Search NASA⌕ Search

SEARCH · Search NASA

Results for “task parallelism”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 1,009 records · Page 56
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Benefits of Integration of Aerojet Rocketdyne and RTI Advanced Gasification Technologies for Hydrogen-Rich Syngas Production

The key objective for this project was to evaluate the potential to achieve substantial reductions in the production cost of H 2 -rich syngas via coal gasification with near-zero emissions due to the cumulative and synergistic benefits realized when multiple advanced technologies are integrated into the overall conversion process. In this project, Aerojet Rocketdyne’s (AR’s) advanced gasification technology (currently being offered as R-GAS™) and RTI International’s (RTI’s) advanced warm syngas cleanup technologies were evaluated via a number of comparative techno-economic case studies. AR’s advanced gasification technology consists of a dry solids pump and a compact gasifier system. Based on the unique design of this gasifier, it has been shown to reduce the capital cost of the gasification block by between 40 and 50%. At the start of this project, actual experimental work had been demonstrated through pilot plant systems for both the gasifier and dry solids pump. RTI’s advanced warm syngas cleanup technologies consist primarily of RTI’s Warm Gas Desulfurization Process (WDP) technology, which effectively allows decoupling of the sulfur and CO 2 removal allowing for more flexibility in the selection of the CO 2 removal technology, plus associated advanced technologies for direct sulfur recovery and water gas shift (WGS). WDP has been demonstrated at pre-commercial scale using an activated amine carbon dioxide recovery process which would not have been possible if a majority of the sulfur had not been removed from the syngas by WDP. This pre-commercial demonstration of RTI’s advanced warm syngas cleanup system was conducted in parallel to the activities on this project. The technical data and cost information from this pre-commercial demonstration were extensively used in this project during the techno-economic analysis. With this project, both of RTI’s advanced WGS technologies were investigated. Because RT’s advanced fixed-bed WGS (AFWGS) process was successfully implemented in the WDP pre-commercial demonstration test mentioned above, this technology was used as part of RTI’s advanced warm syngas technology package for the techno-economic analyses for this project. RTI’s advanced transport-reactor-based WGS (ATWGS) process was still conceptual at the start of this project, but one of the tasks for this project was to evaluate the technical feasibility of this technology. In each of the three application-based comparison studies conducted as part of this project, the reference case was based on an existing Department of Energy National Energy Technology Laboratory (DOE/NETL) system study. Each of these references cases used existing commercial technology and the system resulted in > 90% carbon capture. In the comparison studies for the use of the hydrogen-rich syngas generated in either an Integrated Gasification Combined Cycle (IGCC) or a Coal-to-Methanol (CTM) plant, the comparison cases consisted of the reference case, a case with the integration of each individual advanced technology (either AR or RTI), and finally a case with the integration of all the advanced technologies (AR and RTI combined). In the Coal-to-Liquids (CTL) comparison study, the comparison study consisted of only three cases, which included a reference case, a case with just RTI’s advanced syngas cleaning technology, and a case with AR’s and RTI’s advanced technologies. The results from these comparison studies showed that the integration of the advanced technologies did result in substantial benefits, and by far the greatest benefits were achieved for cases integrating all the advanced technologies. For the IGCC study, the fully integrated case resulted in a 1.4% net efficiency improvement, an 18% reduction in capital cost per kW of capacity, a 12% reduction in the operating cost per kWh, and a 75–79% reduction in sulfur emissions. For the CTM case, the fully integrated plant resulted in a 22% reduction in capital cost, a 13% reduction in operating costs, a > 99% net reduction in sulfur emissions, and a reduction of 13–15% in CO 2 emissions. Because the capital cost represents over 60% of the methanol Required Selling Price (RSP), the significant reduction in the capital cost for the advanced technology case resulted in an 18% reduction in methanol RSP. For the CTL case, the fully integrated plant resulted in a 16% reduction in capital cost, which represented a 13% reduction in diesel RSP. Finally, the technical feasibility analysis of RTI’s ATWGS process demonstrated that a fluid-bed catalyst with sufficient attrition resistance and WGS activity could be made and that the process achieved about a 24% reduction in capital cost compared to a conventional fixed-bed commercial process.

08 HYDROGEN↗
DOE OSTI2017

NSR&D FY17 Report: CartaBlanca Capability Enhancements

Over the last several years, particle technology in the CartaBlanca code has been matured and has been successfully applied to a wide variety of physical problems. It has been shown that the particle methods, especially Los Alamos's dual domain material point method, is capable of computing many problems involves complex physics, chemistries accompanied by large material deformations, where the traditional finite element or Eulerian method encounter significant difficulties. In FY17, the CartaBlanca code has been enhanced with physical models and numerical algorithms. We started out to compute penetration and HE safety problems. Most of the year we focused on the TEPLA model improvement testing against the sweeping wave experiment by Gray et al., because it was found that pore growth and material failure are essentially important for our tasks and needed to be understood for modeling the penetration and the can experiments efficiently. We extended the TEPLA mode from the point view of ensemble phase average to include the effects of nite deformation. It is shown that the assumed pore growth model in TEPLA is actually an exact result from the theory. Alone this line, we then generalized the model to include finite deformations to consider nonlinear dynamics of large deformation. The interaction between the HE product gas and the solid metal is based on the multi-velocity formation. Our preliminary numerical results suggest good agreement between the experiment and the numerical results, pending further verification. To improve the parallel processing capabilities of the CartaBlanca code, we are actively working with the Next Generation Code (NGC) project to rewrite selected packages using C++. This work is expected to continue in the following years. This effort also makes the particle technology developed with CartaBlanca project available to other part of the laboratory. Working with the NGC project and rewriting some parts of the code also given us an opportunity to improve our numerical implementations of the method and to take advantage of recently advances in the numerical methods, such as multiscale algorithms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Surrogate Neural Architecture Codesign Package (SNAC-Pack)

Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost. This gap is particularly large for FPGA deployment, where cost is dominated by a multi-dimensional budget of lookup tables, DSPs, flip-flops, BRAM, and latency. We present the Surrogate Neural Architecture Codesign Package (SNAC-Pack), an open-source AutoML framework for hardware-aware neural architecture codesign and end-to-end FPGA deployment. SNAC-Pack runs a multi-objective global search with Optuna and NSGA-II, loading trials to a shared SQLite store that enables parallel workers across compute nodes. A hardware surrogate model outputs per-trial resource and latency estimates, avoiding the synthesis cost that would otherwise dominate the search loop. A local search stage then applies quantization-aware training (QAT) together with iterative magnitude pruning in a combined compression loop, after which the final model is synthesized to FPGA firmware via the hls4ml Python library. A YAML configuration and an optional agentic frontend let users run the pipeline on new datasets without modifying the framework. We demonstrate SNAC-Pack on jet classification at the Large Hadron Collider and superconducting qubit readout, discovering compact architectures that match or exceed strong baselines on the task metric while reducing FPGA resource utilization and, in the qubit readout case, reducing the design space exploration process from months of manual fine-tuning to hours of automated search.

Weitz, Jason [UC, San Diego]↗
DOE OSTI2017

Traleika Glacier: A hardware-software co-designed approach to exascale computing

The move from current petascale machines to future exascale machines will need both hardware improvements and software changes. Hardware will need to evolve to focus primarily on features that lower energy consumption: near-threshold voltage operation, fine-grained power and clock management and heterogeneity. Software will also need to evolve and be able to express more parallelism, become more dynamic and adaptable in order to be able to operate on a much more variable hardware. Here, we present Traleika Glacier, an effort that seeks to evaluate radical design changes to meet the constraints, both in terms of power and cost, of exascale computing. The salient features of the hardware design presented in the work include a) a use of heterogeneous cores, b) a redesign of the memory system that centers around hierarchical scratchpads and a global address space, c) the hardware acceleration of certain memory and network operations through specialized engines and, d) very fine-grained control and monitoring capabilities. On the software side, we describe a task-based runtime system, the Open Community Runtime (OCR) which aims to express a wide range of higher-level programming models with a very limited set of core concepts: event-driven tasks for computation, events for synchronization and relocatable data-blocks for data management.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1984

An operating system for future aerospace vehicle computer systems

The requirements for future aerospace vehicle computer operating systems are examined in this paper. The computer architecture is assumed to be distributed with a local area network connecting the nodes. Each node is assumed to provide a specific functionality. The network provides for communication so that the overall tasks of the vehicle are accomplished. The O/S structure is based upon the concept of objects. The mechanisms for integrating node unique objects with node common objects in order to implement both the autonomy and the cooperation between nodes is developed. The requirements for time critical performance and reliability and recovery are discussed. Time critical performance impacts all parts of the distributed operating system; e.g., its structure, the functional design of its objects, the language structure, etc. Throughout the paper the tradeoffs - concurrency, language structure, object recovery, binding, file structure, communication protocol, programmer freedom, etc. - are considered to arrive at a feasible, maximum performance design. Reliability of the network system is considered. A parallel multipath bus structure is proposed for the control of delivery time for time critical messages. The architecture also supports immediate recovery for the time critical message system after a communication failure.

Foudriat, E. C.↗
NASA NTRS2003

Evaluation of Alternate Concepts for Synthetic Vision Flight Displays With Weather-Penetrating Sensor Image Inserts During Simulated Landing Approaches

A simulation study was conducted in 1994 at Langley Research Center that used 12 commercial airline pilots repeatedly flying complex Microwave Landing System (MLS)-type approaches to parallel runways under Category IIIc weather conditions. Two sensor insert concepts of 'Synthetic Vision Systems' (SVS) were used in the simulated flights, with a more conventional electro-optical display (similar to a Head-Up Display with raster capability for sensor imagery), flown under less restrictive visibility conditions, used as a control condition. The SVS concepts combined the sensor imagery with a computer-generated image (CGI) of an out-the-window scene based on an onboard airport database. Various scenarios involving runway traffic incursions (taxiing aircraft and parked fuel trucks) and navigational system position errors (both static and dynamic) were used to assess the pilots' ability to manage the approach task with the display concepts. The two SVS sensor insert concepts contrasted the simple overlay of sensor imagery on the CGI scene without additional image processing (the SV display) to the complex integration (the AV display) of the CGI scene with pilot-decision aiding using both object and edge detection techniques for detection of obstacle conflicts and runway alignment errors.

Parrish, Russell V.↗
DOE OSTI2016

A Fault-oblivious Extreme-scale Execution Environment

Exascale computing systems will provide a thousand-fold increase in parallelism and a proportional increase in failure rate relative to today's machines. Systems software for exascale machines must provide the infrastructure to support existing applications while simultaneously enabling efficient execution of new programming models that naturally express dynamic, adaptive, irregular computation; coupled simulations; and massive data analysis in a highly unreliable hardware environment with billions of threads of execution. We propose a new approach to the data and work distribution model provided by system software based on the unifying formalism of an abstract file system. The proposed hierarchical data model provides simple, familiar visibility and access to data structures through the file system hierarchy, while providing fault tolerance through selective redundancy. The hierarchical task model features work queues whose form and organization are represented as file system objects. Data and work are both first class entities. By exposing the relationships between data and work to the runtime system, information is available to optimize execution time and provide fault tolerance. The data distribution scheme provides replication (where desirable and possible) for fault tolerance and efficiency, and it is hierarchical to make it possible to take advantage of locality. The user, tools, and applications, including legacy applications, can interface with the data, work queues, and one another through the abstract file model. This runtime environment will provide multiple interfaces to support traditional Message Passing Interface applications, languages developed under DARPA's High Productivity Computing Systems program, as well as other, experimental programming models. We will validate our runtime system with pilot codes on existing platforms and will use simulation to validate for exascale-class platforms. In this final report, we summarize research results from the work done at the Ohio State University towards the larger goals of the project listed above.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Enabling Highly Efficient Capsule Networks Processing Through A PIM-Based Architecture Design

In recent years, the CNNs have achieved great successes in the image processing tasks, e.g., image recognition and object detection. Unfortunately, traditional CNN's classication is found to be easily misled by increasingly complex image features due to the usage of pooling operations, hence unable to preserve accurate position and pose information of the objects. To address this challenge, a novel neural network structure called Capsule Network has been proposed, which introduces equivariance through capsules to signicantly enhance the learning ability for image segmentation and object detection. Due to its requirement of performing a high volume of matrix operations, CapsNets have been generally accelerated on modern GPU platforms that provide highly optimized software library for common deep learning tasks. However, based on our performance characterization on modern GPUs, CapsNets exhibit low effciency due to the special program and execution features of their routing procedure, including massive unshareable intermediate variables and intensive syn- chronizations, which are very dicult to optimize at software level. To address these challenges, we propose a hybrid computing architecture design named PIM-CapsNet. It preserves GPU's on-chip computing capability for accelerating CNN types of layers in CapsNet, while pipelining with an off-chip in-memory acceleration solution that effectively tackles routing procedure's ineffciency by leveraging the processing-in-memory capability of today's 3D stacked memory. Using routing procedure's inherent parallellization feature, our design enables hierarchical improvements on CapsNet inference effciency through minimizing data movement and maximizing parallel processing in memory. Evaluation results demonstrate that our proposed design can achieve substantial improvement on both performance and energy savings for CapsNet inference, with almost zero accuracy loss. The results also suggest good performance scalability in optimizing the routing procedure with increasing network size.

Zhang, Xingyao↗
NASA NTRS2012

JELC-LITE: Unconventional Instructional Design for Special Operations Training

Current special operations staff training is based on the Joint Event Life Cycle (JELC). It addresses operational level tasks in multi-week, live military exercises which are planned over a 12 to 18 month timeframe. As the military experiences changing global mission sets, shorter training events using distributed technologies will increasingly be needed to augment traditional training. JELC-Lite is a new approach for providing relevant training between large scale exercises. This new streamlined, responsive training model uses distributed and virtualized training technologies to establish simulated scenarios. It keeps proficiency levels closer to optimal levels -- thereby reducing the performance degradation inherent in periodic training. It can be delivered to military as well as under-reached interagency groups to facilitate agile, repetitive training events. JELC-Lite is described by four phases paralleling the JELC, differing mostly in scope and scale. It has been successfully used with a Theater Special Operations Command and fits well within the current environment of reduced personnel and financial resources.

Friedman, Mark↗
DOE OSTI2015

Stable large-scale solver for Ginzburg–Landau equations for superconductors

Understanding the interaction of vortices with inclusions in type-II superconductors is a major outstanding challenge both for fundamental science and energy applications. At application-relevant scales, the long-range interactions between a dense configuration of vortices and the dependence of their behavior on external parameters, such as temperature and an applied magnetic field, are all important to the net response of the superconductor. Capturing these features, in general, precludes analytical description of vortex dynamics and has also made numerical simulation prohibitively expensive. Here we report on a highly optimized iterative implicit solver for the time-dependent Ginzburg-Landau equations suitable for investigations of type-II superconductors on massively parallel architectures. Its main purpose is to study vortex dynamics in disordered or geometrically confined mesoscopic systems. In this work, we present the discretization and time integration scheme in detail for two types of boundary conditions. We describe the necessary conditions for a stable and physically accurate integration of the equations of motion. Using an inclusion pattern generator, we can simulate complex pinning landscapes and the effect of geometric confinement. We show that our algorithm, implemented on a GPU, can provide static and dynamic solutions of the Ginzburg-Landau equations for mesoscopically large systems over thousands of time steps in a matter of hours. Using our formulation, studying scientifically-relevant problems is a computationally reasonable task.

Sadovskyy, I. A.↗
DOE OSTI2022

Metaplastic and energy-efficient biocompatible graphene artificial synaptic transistors for enhanced accuracy neuromorphic computing

CMOS-based computing systems that employ the von Neumann architecture are relatively limited when it comes to parallel data storage and processing. In contrast, the human brain is a living computational signal processing unit that operates with extreme parallelism and energy efficiency. Although numerous neuromorphic electronic devices have emerged in the last decade, most of them are rigid or contain materials that are toxic to biological systems. In this work, we report on biocompatible bilayer graphene-based artificial synaptic transistors (BLAST) capable of mimicking synaptic behavior. The BLAST devices leverage a dry ion-selective membrane, enabling long-term potentiation, with ~50 aJ/µm 2 switching energy efficiency, at least an order of magnitude lower than previous reports on two-dimensional material-based artificial synapses. The devices show unique metaplasticity, a useful feature for generalizable deep neural networks, and we demonstrate that metaplastic BLASTs outperform ideal linear synapses in classic image classification tasks. With switching energy well below the 1 fJ energy estimated per biological synapse, the proposed devices are powerful candidates for bio-interfaced online learning, bridging the gap between artificial and biological neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Validating the simulation of large-scale parallel applications using statistical characteristics

Simulation is a widely adopted method to analyze and predict the performance of large-scale parallel applications. Validating the hardware model is highly important for complex simulations with a large number of parameters. Common practice involves calculating the percent error between the projected and the real execution time of a benchmark program. However, in a high-dimensional parameter space, this coarse-grained approach often suffers from parameter insensitivity, which may not be known a priori. Moreover, the traditional approach cannot be applied to the validation of software models, such as application skeletons used in online simulations. In this work, we present a methodology and a toolset for validating both hardware and software models by quantitatively comparing fine-grained statistical characteristics obtained from execution traces. Although statistical information has been used in tasks like performance optimization, this is the first attempt to apply it to simulation validation. Lastly, our experimental results show that the proposed evaluation approach offers significant improvement in fidelity when compared to evaluation using total execution time, and the proposed metrics serve as reliable criteria that progress toward automating the simulation tuning process.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

Feasibility study for the implementation of NASTRAN on the ILLIAC 4 parallel processor

The ILLIAC IV, a fourth generation multiprocessor using parallel processing hardware concepts, is operational at Moffett Field, California. Its capability to excel at matrix manipulation, makes the ILLIAC well suited for performing structural analyses using the finite element displacement method. The feasibility of modifying the NASTRAN (NASA structural analysis) computer program to make effective use of the ILLIAC IV was investigated. The characteristics are summarized of the ILLIAC and the ARPANET, a telecommunications network which spans the continent making the ILLIAC accessible to nearly all major industrial centers in the United States. Two distinct approaches are studied: retaining NASTRAN as it now operates on many of the host computers of the ARPANET to process the input and output while using the ILLIAC only for the major computational tasks, and installing NASTRAN to operate entirely in the ILLIAC environment. Though both alternatives offer similar and significant increases in computational speed over modern third generation processors, the full installation of NASTRAN on the ILLIAC is recommended. Specifications are presented for performing that task with manpower estimates and schedules to correspond.

Field, E. I.↗
DOE OSTI2017

Performance Analysis of OpenSHMEM Applications with TAU Commander

The TAU Performance System® (TAU) is a powerful and highly versatile profiling and tracing tool ecosystem for performance engineering of parallel programs. Developed over the last twenty years, TAU has evolved with each new generation of HPC systems and scales efficiently to hundreds of thousands of cores. TAU’s organic growth has resulted in a loosely coupled software toolbox such that novice users first encountering TAU’s complexity and vast array of features are often intimidated and easily frustrated. To lower the barrier to entry for novice TAU users, ParaTools and the US Department of Energy have developed “TAU Commander,” a performance engineering workflow manager that facilitates a systematic approach to performance engineering, guides users through common profiling and tracing workflows, and offers constructive feedback in case of error. This work compares TAU and TAU Commander workflows for common performance engineering tasks in OpenSHMEM applications and demonstrates workflows targeting two different SHMEM implementations, Intel Xeon “Haswell” and “Knights Landing” processors, direct and indirect measurement methods, callsite, profiles, and traces.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Neural network methods for radiation detectors and imaging

Recent advances in image data proccesing through deep learning allow for new optimization and performance-enhancement schemes for radiation detectors and imaging hardware. This enables radiation experiments, which includes photon sciences in synchrotron and X-ray free electron lasers as a subclass, through data-endowed artificial intelligence. We give an overview of data generation at photon sources, deep learning-based methods for image processing tasks, and hardware solutions for deep learning acceleration. Most existing deep learning approaches are trained offline, typically using large amounts of computational resources. However, once trained, DNNs can achieve fast inference speeds and can be deployed to edge devices. A new trend is edge computing with less energy consumption (hundreds of watts or less) and real-time analysis potential. While popularly used for edge computing, electronic-based hardware accelerators ranging from general purpose processors such as central processing units (CPUs) to application-specific integrated circuits (ASICs) are constantly reaching performance limits in latency, energy consumption, and other physical constraints. These limits give rise to next-generation analog neuromorhpic hardware platforms, such as optical neural networks (ONNs), for high parallel, low latency, and low energy computing to boost deep learning acceleration (LA-UR-23-32395).

edge computing↗
DOE OSTI2022

Exploring the Use of Novel Spatial Accelerators in Scientific Applications

Driven by the need to find alternative accelerators which can viably replace GPUs in next-generation Supercomputing systems, this paper proposes a methodology to enable agile application/hardware co-design. The application-first methodology provides the ability to come up with design of accelerators while working with real-world workloads, available accelerators, and system software. The iterative design process targets a set of kernels in a workload for performance estimates that can prune the design space for later phases of detailed architectural evaluations. To this effect, in this paper, a novel data-parallel device model is introduced that simulates the latency of performance-sensitive operations in an accelerator including data transfers and kernel computation using multi-core CPUs. The use of off-the-shelf simulators, such as pre-RTL simulator Aladdin or multiple tools available for exploring the design of deep neural network accelerators (e.g., Timeloop) is demonstrated for evaluation of various accelerator designs using applications with realistic inputs. Examples of multiple device configurations that are instantiable in a system are explored to evaluate the performance benefit of deploying novel accelerators. The proposed device is integrated with a programming model and system software to potentially explore the impacts of high-level programming languages/compilers and low-level effects such as task scheduling on multiple accelerators. We analyze our methodology for a set of applications that represent high-performance computing (HPC) and graph analytics. The applications include a computational chemistry kernel realized using tensor contractions, triangle counting, GraphSAGE and Breadth-first Search. These applications include kernels such as dense matrix-dense matrix multiplication, sparse matrix-spare matrix multiplication, and sparse matrix-dense vector multiplication. Our results indicate potential performance benefits and insights for system design by including accelerators that realize these kernels along-side general purpose accelerators.

AI, codesign, Accelerated Computing, Modeling and ↗
NASA NTRS1999

ADPAC v1.0: User's Manual

The overall objective of this study was to evaluate the effects of turbulence models in a 3-D numerical analysis on the wake prediction capability. The current version of the computer code resulting from this study is referred to as ADPAC v7 (Advanced Ducted Propfan Analysis Codes -Version 7). This report is intended to serve as a computer program user's manual for the ADPAC code used and modified under Task 15 of NASA Contract NAS3-27394. The ADPAC program is based on a flexible multiple-block and discretization scheme permitting coupled 2-D/3-D mesh block solutions with application to a wide variety of geometries. Aerodynamic calculations are based on a four-stage Runge-Kutta time-marching finite volume solution technique with added numerical dissipation. Steady flow predictions are accelerated by a multigrid procedure. Turbulence models now available in the ADPAC code are: a simple mixing-length model, the algebraic Baldwin-Lomax model with user defined coefficients, the one-equation Spalart-Allmaras model, and a two-equation k-R model. The consolidated ADPAC code is capable of executing in either a serial or parallel computing mode from a single source code.

Hall, Edward J.↗
NASA NTRS1987

Knowledge-based vision for space station object motion detection, recognition, and tracking

Computer vision, especially color image analysis and understanding, has much to offer in the area of the automation of Space Station tasks such as construction, satellite servicing, rendezvous and proximity operations, inspection, experiment monitoring, data management and training. Knowledge-based techniques improve the performance of vision algorithms for unstructured environments because of their ability to deal with imprecise a priori information or inaccurately estimated feature data and still produce useful results. Conventional techniques using statistical and purely model-based approaches lack flexibility in dealing with the variabilities anticipated in the unstructured viewing environment of space. Algorithms developed under NASA sponsorship for Space Station applications to demonstrate the value of a hypothesized architecture for a Video Image Processor (VIP) are presented. Approaches to the enhancement of the performance of these algorithms with knowledge-based techniques and the potential for deployment of highly-parallel multi-processor systems for these algorithms are discussed.

Symosek, P.↗