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Parallel programmability and the Chapel language

INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2007年第3期21卷 291-312页

作者： Chamberlain, B. L. Callahan, D. Zima, H. P. Cray Res Inc Seattle WA 98104 USA Microsoft Corp Redmond WA 98052 USA Univ Vienna A-1010 Vienna Austria JPL Pasadena CA USA

In this paper we consider productivity challenges for parallel programmers and explore ways that parallel language design might help improve end-user productivity. We offer a candidate list of desirable qualities for a parallel programming language, and describe how these qualities are addressed in the design of the Chapel language. In doing so, we provide an overview of Chapel's features an how they help address parallel productivity. We also survey current techniques for parallel programming and describe ways in which we consider them to fall short of our idealized productive programming model.

关键词： parallel languages productivity parallel programming programming models Chapel

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A lightweight approach to performance portability with targetDP

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2018年第2期32卷 288-301页

作者： Gray, Alan Stratford, Kevin Univ Edinburgh EPCC Edinburgh EH9 3FD Midlothian Scotland

Leading high performance computing systems achieve their status through use of highly parallel devices such as NVIDIA graphics processing units or Intel Xeon Phi many-core CPUs. The concept of performance portability across such architectures, as well as traditional CPUs, is vital for the application programmer. In this paper we describe targetDP, a lightweight abstraction layer which allows grid-based applications to target data parallel hardware in a platform agnostic manner. We demonstrate the effectiveness of our pragmatic approach by presenting performance results for a complex fluid application (with which the model was co-designed), plus separate lattice quantum chromodynamics particle physics code. For each application, a single source code base is seen to achieve portable performance, as assessed within the context of the Roofline model. TargetDP can be combined with Message Passing Interface (MPI) to allow use on systems containing multiple nodes: we demonstrate this through provision of scaling results on traditional and graphics processing unit-accelerated large scale supercomputers.

关键词： Performance portability programming models high performance computing graphics processing unit manycore computational physics

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Parallel computation of transient stability using symplectic Gauss method and GPU

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IET GENERATION TRANSMISSION & DISTRIBUTION 2016年第15期10卷 3727-3735页

作者： Liao, Xiaobing Wang, Fangzong China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Hubei Province Peoples R China

This study presents a new parallel algorithm for power system transient stability computation based on symplectic Gauss method. The s-stage 2s-order symplectic Gauss method is used to convert the differential-algebraic system simultaneously at s time points into a set of non-linear algebraic equations, and the algebraic system is then solved by Newton method. Based on the block matrix characteristics, the solution of the linear equations involved in Newton method's process can be divided into two parts: the first part is fully parallelisable in time, and the second part is solved by preconditioned GMRES method while an efficient preconditioner has been proposed based on the V-transformation. For test, the proposed algorithm is implemented on three programming models, the first is the traditional central processing unit (CPU) computing, the second is CPU-single graphics processing unit (GPU) cooperative computing, and the last one is CPU-multiple GPUs cooperative computing. The results show that, the proposed parallel algorithm is accurate, has good convergence, and has good scalability both in the problem size and in the number of used GPU.

关键词： power system transient stability graphics processing units microprocessor chips Newton method matrix algebra differential algebraic equations nonlinear differential equations parallel algorithm power system transient stability computation s-stage 2s-order symplectic Gauss method GPU differential-algebraic system nonlinear algebraic equations Newton method block matrix characteristics linear equations preconditioned GMRES method V-transformation programming models central processing unit computing CPU-single graphics processing unit cooperative computing

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Parallel programming Paradigms and Frameworks in Big Data Era

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INTERNATIONAL JOURNAL OF PARALLEL programming 2014年第5期42卷 710-738页

作者： Dobre, Ciprian Xhafa, Fatos Univ Politehn Bucuresti Dept Comp Sci Bucharest Romania Univ Politecn Cataluna ES-08034 Barcelona Spain

With Cloud Computing emerging as a promising new approach for ad-hoc parallel data processing, major companies have started to integrate frameworks for parallel data processing in their product portfolio, making it easy for customers to access these services and to deploy their programs. We have entered the Era of Big Data. The explosion and profusion of available data in a wide range of application domains rise up new challenges and opportunities in a plethora of disciplines-ranging from science and engineering to biology and business. One major challenge is how to take advantage of the unprecedented scale of data-typically of heterogeneous nature-in order to acquire further insights and knowledge for improving the quality of the offered services. To exploit this new resource, we need to scale up and scale out both our infrastructures and standard techniques. Our society is already data-rich, but the question remains whether or not we have the conceptual tools to handle it. In this paper we discuss and analyze opportunities and challenges for efficient parallel data processing. Big Data is the next frontier for innovation, competition, and productivity, and many solutions continue to appear, partly supported by the considerable enthusiasm around the MapReduce paradigm for large-scale data analysis. We review various parallel and distributed programming paradigms, analyzing how they fit into the Big Data era, and present modern emerging paradigms and frameworks. To better support practitioners interesting in this domain, we end with an analysis of on-going research challenges towards the truly fourth generation data-intensive science.

关键词： Parallel programming Big Data MapReduce programming models Challenges

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XACC: a system-level software infrastructure for heterogeneous quantum-classical computing*

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QUANTUM SCIENCE AND TECHNOLOGY 2020年第2期5卷

作者： McCaskey, Alexander J. Lyakh, Dmitry, I Dumitrescu, Eugene F. Powers, Sarah S. Humble, Travis S. Oak Ridge Natl Lab Quantum Comp Inst Oak Ridge TN 37831 USA Oak Ridge Natl Lab Comp Sci & Math Div Oak Ridge TN 37831 USA Oak Ridge Natl Lab Natl Ctr Computat Sci Oak Ridge TN 37831 USA Oak Ridge Natl Lab Computat Sci & Engn Div Oak Ridge TN 37831 USA

Quantum programming techniques and software have advanced significantly over the past five years, with a majority focusing on high-level language frameworks targeting remote REST library APIs. As quantum computing architectures advance and become more widely available, lower-level, system software infrastructures will be needed to enable tighter, co-processor programming and access models. Here we present XACC, a system-level software infrastructure for quantum-classical computing that promotes a service-oriented architecture to expose interfaces for core quantum programming, compilation, and execution tasks. We detail XACC's interfaces, their interactions, and its implementation as a hardware-agnostic framework for both near-term and future quantum-classical architectures. We provide concrete examples demonstrating the utility of this framework with paradigmatic tasks. Our approach lays the foundation for the development of compilers, associated runtimes, and low-level system tools tightly integrating quantum and classical workflows.

关键词： quantum computing quantum software programming models

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A Survey of Software Frameworks for Cluster-Based Large High-Resolution Displays

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IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2014年第8期20卷 1158-1177页

作者： Chung, Haeyong Andrews, Christopher North, Chris Virginia Polytech Inst & State Univ Dept Comp Sci Blacksburg VA 24060 USA Middlebury Coll Dept Comp Sci Middlebury VT 05753 USA

Large high-resolution displays (LHRD) enable visualization of extremely large-scale data sets with high resolution, large physical size, scalable rendering performance, advanced interaction methods, and collaboration. Despite the advantages, applications for LHRD can be developed only by a select group of researchers and programmers, since its software implementation requires design and development paradigms different from typical desktop environments. It is critical for developers to understand and take advantage of appropriate software tools and methods for developing their LHRD applications. In this paper, we present a survey of the state-of-the-art software frameworks and applications for cluster-based LHRD, highlighting a three-aspect taxonomy. This survey can aid LHRD application and framework developers in choosing more suitable development techniques and software environments for new LHRD applications, and guide LHRD researchers to open needs in LHRD software frameworks.

关键词： Large high-resolution display tiled displays distributed rendering parallel rendering distributed applications graphics api large scale visualization programming models input devices and strategies

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ParaDIME: Parallel Distributed Infrastructure for Minimization of Energy for data centers

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MICROPROCESSORS AND MICROSYSTEMS 2015年第8期39卷 1174-1189页

作者： Kumar Rethinagiri, Santhosh Palomar, Oscar Sobe, Anita Yalcin, Gulay Knauth, Thomas Titos Gil, Ruben Prieto, Pablo Schneegass, Malte Cristal, Adrian Unsal, Osman Felber, Pascal Fetzer, Christof Milojevic, Dragomir BSC Ctr Nacl Supercomputac Madrid Spain Univ Neuchatel Unine Neuchatel Switzerland Tech Univ Dresden Dresden Germany Cloud & Heat Technol GmbH Dresden Germany IMEC Brussels Belgium Spanish Natl Res Council CSIC IIIA Madrid Spain

Dramatic environmental and economic impact of the ever increasing power and energy consumption of modern computing devices in data centers is now a critical challenge. On the one hand, designers use technology scaling as one of the methods to face the phenomenon called dark silicon (only segments of a chip function concurrently due to power restrictions). On the other hand, designers use extreme-scale systems such as teradevices to meet the performance needs of their applications which in turn increases the power consumption of the platform. In order to overcome these challenges, we need novel computing paradigms that address energy efficiency. One of the promising solutions is to incorporate parallel distributed methodologies at different abstraction levels. The FP7 project ParaDIME focuses on this objective to provide different distributed methodologies (software-hardware techniques) at different abstraction levels to attack the power-wall problem. In particular, the ParaDIME framework will utilize: circuit and architecture operation below safe voltage limits for drastic energy savings, specialized energy-aware computing accelerators, heterogeneous computing, energy-aware runtime, approximate computing and power-aware message passing. The major outcome of the project will be a noval processor architecture for a heterogeneous distributed system that utilizes future device characteristics, runtime and programming model for drastic energy savings of data centers. Wherever possible, ParaDIME will adopt multidisciplinary techniques, such as hardware support for message passing, runtime energy optimization utilizing new hardware energy performance counters, use of accelerators for error recovery from sub-safe voltage operation, and approximate computing through annotated code. Furthermore, we will establish and investigate the theoretical limits of energy savings at the device, circuit, architecture, runtime and programming model levels of the computing stack, as well a

关键词： Low power Runtime energy optimization programming models Error recovery Approximate computing Message passing accelerators

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JEL: unified resource tracking for parallel and distributed applications

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2011年第1期23卷 17-37页

作者： Drost, Niels van Nieuwpoort, Rob V. Maassen, Jason Seinstra, Frank Bal, Henri E. Vrije Univ Amsterdam Dept Comp Sci NL-1081 HV Amsterdam Netherlands

When parallel applications are run in large-scale distributed environments, such as grids, peer-to-peer (P2P) systems, and clouds, the set of resources used can change dynamically as machines crash, reservations end, and new resources become available. It is vital for applications to respond to these changes. Therefore, it is necessary to keep track of the available resources-a problem which is known to be notoriously difficult. In this article we argue that resource tracking must be provided as the standard functionality in the lower parts of the software stack. We propose a general solution to resource tracking: the Join-Elect-Leave (JEL) model. JEL provides unified resource tracking for parallel and distributed applications across environments. JEL is a simple yet powerful model based on notifying when resources have Joined or Left the computation. We demonstrate that JEL is suitable for resource tracking in a wide variety of programming models, ranging from the fixed resource sets traditionally used in MPI-1 to flexible grid-oriented programming models. We compare several JEL implementations, and show these to perform and scale well in several real-world scenarios involving grids, clouds and P2P systems applied concurrently, and wide-area systems with failing resources. Using JEL, we have won the first prize in a number of international distributed computing competitions. Copyright (C) 2010 John Wiley & Sons, Ltd.

关键词： resource tracking programming models parallel applications

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Edelweiss: Automatic Storage Reclamation for Distributed programming

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PROCEEDINGS OF THE VLDB ENDOWMENT 2014年第6期7卷 481-492页

作者： Conway, Neil Alvaro, Peter Andrews, Emily Hellerstein, Joseph M. Univ Calif Berkeley Berkeley CA 94720 USA

Event Log Exchange (ELE) is a common programming pattern based on immutable state and messaging. ELE sidesteps traditional challenges in distributed consistency, at the expense of introducing new challenges in designing space reclamation protocols to avoid consuming unbounded storage. We introduce Edelweiss, a sublanguage of Bloom that provides an ELE programming model, yet automatically reclaims space without programmer assistance. We describe techniques to analyze Edelweiss programs and automatically generate application-specific distributed space reclamation logic. We show how Edelweiss can be used to elegantly implement a variety of communication and distributed storage protocols;the storage reclamation code generated by Edelweiss effectively garbage-collects state and often matches hand-written protocols from the literature.

关键词： Reclamation Application programs Application specific Distributed programming Distributed storage programming models programming patterns

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Signal/Collect Processing large graphs in seconds

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SEMANTIC WEB 2016年第2期7卷 139-166页

作者： Stutz, Philip Strebel, Daniel Bernstein, Abraham Univ Zurich CH-8006 Zurich Switzerland

Both researchers and industry are confronted with the need to process increasingly large amounts of data, much of which has a natural graph representation. Some use MapReduce for scalable processing, but this abstraction is not designed for graphs and has shortcomings when it comes to both iterative and asynchronous processing, which are particularly important for graph algorithms. This paper presents the Signal/Collect programming model for scalable synchronous and asynchronous graph processing. We show that this abstraction can capture the essence of many algorithms on graphs in a concise and elegant way by giving Signal/Collect adaptations of algorithms that solve tasks as varied as clustering, inferencing, ranking, classification, constraint optimisation, and even query processing. Furthermore, we built and evaluated a parallel and distributed framework that executes algorithms in our programming model. We empirically show that our framework efficiently and scalably parallelises and distributes algorithms that are expressed in the programming model. We also show that asynchronicity can speed up execution times. Our framework can compute a PageRank on a large (>1.4 billion vertices, >6.6 billion edges) real-world graph in 112 seconds on eight machines, which is competitive with other graph processing approaches.

关键词： Distributed computing scalability programming abstractions programming models graph processing

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