coarse-grid problems where the overhead in communication dominates computation. In this paper, an algorithm is introduced for redistributing coarse-grid problems through incremental agglomeration. Guided by a predictive performance model, this algorithm provides robust redistribution decisions for structured multilevel solvers. A two-dimensional diffusion problem is used to demonstrate the significant gain in performance of this algorithm over the previous approach that used agglomeration to one processor. In addition, the parallel scalability of this approach

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Understanding the I/O Performance Gap Between Cori KNL and Haswell

The Cori system at NERSC has two compute partitions with different CPU architectures: a 2,004 node Haswell partition and a 9,688 node KNL partition, which ranked as the 5th most powerful and fastest supercomputer on the November 2016 Top 500 list. The compute partitions share a common storage configuration, and understanding the IO performance gap between them is important, impacting not only to NERSC/LBNL users and other national labs, but also to the relevant hardware vendors and software developers. In this paper, we have analyzed performance of single core and single node IO comprehensively on the Haswell and KNL partitions, and have discovered the major bottlenecks, which include CPU frequencies and memory copy performance. We have also extended our performance tests to multi-node IO and revealed the IO cost difference caused by network latency, buffer size, and communication cost. Overall, we have developed a strong understanding of the IO gap between Haswell and KNL nodes and the lessons learned from this exploration will guide us in designing optimal IO solutions in many-core era.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1993

CCARES: A computer algorithm for the reliability analysis of laminated CMC components

Structural components produced from laminated CMC (ceramic matrix composite) materials are being considered for a broad range of aerospace applications that include various structural components for the national aerospace plane, the space shuttle main engine, and advanced gas turbines. Specifically, these applications include segmented engine liners, small missile engine turbine rotors, and exhaust nozzles. Use of these materials allows for improvements in fuel efficiency due to increased engine temperatures and pressures, which in turn generate more power and thrust. Furthermore, this class of materials offers significant potential for raising the thrust-to-weight ratio of gas turbine engines by tailoring directions of high specific reliability. The emerging composite systems, particularly those with silicon nitride or silicon carbide matrix, can compete with metals in many demanding applications. Laminated CMC prototypes have already demonstrated functional capabilities at temperatures approaching 1400 C, which is well beyond the operational limits of most metallic materials. Laminated CMC material systems have several mechanical characteristics which must be carefully considered in the design process. Test bed software programs are needed that incorporate stochastic design concepts that are user friendly, computationally efficient, and have flexible architectures that readily incorporate changes in design philosophy. The CCARES (Composite Ceramics Analysis and Reliability Evaluation of Structures) program is representative of an effort to fill this need. CCARES is a public domain computer algorithm, coupled to a general purpose finite element program, which predicts the fast fracture reliability of a structural component under multiaxial loading conditions.

Duffy, Stephen F.↗

NASA NTRS2003

The NASA/GSFC Advanced Data Grid: A Prototype for Future Earth Science Ground System Architectures

Contents include the following: Background and motivation. Grid computing concepts. Advanced data grid (ADG) prototype development. ADG requirements and operations concept. ADG architecture. ADG implementation. ADG test plan. ADG schedule. Summary and status.

Gasster, Samuel D.↗

NASA NTRS2006

Effect of Reinforcement Architecture on Fracture of Selectively Reinforced Metallic Compact Tension Specimens

A computer-based parametric study of the effect of reinforcement architectures on fracture response of aluminum compact-tension (CT) specimens is performed. Eleven different reinforcement architectures consisting of rectangular and triangular cross-section reinforcements were evaluated. Reinforced specimens produced between 13 and 28 percent higher fracture load than achieved with the non-reinforced case. Reinforcements with blunt leading edges (rectangular reinforcements) exhibited superior performance relative to the triangular reinforcements with sharp leading edges. Relative to the rectangular reinforcements, the most important architectural feature was reinforcement thickness. At failure, the reinforcements carried between 58 and 85 percent of the load applied to the specimen, suggesting that there is considerable load transfer between the base material and the reinforcement.

Abada, Christopher H.↗

DOE OSTI2012

Service Oriented Architecture for High Level Applications

Standalone high level applications often suffer from poor performance and reliability due to lengthy initialization, heavy computation and rapid graphical update. Service-oriented architecture (SOA) is trying to separate the initialization and computation from applications and to distribute such work to various service providers. Heavy computation such as beam tracking will be done periodically on a dedicated server and data will be available to client applications at all time. Industrial standard service architecture can help to improve the performance, reliability and maintainability of the service. Robustness will also be improved by reducing the complexity of individual client applications.

43 PARTICLE ACCELERATORS↗

DOE OSTI2020

Ensembles of Networks Produced from Neural Architecture Search

Neural architecture search (NAS) is a popular topic at the intersection of deep learning and high performance computing. NAS focuses on optimizing the architecture of neural networks along with their hyperparameters in order to produce networks with superior performance. Much of the focus has been on how to produce a single best network to solve a machine learning problem, but as NAS methods produce many networks that work very well, this affords the opportunity to ensemble these networks to produce an improved result. Additionally, the diversity of network structures produced by NAS drives a natural bias towards diversity of predictions produced by the individual networks. This results in an improved ensemble over simply creating an ensemble that contains duplicates of the best network architecture retrained to have unique weights.

Herron, Emily↗

DOE OSTI2017

High-performance computing with quantum processing units

The prospects of quantum computing have driven efforts to realize fully functional quantum processing units (QPUs). Recent success in developing proof-of-principle QPUs has prompted the question of how to integrate these emerging processors into modern high-performance computing (HPC) systems. We examine how QPUs can be integrated into current and future HPC system architectures by accounting for func- tional and physical design requirements. We identify two integration pathways that are differentiated by infrastructure constraints on the QPU and the use cases expected for the HPC system. This includes a tight integration that assumes infrastructure bottlenecks can be overcome as well as a loose integration that as- sumes they cannot. We find that the performance of both approaches is likely to depend on the quantum interconnect that serves to entangle multiple QPUs. As a result, we also identify several challenges in assessing QPU performance for HPC, and we consider new metrics that capture the interplay between system architecture and the quantum parallelism underlying computational performance.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Evaluating Portable Parallelization Strategies for Heterogeneous Architectures in High Energy Physics

High-energy physics (HEP) experiments have developed millions of lines of code over decades that are optimized to run on traditional x86 CPU systems. However, we are seeing a rapidly increasing fraction of floating point computing power in leadership-class computing facilities and traditional data centers coming from new accelerator architectures, such as GPUs. HEP experiments are now faced with the untenable prospect of rewriting millions of lines of x86 CPU code, for the increasingly dominant architectures found in these computational accelerators. This task is made more challenging by the architecture-specific languages and APIs promoted by manufacturers such as NVIDIA, Intel and AMD. Producing multiple, architecture-specific implementations is not a viable scenario, given the available person power and code maintenance issues. The Portable Parallelization Strategies team of the HEP Center for Computational Excellence is investigating the use of Kokkos, SYCL, OpenMP, std::execution::parallel and alpaka as potential portability solutions that promise to execute on multiple architectures from the same source code, using representative use cases from major HEP experiments, including the DUNE experiment of the Long Baseline Neutrino Facility, and the ATLAS and CMS experiments of the Large Hadron Collider. This cross-cutting evaluation of portability solutions using real applications will help inform and guide the HEP community when choosing their software and hardware suites for the next generation of experimental frameworks. We present the outcomes of our studies, including performance metrics, porting challenges, API evaluations, and build system integration.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2012

Optimization of Forward Wave Modeling on Contemporary HPC Architectures

Reverse Time Migration (RTM) is one of the main approaches in the seismic processing industry for imaging the subsurface structure of the Earth. While RTM provides qualitative advantages over its predecessors, it has a high computational cost warranting implementation on HPC architectures. We focus on three progressively more complex kernels extracted from RTM: for isotropic (ISO), vertical transverse isotropic (VTI) and tilted transverse isotropic (TTI) media. In this work, we examine performance optimization of forward wave modeling, which describes the computational kernels used in RTM, on emerging multi- and manycore processors and introduce a novel common subexpression elimination optimization for TTI kernels. We compare attained performance and energy efficiency in both the single-node and distributed memory environments in order to satisfy industry’s demands for fidelity, performance, and energy efficiency. Moreover, we discuss the interplay between architecture (chip and system) and optimizations (both on-node computation) highlighting the importance of NUMA-aware approaches to MPI communication. Ultimately, our results show we can improve CPU energy efficiency by more than 10× on Magny Cours nodes while acceleration via multiple GPUs can surpass the energy-efficient Intel Sandy Bridge by as much as 3.6×.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS1978

Implementation of real-time digital signal processing systems

Special purpose hardware implementation of DFT Computers and digital filters is considered in the light of newly introduced algorithms and IC devices. Recent work by Winograd on high-speed convolution techniques for computing short length DFT's, has motivated the development of more efficient algorithms, compared to the FFT, for evaluating the transform of longer sequences. Among these, prime factor algorithms appear suitable for special purpose hardware implementations. Architectural considerations in designing DFT computers based on these algorithms are discussed. With the availability of monolithic multiplier-accumulators, a direct implementation of IIR and FIR filters, using random access memories in place of shift registers, appears attractive. The memory addressing scheme involved in such implementations is discussed. A simple counter set-up to address the data memory in the realization of FIR filters is also described. The combination of a set of simple filters (weighting network) and a DFT computer is shown to realize a bank of uniform bandpass filters. The usefulness of this concept in arriving at a modular design for a million channel spectrum analyzer, based on microprocessors, is discussed.

Narasimha, M.↗