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Parallel Computing 1990: Volume 15 , Issue CONTENTS. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1990_15_index . Next issue: sim_parallel-computing_1990-09_15_1-3 .

Parallel Computing 1990: Volume 15 , Issue CONTENTS. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1990_15_index . Next issue: sim_parallel-computing_1990-09_15_1-3 .

Parallel Computing 1990: Volume 15 , Issue CONTENTS. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1990_15_index . Next issue: sim_parallel-computing_1990-09_15_1-3 .

xviii,292p. ; 23 cm

xviii,292p. ; 23 cm

First Class

Trimester 2 - 2015 / 2016

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.

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

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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

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

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

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: Volume 8 , Issue Index. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1988-09_7_3 . Next issue: sim_parallel-computing_1988_8_contents .

Parallel Computing 1988: Volume 8 , Issue Index. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1988-09_7_3 . Next issue: sim_parallel-computing_1988_8_contents .

We present an efficient distributed memory parallel algorithm for computing connected components in undirected graphs based on Shiloach-Vishkin's PRAM approach. We discuss multiple optimization techniques that reduce communication volume as well as load-balance the algorithm. We also note that the efficiency of the parallel graph connectivity algorithm depends on the underlying graph topology. Particularly for short diameter graph components, we observe that parallel Breadth First Search (BFS) method offers better performance. However, running parallel BFS is not efficient for computing large diameter components or large number of small components. To address this challenge, we employ a heuristic that allows the algorithm to quickly predict the type of the network by computing the degree distribution and follow the optimal hybrid route. Using large graphs with diverse topologies from domains including metagenomics, web crawl, social graph and road networks, we show that our hybrid implementation is efficient and scalable for each of the graph types. Our approach achieves a runtime of 215 seconds using 32K cores of Cray XC30 for a metagenomic graph with over 50 billion edges. When compared against the previous state-of-the-art method, we see performance improvements up to 24x.

As the Large Hadron Collider (LHC) continues its upward progression in energy and luminosity towards the planned High-Luminosity LHC (HL-LHC) in 2025, the challenges of the experiments in processing increasingly complex events will also continue to increase. Improvements in computing technologies and algorithms will be a key part of the advances necessary to meet this challenge. Parallel computing techniques, especially those using massively parallel computing (MPC), promise to be a significant part of this effort. In these proceedings, we discuss these algorithms in the specific context of a particularly important problem: the reconstruction of charged particle tracks in the trigger algorithms in an experiment, in which high computing performance is critical for executing the track reconstruction in the available time. We discuss some areas where parallel computing has already shown benefits to the LHC experiments, and also demonstrate how a MPC-based trigger at the CMS experiment could not only improve performance, but also extend the reach of the CMS trigger system to capture events which are currently not practical to reconstruct at the trigger level.

As the Large Hadron Collider (LHC) continues its upward progression in energy and luminosity towards the planned High-Luminosity LHC (HL-LHC) in 2025, the challenges of the experiments in processing increasingly complex events will also continue to increase. Improvements in computing technologies and algorithms will be a key part of the advances necessary to meet this challenge. Parallel computing techniques, especially those using massively parallel computing (MPC), promise to be a significant part of this effort. In these proceedings, we discuss these algorithms in the specific context of a particularly important problem: the reconstruction of charged particle tracks in the trigger algorithms in an experiment, in which high computing performance is critical for executing the track reconstruction in the available time. We discuss some areas where parallel computing has already shown benefits to the LHC experiments, and also demonstrate how a MPC-based trigger at the CMS experiment could not only improve performance, but also extend the reach of the CMS trigger system to capture events which are currently not practical to reconstruct at the trigger level.

The use of computers in statistical physics is common because the sheer number of equations that describe the behavior of an entire system particle by particle often makes it impossible to solve them exactly. Monte Carlo methods form a particularly important class of numerical methods for solving problems in statistical physics. Although these methods are simple in principle, their proper use requires a good command of statistical mechanics, as well as considerable computational resources. The aim of this paper is to demonstrate how the usage of widely accessible graphics cards on personal computers can elevate the computing power in Monte Carlo simulations by orders of magnitude, thus allowing live classroom demonstration of phenomena that would otherwise be out of reach. As an example, we use the public goods game on a square lattice where two strategies compete for common resources in a social dilemma situation. We show that the second-order phase transition to an absorbing phase in the system belongs to the directed percolation universality class, and we compare the time needed to arrive at this result by means of the main processor and by means of a suitable graphics card. Parallel computing on graphics processing units has been developed actively during the last decade, to the point where today the learning curve for entry is anything but steep for those familiar with programming. The subject is thus ripe for inclusion in graduate and advanced undergraduate curricula, and we hope that this paper will facilitate this process in the realm of physics education. To that end, we provide a documented source code for an easy reproduction of presented results and for further development of Monte Carlo simulations of similar systems.

The use of computers in statistical physics is common because the sheer number of equations that describe the behavior of an entire system particle by particle often makes it impossible to solve them exactly. Monte Carlo methods form a particularly important class of numerical methods for solving problems in statistical physics. Although these methods are simple in principle, their proper use requires a good command of statistical mechanics, as well as considerable computational resources. The aim of this paper is to demonstrate how the usage of widely accessible graphics cards on personal computers can elevate the computing power in Monte Carlo simulations by orders of magnitude, thus allowing live classroom demonstration of phenomena that would otherwise be out of reach. As an example, we use the public goods game on a square lattice where two strategies compete for common resources in a social dilemma situation. We show that the second-order phase transition to an absorbing phase in the system belongs to the directed percolation universality class, and we compare the time needed to arrive at this result by means of the main processor and by means of a suitable graphics card. Parallel computing on graphics processing units has been developed actively during the last decade, to the point where today the learning curve for entry is anything but steep for those familiar with programming. The subject is thus ripe for inclusion in graduate and advanced undergraduate curricula, and we hope that this paper will facilitate this process in the realm of physics education. To that end, we provide a documented source code for an easy reproduction of presented results and for further development of Monte Carlo simulations of similar systems.

The use of computers in statistical physics is common because the sheer number of equations that describe the behavior of an entire system particle by particle often makes it impossible to solve them exactly. Monte Carlo methods form a particularly important class of numerical methods for solving problems in statistical physics. Although these methods are simple in principle, their proper use requires a good command of statistical mechanics, as well as considerable computational resources. The aim of this paper is to demonstrate how the usage of widely accessible graphics cards on personal computers can elevate the computing power in Monte Carlo simulations by orders of magnitude, thus allowing live classroom demonstration of phenomena that would otherwise be out of reach. As an example, we use the public goods game on a square lattice where two strategies compete for common resources in a social dilemma situation. We show that the second-order phase transition to an absorbing phase in the system belongs to the directed percolation universality class, and we compare the time needed to arrive at this result by means of the main processor and by means of a suitable graphics card. Parallel computing on graphics processing units has been developed actively during the last decade, to the point where today the learning curve for entry is anything but steep for those familiar with programming. The subject is thus ripe for inclusion in graduate and advanced undergraduate curricula, and we hope that this paper will facilitate this process in the realm of physics education. To that end, we provide a documented source code for an easy reproduction of presented results and for further development of Monte Carlo simulations of similar systems.

The use of computers in statistical physics is common because the sheer number of equations that describe the behavior of an entire system particle by particle often makes it impossible to solve them exactly. Monte Carlo methods form a particularly important class of numerical methods for solving problems in statistical physics. Although these methods are simple in principle, their proper use requires a good command of statistical mechanics, as well as considerable computational resources. The aim of this paper is to demonstrate how the usage of widely accessible graphics cards on personal computers can elevate the computing power in Monte Carlo simulations by orders of magnitude, thus allowing live classroom demonstration of phenomena that would otherwise be out of reach. As an example, we use the public goods game on a square lattice where two strategies compete for common resources in a social dilemma situation. We show that the second-order phase transition to an absorbing phase in the system belongs to the directed percolation universality class, and we compare the time needed to arrive at this result by means of the main processor and by means of a suitable graphics card. Parallel computing on graphics processing units has been developed actively during the last decade, to the point where today the learning curve for entry is anything but steep for those familiar with programming. The subject is thus ripe for inclusion in graduate and advanced undergraduate curricula, and we hope that this paper will facilitate this process in the realm of physics education. To that end, we provide a documented source code for an easy reproduction of presented results and for further development of Monte Carlo simulations of similar systems.

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 .

Parallel Computing 1989: Volume 10 , Issue Index. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1989-02_9_3 . Next issue: sim_parallel-computing_1989-03_10_1 .

The 1984/85 accomplishments of the research project High Performance Parallel Computing included bringing the prototype of the Texas Reconfigurable Array Computer (TRAC) to a configuration and to a state of stability where it could support execution of simple assembly language programs, initial development of a unified model of parallel computation which is a basis for a programming environment uniting process and data flow models of parallel computation, bringing to operational status on an alternative host one of the two parallel programming languages (the Computation Structures Language, CSL) originally intended for use on TRAC, exploration of the expressive capabilities of this programming language, initiation of development of a graphical programming language based on the unified model of parallel computation mentioned preceding, major progress on a graphically interfaced Petri Net-based performance modeling system for parallel computations and development of algorithms for scheduling of circuits to realize configurations in configurable banyan network based computer architectures.

The 1984/85 accomplishments of the research project High Performance Parallel Computing included bringing the prototype of the Texas Reconfigurable Array Computer (TRAC) to a configuration and to a state of stability where it could support execution of simple assembly language programs, initial development of a unified model of parallel computation which is a basis for a programming environment uniting process and data flow models of parallel computation, bringing to operational status on an alternative host one of the two parallel programming languages (the Computation Structures Language, CSL) originally intended for use on TRAC, exploration of the expressive capabilities of this programming language, initiation of development of a graphical programming language based on the unified model of parallel computation mentioned preceding, major progress on a graphically interfaced Petri Net-based performance modeling system for parallel computations and development of algorithms for scheduling of circuits to realize configurations in configurable banyan network based computer architectures.

The 1984/85 accomplishments of the research project High Performance Parallel Computing included bringing the prototype of the Texas Reconfigurable Array Computer (TRAC) to a configuration and to a state of stability where it could support execution of simple assembly language programs, initial development of a unified model of parallel computation which is a basis for a programming environment uniting process and data flow models of parallel computation, bringing to operational status on an alternative host one of the two parallel programming languages (the Computation Structures Language, CSL) originally intended for use on TRAC, exploration of the expressive capabilities of this programming language, initiation of development of a graphical programming language based on the unified model of parallel computation mentioned preceding, major progress on a graphically interfaced Petri Net-based performance modeling system for parallel computations and development of algorithms for scheduling of circuits to realize configurations in configurable banyan network based computer architectures.

Parallel Computing 1989: Volume 11 , Issue Index. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1989-05_10_3 . Next issue: sim_parallel-computing_1989-07_11_1 .

Parallel Computing 1989: Volume 11 , Issue Index. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1989-05_10_3 . Next issue: sim_parallel-computing_1989-07_11_1 .

Parallel Computing 1989: Volume 11 , Issue Index. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1989-05_10_3 . Next issue: sim_parallel-computing_1989-07_11_1 .

788 p. ; 27 cm

788 p. ; 27 cm

Parallel Computing 1991: Volume 17 , Issue Index. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1990-12_16_2-3 . Next issue: sim_parallel-computing_1991-04_17_1 .

Parallel Computing 1991: Volume 17 , Issue Index. Digitized from IA1652917-03 . Previous issue: sim_parallel-computing_1990-12_16_2-3 . Next issue: sim_parallel-computing_1991-04_17_1 .

Motivated by the computational demands of our research and budgetary constraints which are common to many research institutions, we built a ``poor man's supercomputer'', a cluster of PC nodes which together can perform parallel calculations at a fraction of the price of a commercial supercomputer. We describe the construction, cost, and performance of our cluster.

In this paper, we have developed a parallel branch and bound algorithm which computes the maximal structured singular value $\mu$ without tightly bounding $\mu$ for each frequency and thus significantly reduce the computational complexity.

Users expect fast and fluid response from today's cloud infrastructure. Large-scale computing frameworks such as MapReduce divide jobs into many parallel tasks and execute them on different machines to enable faster processing. But the tasks on the slowest machines (straggling tasks) become the bottleneck in the completion of the job. One way to combat the variability in machine response time, is to add replicas of straggling tasks and wait for one copy to finish. In this paper we analyze how task replication strategies can be used to reduce latency, and their impact on the cost of computing resources. We use extreme value theory to show that the tail of the execution time distribution is the key factor in characterizing the trade-off between latency and computing cost. From this trade-off we can determine which task replication strategies reduce latency, without a large increase in computing cost. We also propose a heuristic algorithm to search for the best replication strategies when it is difficult to fit a simple distribution to model the empirical behavior of task execution time, and use the proposed analysis techniques. Evaluation of the heuristic policies on Google Trace data shows a significant latency reduction compared to the replication strategy used in MapReduce.