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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2007年第3期21卷 336-359页

作者： Bliss, N. Travinin Kepner, J. MIT Lincoln Lab Lexington MA 02420 USA

MATLAB (R) has emerged as one of the languages most commonly used by scientists and engineers for technical computing, with approximately one million users worldwide. The primary benefits Of MATLAB are reduced code development time via high levels of abstractions (e.g. first class multi-dimensional arrays and thousands of built in functions), interpretive, interactive programming, and powerful mathematical graphics. The compute intensive nature of technical computing means that many MATLAB users have codes that can significantly benefit from the increased performance offered by parallel computing. plMatlab provides this capability by implementing parallel global array semantics using standard operator overloading techniques. The core data structure in pMatlab is a distributed numerical array whose distribution onto multiple processors is specified with a "map" construct. Communication operations between distributed arrays are abstracted away from the user and plMatlab transparently supports redistribution between any block-cyclic-overlapped distributions up to four dimensions. pMatlab is built on top of the MatlabMPI communication library and runs on any combination of heterogeneous systems that support MATLAB, which includes Windows, Linux, MacOS X, and SunOS. This paper describes the overall design and architecture of the pMatlab implementation. Performance is validated by implementing the HPC Challenge benchmark suite and comparing plMatlab performance with the equivalent C+MPI codes. These results indicate that plMatlab can often achieve comparable performance to C+MPI, usually at one tenth the code size. Finally, we present implementation data collected from a sample of real pMatlab applications drawn from the approximately one hundred users at MIT Lincoln Laboratory. These data indicate that users are typically able to go from a serial code to an efficient pMatlab code in about 3 hours while changing less than 1% of their code.

关键词： parallel computing parallel programming models parallel MATLAB HPC challenge

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Concurrent Kleene algebra with tests and branching automata

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JOURNAL OF LOGICAL AND ALGEBRAIC METHODS IN programming 2016年第4期85卷 637-652页

作者： Jipsen, Peter Moshier, M. Andrew Chapman Univ Orange CA 92866 USA

We introduce concurrent Kleene algebra with tests (CKAT) as a combination of Kleene algebra with tests (KAT) of Kozen and Smith with concurrent Kleene algebras (CKA), introduced by Hoare, Moller, Struth and Wehrman. CKAT provides a relatively simple algebraic model for reasoning about semantics of concurrent programs. We generalize guarded strings to guarded series-parallel strings, or gsp-strings, to give a concrete language model for CKAT. Combining nondeterministic guarded automata of Kozen with branching automata of Lodaya and Weil one obtains a model for processing gsp-strings in parallel. To ensure that the model satisfies the weak exchange law (x parallel to y)(z parallel to w) <= (xz)parallel to(yw) of CKA, we make use of the subsumption order of Gischer on the gsp-strings. We also define deterministic branching automata and investigate their relation to (nondeterministic) branching automata. To express basic concurrent algorithms, we define concurrent deterministic flowchart schemas and relate them to branching automata and to concurrent Kleene algebras with tests. (C) 2016 Elsevier Inc. All rights reserved.

关键词： Concurrent Kleene algebra Kleene algebra with tests parallel programming models Deterministic fork-join automata Series-parallel strings Weak exchange law Positive separation algebra Flowchart schemas

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TProf: An energy profiler for task-parallel programs

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SUSTAINABLE COMPUTING-INFORMATICS & SYSTEMS 2015年 5卷 1-13页

作者： Manousakis, Ioannis Zakkak, Foivos S. Pratikakis, Polyvios Nikolopoulos, Dimitrios S. Rutgers State Univ Dept Comp Sci Piscataway NJ 08855 USA Fdn Res & Technol Inst Comp Sci Hellas Greece Queens Univ Belfast Sch Elect Elect Engn & Comp Sci Belfast BT7 1NN Antrim North Ireland

We present TPR0F, an energy profiling tool for OpenMP-like task-parallel programs. To compute the energy consumed by each task in a parallel application, TPRoF dynamically traces the parallel execution and uses a novel technique to estimate the per-task energy consumption. To achieve this estimation, TPRoF apportions the total processor energy among cores and overcomes the limitation of current works which would otherwise make parallel accounting impossible to achieve. We demonstrate the value of TPRoF by characterizing a set of task parallel programs, where we find that data locality, memory access patterns and task working sets are responsible for significant variance in energy consumption between seemingly homogeneous tasks. In addition, we identify opportunities for fine-grain energy optimization by applying per-task Dynamic Voltage and Frequency Scaling (DVFS). (C) 2014 Published by Elsevier Inc.

关键词： Energy profiling parallel programming models parallel runtime systems Task parallelism

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programming big data analysis: principles and solutions

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JOURNAL OF BIG DATA 2022年第1期9卷 1-50页

作者： Belcastro, Loris Cantini, Riccardo Marozzo, Fabrizio Orsino, Alessio Talia, Domenico Trunfio, Paolo Univ Calabria Arcavacata Di Rende Italy Dtok Lab Arcavacata Di Rende Italy

In the age of the Internet of Things and social media platforms, huge amounts of digital data are generated by and collected from many sources, including sensors, mobile devices, wearable trackers and security cameras. This data, commonly referred to as Big Data, is challenging current storage, processing, and analysis capabilities. New models, languages, systems and algorithms continue to be developed to effectively collect, store, analyze and learn from Big Data. Most of the recent surveys provide a global analysis of the tools that are used in the main phases of Big Data management (generation, acquisition, storage, querying and visualization of data). Differently, this work analyzes and reviews parallel and distributed paradigms, languages and systems used today to analyze and learn from Big Data on scalable computers. In particular, we provide an in-depth analysis of the properties of the main parallel programming paradigms (MapReduce, workflow, BSP, message passing, and SQL-like) and, through programming examples, we describe the most used systems for Big Data analysis (e.g., Hadoop, Spark, and Storm). Furthermore, we discuss and compare the different systems by highlighting the main features of each of them, their diffusion (community of developers and users) and the main advantages and disadvantages of using them to implement Big Data analysis applications. The final goal of this work is to help designers and developers in identifying and selecting the best/appropriate programming solution based on their skills, hardware availability, application domains and purposes, and also considering the support provided by the developer community.

关键词： parallel programming models programming systems Big Data analysis MapReduce Workflow Message Passing Bulk Synchronous parallel SQL-like

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A high-productivity task-based programming model for clusters

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2012年第18期24卷 2421-2448页

作者： Tejedor, Enric Farreras, Montse Grove, David Badia, Rosa M. Almasi, Gheorghe Labarta, Jesus Barcelona Supercomp Ctr BSC CNS Barcelona 08034 Spain Univ Politecn Cataluna UPC Barcelona Spain IBM Corp Thomas J Watson Res Ctr Yorktown Hts NY 10598 USA CSIC Artificial Intelligence Res Inst IIIA Barcelona Spain

programming for large-scale, multicore-based architectures requires adequate tools that offer ease of programming and do not hinder application performance. StarSs is a family of parallel programming models based on automatic function-level parallelism that targets productivity. StarSs deploys a data-flow model: it analyzes dependencies between tasks and manages their execution, exploiting their concurrency as much as possible. This paper introduces Cluster Superscalar (ClusterSs), a new StarSs member designed to execute on clusters of SMPs (Symmetric Multiprocessors). ClusterSs tasks are asynchronously created and assigned to the available resources with the support of the IBM APGAS runtime, which provides an efficient and portable communication layer based on one-sided communication. We present the design of ClusterSs on top of APGAS, as well as the programming model and execution runtime for Java applications. Finally, we evaluate the productivity of ClusterSs, both in terms of programmability and performance and compare it to that of the IBM X10 language. Copyright (c) 2012 John Wiley & Sons, Ltd.

关键词： parallel programming models high performance computing asynchronous execution productivity

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Transparent Orchestration of Task-based parallel Applications in Containers Platforms

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JOURNAL OF GRID COMPUTING 2018年第1期16卷 137-160页

作者： Ramon-Cortes, Cristian Serven, Albert Ejarque, Jorge Lezzi, Daniele Badia, Rosa M. BSC Barcelona Spain CSIC IIIA Artificial Intelligence Res Inst Spanish Natl Res Council Barcelona Spain

This paper presents a framework to easily build and execute parallel applications in container-based distributed computing platforms in a user-transparent way. The proposed framework is a combination of the COMP Superscalar (COMPSs) programming model and runtime, which provides a straightforward way to develop task-based parallel applications from sequential codes, and containers management platforms that ease the deployment of applications in computing environments (as Docker, Mesos or Singularity). This framework provides scientists and developers with an easy way to implement parallel distributed applications and deploy them in a one-click fashion. We have built a prototype which integrates COMPSs with different containers engines in different scenarios: i) a Docker cluster, ii) a Mesos cluster, and iii) Singularity in an HPC cluster. We have evaluated the overhead in the building phase, deployment and execution of two benchmark applications compared to a Cloud testbed based on KVM and OpenStack and to the usage of bare metal nodes. We have observed an important gain in comparison to cloud environments during the building and deployment phases. This enables better adaptation of resources with respect to the computational load. In contrast, we detected an extra overhead during the execution, which is mainly due to the multi-host Docker networking.

关键词： Cloud computing Containers orchestration Linux containers Distributed systems parallel programming models

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Hybrid MPI-thread parallelization of adaptive mesh operations

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parallel COMPUTING 2016年 52卷 133-143页

作者： Ibanez, Dan Dunn, Ian Shephard, Mark S. Rensselaer Polytech Inst Sci Computat Res Ctr 110 Eighth St Troy NY 12180 USA

Many of the world's leading supercomputer architectures are a hybrid of shared memory and network-distributed memory. Such an architecture lends itself to a hybrid MPI-thread programming model. We first present an implementation of inter-thread message passing based on the MPI and pthread libraries. In addition, we present an efficient implementation of termination detection for communication rounds. We use the term phased message passing to denote the communication interface based on this termination detection. This interface is then used to implement parallel operations for adaptive unstructured meshes, and the performance of resulting applications is compared to pure MPI operation. We also present new workflows enabled by the ability to vary the number of threads during run-time. (C) 2016 Elsevier B.V. All rights reserved.

关键词： Hybrid system Shared memory parallel programming models MPI Termination detection Non-blocking

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Associative nets: A graph-based parallel computing model

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IEEE TRANSACTIONS ON COMPUTERS 1997年第5期46卷 558-571页

作者： Merigot, A UNIV PARIS 11 CNRSURA22 INTEGRATED CIRCUITS & SYST ARCHITECTURE GRP FUNDAMENTAL ELECT INST ORSAY FRANCE

This paper presents a new parallel computing model called Associative Nets. This model relies on basic primitives called associations that consist to apply an associative operator over connected components of a subgraph of the physical interprocessor connection graph. Associations can be very efficiently implemented (in terms of hardware cost or processing time) thanks to asynchronous computation. This model is quite effective for image analysis and several other fields;as an example, graph processing algorithms are presented. While relying on a much simpler architecture, these algorithms have, in general, a complexity equivalent to the one obtained by more expensive computing models, like the PRAM model.

关键词： parallel programming models parallel algorithms fine grain parallelism SIMD bit-serial arithmetic graph processing asynchronous logic

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***: MPI-Based Asynchronous Task Execution for Python

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 2023年第2期34卷 611-622页

作者： Rogowski, Marcin Aseeri, Samar Keyes, David Dalcin, Lisandro King Abdullah Univ Sci & Technol KAUST Extreme Comp Res Ctr ECRC Thuwal 239556900 Saudi Arabia King Abdullah Univ Sci & Technol KAUST Comp Sci Program Thuwal 239556900 Saudi Arabia

We present ***, a lightweight, asynchronous task execution framework targeting the Python programming language and using the Message Passing Interface (MPI) for interprocess communication. *** follows the interface of the *** package from the Python standard library and can be used as its drop-in replacement, while allowing applications to scale over multiple compute nodes. We discuss the design, implementation, and feature set of *** and compare its performance to other solutions on both shared and distributed memory architectures. On a shared-memory system, we show *** to consistently outperform Python's *** with speedup ratios between 1.4X and 3.7X in throughput (tasks per second) and between 1.9X and 2.9X in bandwidth. On a Cray XC40 system, we compare *** to Dask - a well-known Python parallel computing package. Although we note more varied results, we show *** to outperform Dask in most scenarios.

关键词： MPI Python parallelism master-worker parallel programming models distributed computing high performance computing task execution multiprocessing

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The JStar language philosophy

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parallel COMPUTING 2014年第2期40卷 35-50页

作者： Utting, Mark Weng, Min-Hsien Cleary, John G. Univ Waikato Dept Comp Sci FCMS Hamilton New Zealand

This paper introduces the JStar parallel programming language, which is a Java-based declarative language aimed at discouraging sequential programming, encouraging massively parallel programming, and giving the compiler and runtime maximum freedom to try alternative parallelisation strategies. We describe the execution semantics and runtime support of the language, several optimisations and parallelism strategies, with some benchmark results. (C) 2013 Elsevier B.V. All rights reserved.

关键词： parallel programming models Architecture independence JStar Java Datalog Linda-like languages

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