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Parallel Computing 1988: Volume 7 , Issue Index. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1988-03_6_3 . Next issue: sim_parallel-computing_1988-04_7_1 .

Parallel Computing 1988: Volume 7 , Issue Index. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1988-03_6_3 . Next issue: sim_parallel-computing_1988-04_7_1 .

Parallel Computing 1988: Volume 7 , Issue Index. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1988-03_6_3 . Next issue: sim_parallel-computing_1988-04_7_1 .

First Class

First Class

Given the recent progress in the evolution of high-performance computing (HPC) technologies, the research in computational intelligence has entered a new era. In this paper, we present an HPC-based context-aware intelligent text recognition system (ITRS) that serves as the physical layer of machine reading. A parallel computing architecture is adopted that incorporates the HPC technologies with advances in neuromorphic computing models. The algorithm learns from what has been read and, based on the obtained knowledge, it forms anticipations of the word and sentence level context. The information processing flow of the ITRS imitates the function of the neocortex system. It incorporates large number of simple pattern detection modules with advanced information association layer to achieve perception and recognition. Such architecture provides robust performance to images with large noise. The implemented ITRS software is able to process about 16 to 20 scanned pages per second on the 500 trillion floating point operations per second (TFLOPS) Air Force Research Laboratory (AFRL)/Information Directorate (RI) Condor HPC after performance optimization.

We show the use of a reconfigurable computer in computing the correlation immunity of Boolean functions of up to 6 variables. Boolean functions with high correlation immunity are desired in cryptographic systems because they are immune to correlation attacks. The SRC-6 reconfigurable computer was programmed in Verilog to compute the correlation immunity of functions. This computation is performed at a rate that is 190 times faster than a conventional computer. Our analysis of the correlation immunity is across all n-variable Boolean functions for 2 less than or equal to n less than or equal to 6, thus obtaining, for the first time, a complete distribution of such functions. We also compare correlation immunity with two other cryptographic properties nonlinearity and degree.

We show the use of a reconfigurable computer in computing the correlation immunity of Boolean functions of up to 6 variables. Boolean functions with high correlation immunity are desired in cryptographic systems because they are immune to correlation attacks. The SRC-6 reconfigurable computer was programmed in Verilog to compute the correlation immunity of functions. This computation is performed at a rate that is 190 times faster than a conventional computer. Our analysis of the correlation immunity is across all n-variable Boolean functions for 2 less than or equal to n less than or equal to 6, thus obtaining, for the first time, a complete distribution of such functions. We also compare correlation immunity with two other cryptographic properties nonlinearity and degree.

Perma.cc archive of https://www.intel.com/content/www/us/en/developer/articles/community/parallel-computing-center-at-lamda-group-nanjing-university.html created on 2022-07-11 15:02:21.913891+00:00.

A powerful high-performance computer program for simulating and analyzing adaptive and controlled optical systems has been developed by modifying the serial version of the Modeling and Analysis for Controlled Optical Systems (MACOS) program to impart capabilities for multithreaded parallel processing on computing systems ranging from supercomputers down to Symmetric Multiprocessing (SMP) personal computers. The modifications included the incorporation of OpenMP, a portable and widely supported application interface software, that can be used to explicitly add multithreaded parallelism to an application program under a shared-memory programming model. OpenMP was applied to parallelize ray-tracing calculations, one of the major computing components in MACOS. Multithreading is also used in the diffraction propagation of light in MACOS based on pthreads [POSIX Thread, (where "POSIX" signifies a portable operating system for UNIX)]. In tests of the parallelized version of MACOS, the speedup in ray-tracing calculations was found to be linear, or proportional to the number of processors, while the speedup in diffraction calculations ranged from 50 to 60 percent, depending on the type and number of processors. The parallelized version of MACOS is portable, and, to the user, its interface is basically the same as that of the original serial version of MACOS.

This software computes the tomographic reconstruction of spatial-spectral data from raw detector images of the Computed-Tomography Imaging Spectrometer (CTIS), which enables transient-level, multi-spectral imaging by capturing spatial and spectral information in a single snapshot.

This article is from PLoS ONE , volume 9 . Abstract The metagenomic method directly sequences and analyses genome information from microbial communities. The main computational tasks for metagenomic analyses include taxonomical and functional structure analysis for all genomes in a microbial community (also referred to as a metagenomic sample). With the advancement of Next Generation Sequencing (NGS) techniques, the number of metagenomic samples and the data size for each sample are increasing rapidly. Current metagenomic analysis is both data- and computation- intensive, especially when there are many species in a metagenomic sample, and each has a large number of sequences. As such, metagenomic analyses require extensive computational power. The increasing analytical requirements further augment the challenges for computation analysis. In this work, we have proposed Parallel-META 2.0, a metagenomic analysis software package, to cope with such needs for efficient and fast analyses of taxonomical and functional structures for microbial communities. Parallel-META 2.0 is an extended and improved version of Parallel-META 1.0, which enhances the taxonomical analysis using multiple databases, improves computation efficiency by optimized parallel computing, and supports interactive visualization of results in multiple views. Furthermore, it enables functional analysis for metagenomic samples including short-reads assembly, gene prediction and functional annotation. Therefore, it could provide accurate taxonomical and functional analyses of the metagenomic samples in high-throughput manner and on large scale.

Trimester 2 - 2018/2019

Trimester 2 2016/2017

Trimester 1 2015/2016

IPython: A New Architecture for Interactive and Parallel Computing Fernando Perez

IPython: A New Architecture for Interactive and Parallel Computing Fernando Perez

IPython: A New Architecture for Interactive and Parallel Computing Fernando Perez

IPython: A New Architecture for Interactive and Parallel Computing Fernando Perez

viii, 203 p. : 24 cm

Generation of optimal codes is a well known problem in coding theory. Many computational approaches exist in the literature for finding record breaking codes. However generating codes with long lengths $n$ using serial algorithms is computationally very expensive, for example the worst case time complexity of a Greedy algorithm is $\mathcal{O}(n\; 4^n)$. In order to improve the efficiency of generating codes with long lengths, we propose and investigate some parallel algorithms using General Purpose Graphic Processing Units (GPGPU). This paper considers the implementation of parallel Greedy algorithm using GPGPU-CUDA (Computed Unified Device Architecture) framework and discusses various optimization techniques to accelerate the GPU code. The performance achieved for optimized parallel implementations is more than two to three orders of magnitude faster than that of serial implementation and shows a great potential of GPGPU in the field of coding theory applications.

This effort produced an approach for developing software for parallel processing systems based on the parallel object-oriented functional computational model (PROOF). A benefit of this approach is that it allows the software development to be separated from architecture-dependent issues to a great degree. For example, the programmers do not need to be concerned with issues such as synchronization, parallelization, or the topology of the parallel processing system when they address objects in the system. The object-oriented paradigm reflects the parallel structure of the problem space and can represent inherently concurrent behavior. This approach is suitable for MIMD machines and should result in software that is more understandable, modifiable and portable over a variety of parallel computer architectures.

This report compiles the results of army research in the area of modeling asynchronous parallel architectures and computation for applications in the areas of digital image and signal processing. The work can be broadly divided into three areas: (1) Case studies of parallel image processing algorithms and tasks, the objective of which is to study the interaction of parallel processes and parallel architectures; (2) Modeling of interconnection networks and (3) Aspects of the problem of modeling parallel processes and parallel architectures.

Two methods are used to speed up the execution of a computational task. One is new technology development and the other is the exploitation of parallelism in the computation. To take an advantage of the parallelism in a task requires the utilization of parallel computer architectures. At a certain high level of abstraction a parallel computer system is represented as a graph where the nodes represent processors, memories, or other devices, and the edges represent the communication links. In this thesis the following problems of parallel processing are studied. First is a theoretical study of topological properties of interconnection networks. Second is a case study of a network design for a real-time system. Lastly, the use of SIMD(Single Instruction Stream Multiple Data Stream) networks for performing 'shuffles'. A general model that can be used to describe networks and systems with arbitrary topologies is developed. Based upon the of morphism of groups, the concept of morphism of systems is developed. The morphism of systems is called quasimorphism and allows a method of comparison between topologically arbitrary parallel computer systems. The quasimorphism is used to study the emulation of one system by another.

Parallel Computing 1990: Volume 13 , Issue Index. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1989-12_12_3 . Next issue: sim_parallel-computing_1990-01_13_1 .

While modern parallel computing systems provide high performance resources, utilizing them to the highest extent requires advanced programming expertise. Programming for parallel computing systems is much more difficult than programming for sequential systems. OpenMP is an extension of C++ programming language that enables to express parallelism using compiler directives. While OpenMP alleviates parallel programming by reducing the lines of code that the programmer needs to write, deciding how and when to use these compiler directives is up to the programmer. Novice programmers may make mistakes that may lead to performance degradation or unexpected program behavior. Cognitive computing has shown impressive results in various domains, such as health or marketing. In this paper, we describe the use of IBM Watson cognitive system for education of novice parallel programmers. Using the dialogue service of the IBM Watson we have developed a solution that assists the programmer in avoiding common OpenMP mistakes. To evaluate our approach we have conducted a survey with a number of novice parallel programmers at the Linnaeus University, and obtained encouraging results with respect to usefulness of our approach.

Scalable Parallel Computing on Many/Multicore Systems This set of lectures will review the application and programming model issues that will one must address when one gets chips with 32-1024 cores and “scalable” approaches will be needed to make good use of such systems. We will not discuss bit-level and instruction-level parallelism i.e. what happens on a possibly special purpose core, even though this is clearly important. We will use science and engineering applications to drive the discussion as we have substantial experience in these cases but we are interested in the lessons for commodity client and server applications that will be broadly used 5-10 years from now. We start with simple applications and algorithms from a variety of fields and identify features that makes it “obvious” that “all” science and engineering run well and scalably in parallel. We explain why unfortunately it is equally obvious that there is no straightforward way of expressing this parallelism. Parallel hardware architectures are described in enough detail to understand performance and algorithm issues and need for cross-architecture compatibility; however in these lectures we will just be users of hardware. We must understand what features of multicore chips we can and should exploit. We can explicitly use the shared memory between cores or just enjoy its implications for very fast inter-core control and communication linkage. We note that parallel algorithm research is hugely successful although this success has reduced activity in an area that deserves new attention for the next generation of architectures. The parallel software environment is discussed at several levels including programming paradigm, runtime and operating system. The importance of libraries, templates, kernels (dwarfs) and benchmarks is stressed. The programming environment has various tools including compilers with possible parallelism hints like OpenMP; tuners like Atlas; messaging models; parallelism and distribution support as in HPF, HPCS Languages, co-array Fortran, Global arrays and UPC. We also discuss the relevance of important general ideas like object-oriented paradigms (as in for example Charm++), functional languages and Software Transactional Memories. Streaming, pipelining, co-ordination, services and workflow are placed in context. Examples discussed in the last category include CCR/DSS from Microsoft and the Common Component Architecture CCA from DoE. Domain Specific environments like Matlab and Mathematica are important as there is no universal silver programming bullet; one will need interoperable focused environments. We discuss performance analysis including speedup, efficiency, scaled speedup and Amdahl's law. We show how to relate performance to algorithm/application structure and the hardware characteristics. Applications will not get scalable parallelism accidentally but only if there is an understandable performance model. We review some of the many pitfalls for both performance and correctness; these include deadlocks, race conditions, nodes that are busy doing something else and the difficulty of second-guessing automatic parallelization methods. We describe the formal sources of overhead; load imbalance and the communication/control overhead and the ways to reduce them. We relate the blocking used in caching to that used in parallel computation. We note that load balancing was the one part of parallel computing that was easier than expected. We will mix both simple idealized applications with “real problems” noting that usually it is simple problems that are the hardest as they have poor computation to control/communication ratios. We will explain the parallelism in several application classes including for science and engineering: Finite Difference, Finite Elements, FFT/Spectral, Meshes of all sorts, Particle Dynamics, Particle-Mesh, and Monte Carlo methods. Some applications like Image Processing, Graphics, Cryptography and Media coding/decoding have features similar to well understood science and engineering problems. We emphasize that nearly all applications are built hierarchically from more “basic applications” with a variety of different structures and natural programming models. Such application composition (co-ordination, workflow) must be supported with a common run-time. We contrast the difference between decomposition needed in most “basic parallel applications” to the composition supported in workflow and Web 2.0 mashups. Looking at broader application classes we should cover; Internet applications and services; artificial intelligence, optimization, machine learning; divide and conquer algorithms; tree structured searches like computer chess; applications generated by the data deluge including access, search, and the assimilation of data into simulations. There has been a growing interest in Complex Systems whether it be for critical infrastructure like energy and transportation, the Internet itself, commodity gaming, computational epidemiology or the original war games. We expect Discrete Event Simulations (such as DoD’s High Level Architecture HLA) to grow in importance as they naturally describe complex systems and because they can clearly benefit from multicore architectures. We will discuss the innovative Sequential Dynamical Systems approach used in the EpiSims, TransSims and other critical infrastructure simulation environments. In all applications we need to identify the intrinsic parallelism and the degrees of freedom that can be parallelized and distinguish small parallelism (local to core) compared to large parallelism (scalable across many cores) We will not forget many critical non-technical Issues including “Who programs – everybody or just the marine corps?”; “The market for science and engineering was small but it will be large for general multicore”; “The program exists and can’t be changed”; “What features will next hardware/software release support and how should I protect myself from change?” We will summarize lessons and relate them to application and programming model categories. In last lecture or at end of all lectures we encourage the audience to bring their own multicore application or programming model so we can discuss examples that interest you. ©2007 Microsoft Corporation. All rights reserved.

Parallel Computing is an active peer-reviewed academic journal covering the theory and use of parallel computer systems, including vector, pipeline, array and fifth-generation computers, as well as high-speed computing. It was established in 1984 and is published 10 times a year (monthly until 2014) by Elsevier. The journal serves as a platform for researchers, computer scientists, engineers, and practitioners to publish original research findings, methodologies, and innovations in the field of parallel computing. It covers a wide range of topics, including parallel algorithms and programming models, parallel architectures and systems, parallel software and tools, performance analysis and evaluation, parallel applications in various domains, and emerging trends in parallel computing. The journal encourages submissions that address both theoretical foundations and practical aspects of parallel computing, including scalability, fault tolerance, load balancing, and efficient resource utilisation as well as studies that explore parallel computing in areas such as scientific simulations, data analytics, machine learning, image and signal processing, and computational biology. The Internet Archive Collection contains microfilm published between 1984 and 1994 Parallel Computing can be found online at: www.sciencedirect.com/journal/parallel-computing The ISSN is: 0167-8191

Recently, networked and cluster computation have become very popular. This paper is an introduction to a new C based parallel language for architecture-adaptive programming, aCe C. The primary purpose of aCe (Architecture-adaptive Computing Environment) is to encourage programmers to implement applications on parallel architectures by providing them the assurance that future architectures will be able to run their applications with a minimum of modification. A secondary purpose is to encourage computer architects to develop new types of architectures by providing an easily implemented software development environment and a library of test applications. This new language should be an ideal tool to teach parallel programming. In this paper, we will focus on some fundamental features of aCe C.

Parallel Computing 1984: Volume 1 , Issue Index. Digitized from IA1652917-03 . Next issue: sim_parallel-computing_1984-08_1_1 .

The Microsoft .NET Framework 4 and Visual Studio 2010 include new technologies for expressing, debugging, and tuning parallelism in managed applications. Dive into key areas of support, including Parallel Language Integrated Query (PLINQ), cutting-edge concurrency views in the Visual Studio profiler, and debugger tool windows for analyzing the state of concurrent code. In addition to exploring such features, we will examine some common parallel patterns prevalent in technical computing and how these features can be used to best implement such patterns. ©2010 Microsoft Corporation. All rights reserved.

The Microsoft .NET Framework 4 and Visual Studio 2010 include new technologies for expressing, debugging, and tuning parallelism in managed applications. Dive into key areas of support, including Parallel Language Integrated Query (PLINQ), cutting-edge concurrency views in the Visual Studio profiler, and debugger tool windows for analyzing the state of concurrent code. In addition to exploring such features, we will examine some common parallel patterns prevalent in technical computing and how these features can be used to best implement such patterns. ©2010 Microsoft Corporation. All rights reserved.

ai turing parabellum glupek

The James Webb Space Telescope (JWST) is the successor to the Hubble Space Telescope and will be NASA's premier observatory of the next decade. Image-based wavefront sensing (phase retrieval) is the primary method for ground testing and on-orbit commissioning. For ground tests at NASA's Goddard Space Flight Center (GSFC) and Johnson Space Center (JSC), near-real-time analysis is critical for ensuring that pass/fail criteria are met before completion of a specific test. To address this need we have developed a computational architecture for image processing and phase retrieval. Using commercially available off-the-shelf hardware and software, we have designed, implemented, and tested a solution for high-speed parallel computing. The architecture is a hybrid solution utilizing both CPUs and GPUs and exploiting the unique advantages of each. Discussions are presented of the architecture, performance, and current limitations.

788 p. ; 27 cm

788 p. ; 27 cm

Establishing a test-bed for clustered parallel computing is reported along with performance evaluation of various clusters for a number of applications/parallel algorithms.

The problem of optimally assigning the modules of a parallel program over the processors of a multiple computer system is addressed. A Sum-Bottleneck path algorithm is developed that permits the efficient solution of many variants of this problem under some constraints on the structure of the partitions. In particular, the following problems are solved optimally for a single-host, multiple satellite system: partitioning multiple chain structured parallel programs, multiple arbitrarily structured serial programs and single tree structured parallel programs. In addition, the problems of partitioning chain structured parallel programs across chain connected systems and across shared memory (or shared bus) systems are also solved under certain constraints. All solutions for parallel programs are equally applicable to pipelined programs. These results extend prior research in this area by explicitly taking concurrency into account and permit the efficient utilization of multiple computer architectures for a wide range of problems of practical interest.

The problem of optimally assigning the modules of a parallel program over the processors of a multiple computer system is addressed. A Sum-Bottleneck path algorithm is developed that permits the efficient solution of many variants of this problem under some constraints on the structure of the partitions. In particular, the following problems are solved optimally for a single-host, multiple satellite system: partitioning multiple chain structured parallel programs, multiple arbitrarily structured serial programs and single tree structured parallel programs. In addition, the problems of partitioning chain structured parallel programs across chain connected systems and across shared memory (or shared bus) systems are also solved under certain constraints. All solutions for parallel programs are equally applicable to pipelined programs. These results extend prior research in this area by explicitly taking concurrency into account and permit the efficient utilization of multiple computer architectures for a wide range of problems of practical interest.

Perma.cc archive of https://en.wikipedia.org/wiki/Parallel_computing created on 2022-08-13 07:53:19.464194+00:00.

Perma.cc archive of https://en.wikipedia.org/wiki/Parallel_computing created on 2022-08-13 07:53:19.464194+00:00.

Perma.cc archive of https://en.wikipedia.org/wiki/Parallel_computing created on 2022-08-13 07:53:19.464194+00:00.

Perma.cc archive of https://en.wikipedia.org/wiki/Parallel_computing created on 2022-08-13 07:53:19.464194+00:00.

Perma.cc archive of https://en.wikipedia.org/wiki/Parallel_computing created on 2022-08-13 07:53:19.464194+00:00.

Parallel Computing 1988 - 1989: Volume 9 , Issue Index. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1988-10_8_1-3 . Next issue: sim_parallel-computing_1988-12_9_1 .