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Design patterns percolating to parallel programming framework implementation

INTERNATIONAL JOURNAL OF PARALLEL programming 2014年第6期42卷 1012-1031页

作者： Aldinucci, M. Campa, S. Danelutto, M. Kilpatrick, P. Torquati, M. Univ Pisa Dept Comp Sci Pisa Italy Queens Univ Belfast Belfast Antrim North Ireland Univ Turin Turin Italy

Structured parallel programming is recognised as a viable and effective means of tackling parallel programming problems. Recently, a set of simple and powerful parallel building blocks () has been proposed to support modelling and implementation of parallel frameworks. In this work we demonstrate how that same parallel building block set may be used to model both general purpose parallel programming abstractions, not usually listed in classical skeleton sets, and more specialized domain specific parallel patterns. We show how an implementation of can be realised via the FastFlow framework and present experimental evidence of the feasibility and efficiency of the approach.

关键词： Algorithmic skeleton Parallel design patterns programming frameworks RISC-pb(2)I Parallel building blocks

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programming models and systems for Big Data analysis

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INTERNATIONAL JOURNAL OF PARALLEL EMERGENT AND DISTRIBUTED SYSTEMS 2019年第6期34卷 632-652页

作者： Belcastro, Loris Marozzo, Fabrizio Talia, Domenico Univ Calabria DIMES Arcavacata Di Rende Italy

Big Data analysis refers to advanced and efficient datamining and machine learning techniques applied to large amount of data. Research work and results in the area of Big Data analysis are continuously rising, and more and more new and efficient architectures, programming models, systems, and data mining algorithms are proposed. Taking into account the most popular programming models for Big Data analysis (MapReduce, Directed Acyclic Graph, Message Passing, Bulk Synchronous Parallel, Workflow and SQL-like), we analysed the features of the main systems implementing them. Such systems are compared using four classification criteria (i.e. level of abstraction, type of parallelism, infrastructure scale and classes of applications) for helping developers and users to identify and select the best solution according to their skills, hardware availability, productivity and application needs. [GRAPHICS] This figure is a word cloud highlighting the most popular words related to Big Data analysis.

关键词： Parallel programming models programming systems Big Data analysis cloud computing programming frameworks MapReduce directed acyclic graph message passing bulk synchronous parallel workflow SQL-like

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Designing parallel data processing for enabling large-scale sensor applications

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PERSONAL AND UBIQUITOUS COMPUTING 2017年第3期21卷 457-473页

作者： Kabac, Milan Consel, Charles Volanschi, Nic Inria Bordeaux Bordeaux France Bordeaux Inst Technol Bordeaux France

Masses of sensors are being deployed at the scale of cities to manage parking spaces, transportation infrastructures to monitor traffic, and campuses of buildings to reduce energy consumption. These large-scale infrastructures become a reality for citizens via applications that orchestrate sensors to deliver high-value, innovative services. These applications critically rely on the processing of large amounts of data to analyze situations, inform users, and control devices. This paper proposes a design-driven approach to developing orchestrating applications for masses of sensors that integrates parallel processing of large amounts of data. Specifically, an application design exposes declarations that are used to generate a programming framework based on the MapReduce programming model. We have developed a prototype of our approach, using Apache Hadoop. We applied it to a case study and obtained significant speedups by parallelizing computations over twelve nodes. In doing so, we demonstrate that our design-driven approach allows to abstract over implementation details, while exposing architectural properties used to generate high-performance code for processing large datasets. Furthermore, we show that this high-performance support enables new, personalized services in a smart city. Finally, we discuss the expressiveness of our design language, identify some limitations, and present language extensions.

关键词： Data processing programming frameworks Sensors MapReduce Orchestration

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dispel4py: A Python framework for data-intensive scientific computing

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2017年第4期31卷 316-334页

作者： Filguiera, Rosa Krause, Amrey Atkinson, Malcolm Klampanos, Iraklis Moreno, Alexander Univ Edinburgh Sch Informat eSci Edinburgh Midlothian Scotland Univ Edinburgh EPCC Edinburgh Midlothian Scotland Natl Ctr Sci Res Demokritos Athens Greece Georgia Inst Technol Sch Comp Sci Atlanta GA 30332 USA

This paper presents dispel4py, a new Python framework for describing abstract stream-based workflows for distributed data-intensive applications. These combine the familiarity of Python programming with the scalability of workflows. Data streaming is used to gain performance, rapid prototyping and applicability to live observations. dispel4py enables scientists to focus on their scientific goals, avoiding distracting details and retaining flexibility over the computing infrastructure they use. The implementation, therefore, has to map dispel4py abstract workflows optimally onto target platforms chosen dynamically. We present four dispel4py mappings: Apache Storm, message-passing interface (MPI), multi-threading and sequential, showing two major benefits: a) smooth transitions from local development on a laptop to scalable execution for production work, and b) scalable enactment on significantly different distributed computing infrastructures. Three application domains are reported and measurements on multiple infrastructures show the optimisations achieved;they have provided demanding real applications and helped us develop effective training. The *** is an open-source project to which we invite participation. The effective mapping of dispel4py onto multiple target infrastructures demonstrates exploitation of data-intensive and high-performance computing (HPC) architectures and consistent scalability.

关键词： Data-intensive computing e-infrastructures data streaming scientific workflows programming frameworks

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Factors and actors leading to the adoption of a JavaScript framework

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EMPIRICAL SOFTWARE ENGINEERING 2018年第6期23卷 3503-3534页

作者： Pano, Amantia Graziotin, Daniel Abrahamsson, Pekka Free Univ Bozen Bolzano Fac Comp Sci Bolzano Italy Univ Stuttgart Inst Software Technol Stuttgart Germany Univ Jyvaskyla Fac Informat Technol Jyvaskyla Finland

The increasing popularity of JavaScript has led to a variety of JavaScript frameworks that aim to help developers to address programming tasks. However, the number of JavaScript frameworks has risen rapidly to thousands of versions. It is challenging for practitioners to identify the frameworks that best fit their needs and to develop new ones which fit such needs. Furthermore, there is a lack of knowledge regarding what drives developers toward the choice. This paper explores the factors and actors that lead to the choice of a JavaScript framework. We conducted a qualitative interpretive study of semi-structured interviews. We interviewed 18 decision makers regarding the JavaScript framework selection, up to reaching theoretical saturation. Through coding of the interview responses, we offer a model of desirable JavaScript framework adoption factors. The factors are grouped into categories that are derived via the Unified Theory of Acceptance and Use of Technology. The factors are performance expectancy (performance, size), effort expectancy (automatization, learnability, complexity, understandability), social influence (competitor analysis, collegial advice, community size, community responsiveness), facilitating conditions (suitability, updates, modularity, isolation, extensibility), and price value. A combination of four actors, which are customer, developer, team, and team leader, leads to the choice. Our model contributes to the body of knowledge related to the adoption of technology by software engineers. As a practical implication, our model is useful for decision makers when evaluating JavaScript frameworks, as well as for developers for producing desirable frameworks.

关键词： JavaScript programming frameworks Decision making Technology adoption Human aspects of software development Qualitative research Interpretivism

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frameworks compiled from declarations: a language-independent approach

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SOFTWARE-PRACTICE & EXPERIENCE 2017年第5期47卷 741-762页

作者： van der Walt, Paul Consel, Charles Balland, Emilie INRIA Bordeaux Equipe Phoenix Bordeaux France INRIA Bordeaux Bordeaux France INRIA Le Chesnay France

programming frameworks are an accepted fixture in the object-oriented world, motivated by the need for code reuse, developer guidance and restriction. A new trend is emerging where frameworks require domain experts to provide declarations using a domain-specific language, influencing the structure and behaviour of the resulting application. These mechanisms address concerns such as user privacy. Although many popular open platforms such as Android are based on declaration-driven frameworks, current implementations provide ad hoc and narrow solutions to concerns raised by their openness to non-certified developers. Most widely used frameworks fail to address serious privacy leaks and provide the user with little insight into application behaviour. To address these shortcomings, we show that declaration-driven frameworks can limit privacy leaks, as well as guide developers, independently from the underlying programming paradigm. To do so, we identify concepts that underlie declaration-driven frameworks and apply them systematically to an object-oriented language, Java and a dynamic functional language, Racket. The resulting programming framework generators are used to develop a prototype mobile application, illustrating how we mitigate a common class of privacy leaks. Finally, we explore the possible design choices and propose development principles for developing domain-specific language compilers to produce frameworks, applicable across a spectrum of programming paradigms. Copyright (C) 2016 John Wiley & Sons, Ltd.

关键词： generative programming programming frameworks privacy controls domain-specific languages

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Melia: A MapReduce Framework on OpenCL-Based FPGAs

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2016年第12期27卷 3547-3560页

作者： Wang, Zeke Zhang, Shuhao He, Bingsheng Zhang, Wei Nanyang Technol Univ Parallel Distributed Comp Ctr Sch Comp Engn Singapore Singapore Nanyang Technol Univ Dept Comp Sci & Engn Singapore Singapore Nanyang Technol Univ Sch Comp Engn Singapore Singapore Hong Kong Univ Sci & Technol Reconfigurable Syst Lab Hong Kong Hong Kong Peoples R China

MapReduce, originally developed by Google for search applications, has recently become a popular programming framework for parallel and distributed environments. This paper presents an energy-efficient architecture design for MapReduce on Field Programmable Gate Arrays (FPGAs). The major goal is to enable users to program FPGAs with simple MapReduce interfaces, and meanwhile to embrace automatic performance optimizations within the MapReduce framework. Compared to other processors like CPUs and GPUs, FPGAs are (re-)programmable hardware and have very low energy consumption. However, the design and implementation of MapReduce on FPGAs can be challenging: firstly, FPGAs are usually programmed with hardware description languages, which hurts the programmability of the MapReduce design to its users;secondly, since MapReduce has irregular access patterns (especially in the reduce phase) and needs to support user-defined functions, careful designs and optimizations are required for efficiency. In this paper, we design, implement and evaluate Melia, a MapReduce framework on FPGAs. Melia takes advantage of the recent OpenCL programming framework developed for Altera FPGAs, and abstracts FPGAs behind the simple and familiar MapReduce interfaces in C. We further develop a series of FPGA-centric optimization techniques to improve the efficiency of Melia, and a cost-and resource-based approach to automate the parameter settings for those optimizations. We evaluate Melia on a recent Altera Stratix V GX FPGA with a number of commonly used MapReduce benchmarks. Our results demonstrate that 1) the efficiency and effectiveness of our optimizations and automated parameter setting approach, 2) Melia can achieve promising energy efficiency in comparison with its counterparts on CPUs/GPUs on both single-FPGA and cluster settings.

关键词： FPGA MapReduce programming frameworks cost model OpenCL

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AutoMate: enabling autonomic applications on the Grid

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CLUSTER COMPUTING-THE JOURNAL OF NETWORKS SOFTWARE TOOLS AND APPLICATIONS 2006年第2期9卷 161-174页

作者： Parashar, M Liu, H Li, Z Matossian, V Schmidt, C Zhang, G Hariri, S Rutgers State Univ Appl Software Syst Lab Dept Elect & Comp Engn Piscataway NJ 08855 USA Univ Arizona High Performance Distributed Comp Lab Dept Elect & Comp Engn Tucson AZ 85721 USA

The increasing complexity, heterogeneity, and dynamism of emerging pervasive Grid environments and applications has necessitated the development of autonomic self-managing solutions, that are inspired by biological systems and deal with similar challenges of complexity, heterogeneity, and uncertainty. This paper introduces Project AutoMate and describes its key components. The overall goal of Project Automate is to investigate conceptual models and implementation architectures that can enable the development and execution of such self-managing Grid applications. Illustrative autonomic scientific and engineering Grid applications enabled by AutoMate are presented.

关键词： autonomic computing autonomic Grid applications programming frameworks middleware services

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P-INSPECT: Architectural Support for Programmable Non-Volatile Memory frameworks 53

P-INSPECT: Architectural Support for Programmable Non-Volati...

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53rd Annual IEEE/ACM International Symposium on Microarchitecture (MICRO)

作者： Kokolis, Apostolos Shull, Thomas Huang, Jian Torrellas, Josep Univ Illinois Champaign IL 61820 USA

ISBN: (纸本)9781728173832

The availability of user-friendly programming frameworks is key to the success of Non-Volatile Memory (NVM). Unfortunately, most current NVM frameworks rely heavily on user intervention to mark persistent objects and even persistent writes. This not only complicates NVM programming, but also introduces potential bugs. To address these issues, researchers have proposed Persistence by Reachability frameworks, which require minimal user intervention. However, these frameworks are slow because their runtimes have to perform checks at program load/store operations, and move data structures between DRAM and NVM during program execution. In this paper, we introduce P-INSPECT, a novel hardware architecture targeted to speeding up persistence by reachability NVM programming frameworks. P-INSPECT uses bloom-filter hardware to perform various checks in a transparent and efficient manner. It also provides hardware for low-overhead persistent writes. Our simulation-based evaluation running a state-of-the-art persistence by reachability framework shows that P-INSPECT retains programmability and eliminates most of the overhead. We use real-world applications to demonstrate that, on average, P-INSPECT reduces an application's number of executed instructions by 26% and the execution time by 16%-delivering similar performance to an ideal runtime that has no persistence by reachability overhead.

关键词： Non-volatile memory programming frameworks Hardware for programmability

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A Formal Algebra Implementation for Distributed Image and Video Stream Processing 16

A Formal Algebra Implementation for Distributed Image and Vi...

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10th International Conference on Distributed Smart Cameras (ICDSC)

作者： Helala, Mohamed A. Pu, Ken Q. Qureshi, Faisal Z. Univ Ontario Inst Technol Fac Sci Oshawa ON Canada

ISBN: (纸本)9781450347860

We are interested in building scalable computer vision systems for distributed processing of big visual data. We apply data streaming concepts, namely stream algebra operators, which have been proven effective in the database literature. The operators collectively form an algebra over data streams. The algebra has well defined semantics. It naturally describes online computer vision algorithms and their feedback control and tuning algorithms. In this work, we present the first implementation of such algebra at large scale. Our implementation provides a high level programming interface for constructing and executing vision workflow graphs while hiding the data transfer and concurrency details. It also allows feedback control and dynamic reconfiguration of vision algorithms. A case study is discussed showing a streaming workflow for online lane and road boundary detection and describing the flexibility and effectiveness of the algebra for building complex distributed applications.

关键词： stream algebra workflow graphs programming frameworks distributed vision processing

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