DOCSLIB.ORG

Jflow: Practical Refactorings for Flow-Based Parallelism

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Jflow: Practical Refactorings for Flow-Based Parallelism View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Illinois Digital Environment for Access to Learning and Scholarship Repository JFlow: Practical Refactorings for Flow-based Parallelism Nicholas Chen, Ralph E. Johnson Department of Computer Science University of Illinois at Urbana-Champaign nchen, rjohnson @illinois.edu f g Abstract—Emerging applications in the domains of recognition, coupling between different parts of the software, making it mining and synthesis (RMS); image and video processing; data easier to maintain and evolve each part independently. warehousing; and automatic financial trading admit a particular A compelling way to leverage flow-based parallelism is style of parallelism termed flow-based parallelism. To help devel- opers exploit flow-based parallelism, popular parallel libraries to refactor existing sequential code to use the constructs of such as Groovy’s GPars, Intel’s TBB Flow Graph and Microsoft’s a parallel library such as Groovy’s GPars [4], Intel’s TBB TPL Dataflow have begun introducing many new and useful Flow Graph [5] or Microsoft’s TPL Dataflow [6]. Our prior constructs. However, to reap the benefits of such constructs, work examined the constructs in an older version of Intel’s developers must first use them. This involves refactoring their TBB and found them expressive enough to address many existing sequential code to incorporate these constructs – a manual process that overwhelms even experts. To alleviate this of the common idioms found in applications [7]. We found burden, we introduce a set of novel analyses and transformations using such constructs to be more advantageous compared to targeting flow-based parallelism. We implemented these ideas direct parallelizing with low-level threads. Firstly, programs in JFlow, an interactive refactoring tool integrated into the written using parallel constructs were more succinct and easier Eclipse IDE. We used JFlow to parallelize seven applications: four to evolve because the library provides useful constructs for from a previously known benchmark and three from a suite of large open source projects. Our evaluation on these applications adding/removing nodes or edges to a computation graph. Sec- demonstrates that JFlow, with some minimal interaction from ondly, in some cases, code parallelized using library constructs the developer, can successfully parallelize applications from the were faster than code parallelized directly with low-level aforementioned domains with good performance (offering up to threads because the library has better support for managing 3.45x speedup on a 4-core machine) and is fast enough to be and coordinating threads through a thread pool. used interactively as part of a developer’s workflow. To refactor their code to use such constructs, developers face two overwhelming tasks: analysis and transformation of I. INTRODUCTION their sequential code. First, a developer has to partition the The term “flow-based parallelism” is coined after Morri- original sequential code into a computation flow graph with son’s Flow-Based Programming: A New Approach to Appli- nodes and edges. She must scrutinize each node to ensure that cation Development [1]. Flow-based parallelism is a parallel no data races exist. Given a large and unfamiliar code base, programming paradigm that partitions an expensive computa- this is hard even for experts. Second, once she is convinced tion into a directed graph, i.e., a computation flow graph. Each that there are no data races, she still has to meticulously write node in the graph embodies part of the original computation repetitive, boilerplate code to create the nodes and link the and the edges between nodes represent dependencies. Data edges between nodes. Given a large computation graph, there flow between nodes. Nodes perform their computation when all could be many nodes and edges, and manually writing code their data dependencies are available. Parallelism is achieved to describe them is tedious. How can we assist developers in when nodes can operate concurrently. Examples of flow- analyzing and transforming their sequential programs? based parallelism include pipeline parallelism, event-based Parallelizing compilers attempt to relieve the burden of coordination and the wavefront pattern [2], [3]. The structure analyzing and transforming the code in an entirely automated of the graph is usually simple, e.g., in the case of pipeline manner, without involving the developer. While appealing, parallelism but could also be more complex, e.g., in the case even the most advanced static analysis in the compiler are of wavefront computations. frequently confounded by common programming idioms that Flow-based parallelism offers many advantages. It is well- abound in typical object-oriented programs, forcing the com- suited for parallelizing emerging applications in the domains piler to be overly pessimistic and precluding many paralleliza- of recognition, mining and synthesis (RMS); image and video tion opportunities. Kim et al. [8] tested two commercial C/C++ processing; data warehousing; and automatic financial trading compilers on the OmpSCR benchmarks [9] and found that, (SectionIV). Using flow-based parallelism not only improves at best, the compilers could only automatically parallelize 4 the performance of such applications but also their modular- out of 13 loops although loop parallelism had been studied ity. By partitioning the computation into nodes of a graph since the first parallelizing compilers [10]. They attributed that communicate via explicit parameters, we can reduce the the reasons for failure to pointer based accesses, complex control flow and insufficient cost-benefit analysis. Even state readable source code to describe the computation flow graph. of the art static analysis for object-oriented program [11]– The ideas behind our analyses and transformations are general [13] have trouble reasoning about many best practice idioms. and can be adapted for different languages and frameworks. For instance, in the applications we evaluated, we found that Second, we incorporated our analyses and transformations into the use of static factory methods, logging constructs and, in our interactive tool, JFlow. JFlow works with Java applications general, the use of fairly standard library APIs confuse the and targets the constructs from Groovy’s GPars parallel library. static analysis and force it to be safe but overly conservative. We evaluated JFlow on seven applications from different Common techniques for increasing the precision of static emerging domains. We report the parallel speedups and discuss analysis (at the expense of memory and running time) e.g., the user interactions necessary for each application. increasing k for k-object sensitivity [14] or n for call-string- The source code for JFlow and the applications that we sensitivity [15], do not help. evaluated are available at http://vazexqi.github.io/JFlow/. While it is difficult for static analysis to reason about such idioms, it is easier for a human. Thus, we propose a practical II. MOTIVATING EXAMPLE approach that combines static analysis with human interaction. Consider LIRe (16K SLOC), an open source Java content- Our approach actively engages the developer, i.e., the domain based image retrieval (CBIR) library [17]. LIRe takes a query expert in the parallelization process. We adapt ideas from image, extracts features from it and, based on those features, human automation and divide the tasks between developer retrieves similar images from a database of candidate images. and tool such that each performs what each excels at [16]. Typical features that are extracted using computer vision The developer performs the high-level reasoning task that tools techniques include color histograms, shapes and textures. have problems with — identifying which parts of the original In LIRe, the main bottlenecks to performance are the computation to partition — whereas the tool handles the low- indexing and retrieval stages. This example focuses on the level analysis task that developers find tedious — analyzing if indexing stage. In the indexing stage, a set of images is the partitions have data races and generating the parallel code. analyzed to build the database of candidate images. During This combination is effective. It enables us to parallelize many the analysis, features are extracted from the images and stored more applications than would have been possible through static in the database. Because there isn’t a single feature that works analysis alone. across all images, multiple features are usually extracted and We implemented this interactive approach in our tool, JFlow. stored in the database. The bottleneck in the indexing stage JFlow statically analyzes the code and reports back to the comes from the number of features extracted and the number developer. As a tool, JFlow has high utility and is useful of images that need to be analyzed (typically in the hundreds in many scenarios. When the analyses are successful, the of thousands for a decent size database). tool performs the transformation, generating source code that Figure1 shows the indexing loop for LIRe with four feature targets the constructs of a parallel library. Even when the extractors. Consider a developer who decides to parallelize analyses fail, the tool is useful — it pinpoints and reports the this loop using flow-based parallelism. She decides to partition problems. This allows the developer to either fix the problems each feature extractor into its own node. The flow graph for and retry, or proceed with the transformation directly if she such a partition, i.e., a pipeline, is shown on the right hand side deems the reported problems innocuous (e.g., as determined by of the figure. How could she make use of JFlow to parallelize her suite of test cases). JFlow can even be used exploratorily: her code? Three steps: annotate, extract nodes and invert loop. if the developer wishes to inspect for data races, she could First, she would need to annotate her desired partitioning; use the tool to analyze the code, view the results and forego we use simple comments for now. While there have been prior the transformation step.
Load more

Recommended publications
  • Integrating Stream Parallelism and Task Parallelism in a Dataflow Programming Model
    RICE UNIVERSITY Integrating Stream Parallelism and Task Parallelism in a Dataflow Programming Model by Drago¸sDumitru Sb^ırlea A Thesis Submitted in Partial Fulfillment of the Requirements for the Degree Master of Science Approved, Thesis Committee: Vivek Sarkar Professor of Computer Science E.D. Butcher Chair in Engineering Keith D. Cooper L. John and Ann H. Doerr Professor of Computational Engineering Lin Zhong Associate Professor of Electrical and Computer Engineering Jun Shirako Research Scientist Computer Science Department Houston, Texas September 3rd, 2011 ABSTRACT Integrating Stream Parallelism and Task Parallelism in a Dataflow Programming Model by Drago¸sDumitru Sb^ırlea As multicore computing becomes the norm, exploiting parallelism in applications becomes a requirement for all software. Many applications exhibit different kinds of parallelism, but most parallel programming languages are biased towards a specific paradigm, of which two common ones are task and streaming parallelism. This results in a dilemma for programmers who would prefer to use the same language to exploit different paradigms for different applications. Our thesis is an integration of stream- parallel and task-parallel paradigms can be achieved in a single language with high programmability and high resource efficiency, when a general dataflow programming model is used as the foundation. The dataflow model used in this thesis is Intel's Concurrent Collections (CnC). While CnC is general enough to express both task-parallel and stream-parallel paradigms, all current implementations of CnC use task-based runtime systems that do not de- liver the resource efficiency expected from stream-parallel programs. For streaming programs, this use of a task-based runtime system is wasteful of computing cycles and makes memory management more difficult than it needs to be.
    [Show full text]
  • Dataflow Programming Model for Reconfigurable Computing Laurent Gantel, Amel Khiar, Benoît Miramond, Mohamed El Amine Benkhelifa, Fabrice Lemonnier, Lounis Kessal
    Dataflow Programming Model For Reconfigurable Computing Laurent Gantel, Amel Khiar, Benoît Miramond, Mohamed El Amine Benkhelifa, Fabrice Lemonnier, Lounis Kessal To cite this version: Laurent Gantel, Amel Khiar, Benoît Miramond, Mohamed El Amine Benkhelifa, Fabrice Lemonnier, et al.. Dataflow Programming Model For Reconfigurable Computing. 6th International Workshop on Reconfigurable Communication-centric Systems-on-Chip (ReCoSoC), Jun 2011, Montpellier, France. pp.1-8, 10.1109/ReCoSoC.2011.5981505. hal-00623674 HAL Id: hal-00623674 https://hal.archives-ouvertes.fr/hal-00623674 Submitted on 14 Sep 2011 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Dataflow Programming Model For Reconfigurable Computing L. Gantel∗† and A. Khiar∗ and B. Miramond∗ and A. Benkhelifa∗ and F. Lemonnier† and L. Kessal∗ ∗ ETIS Laboratory – UMR CNRS 8051 † Embedded System Lab Universityof Cergy-Pontoise/ ENSEA Thales Research and Technology 6,avenueduPonceau 1,avenueAugustinFresnel 95014 Cergy-Pontoise, FRANCE 91767 Palaiseau, FRANCE Email {firstname.name}@ensea.fr Email {firstname.name}@thalesgroup.com Abstract—This paper addresses the problem of image process- system, such as sockets. ing algorithms implementation onto dynamically and reconfig- It reduces significantly the work of application programmers urable architectures. Today, these Systems-on-Chip (SoC), offer by relieving them of tedious and error-prone programming.
    [Show full text]
  • Machine Learning with Multiscale Dataflow Computing for High Energy Physics
    Machine Learning with Multiscale Dataflow Computing for High Energy Physics 10.7.2019 Outline • Dataflow Concept and Maxeler • Dataflow for ML and Use Cases • Dataflow Programming Introduction • Hands-on Example 2 Dataflow Concept and Maxeler 10.7.2019 Programmable Spectrum Control-flow processors Dataflow processor GK110 Single-Core CPU Multi-Core Several-Cores Many-Cores Dataflow Increasing Parallelism (#cores) Increasing Core Complexity ( Hardware Clock Frequency ) GPU (NVIDIA, AMD) Intel, AMD Tilera, XMOS etc... Maxeler Hybrid e.g. AMD Fusion, IBM Cell 4 Maxeler Dataflow Engines (DFEs) • Largest Reconfigurable DataflowLMEM Engine (DFE) (Large Memory) Chip 4-96GB MaxRing • O(1k) multipliers High bandwidth memory link Interconnect • O(100k) logic cells Reconfigurable compute fabric • O(10MB) of on-chip SRAM MaxRing Dataflow cores & * links FMEM (fast • O(10GB) of on-card DRAM memory) • DFE-to-DFE interconnect Link to main data network * approaching 128GB on a ¾, single slot PCIe card 5 5 Maxeler Dataflow Engines (DFEs) CPU DFE (Dataflow Engine) 6 Control Flow versus Data Flow • Control Flow: • It is all about how instructions “move” • Data may move along with instructions (secondary issue) • Order of computation is the key • Data Flow: • It is about how data moves through a set of “instructions” in 2D space • Data moves will trigger control • Data availability, transformations and operation latencies are the key 7 Area Utilisation of Modern Chips AMD Bulldozer CPU Nvidia Tesla V100 GPU 8 DFE Area Utilisation 9 Dataflow Computing • A custom chip for a specific application • No instructions ➝ no instruction decode logic • No branches ➝ no branch prediction • Explicit parallelism ➝ no out-of-order scheduling • Data streamed onto-chip ➝ no multi-level caches Memory (Lots (Lots of) Rest of the My Dataflow world Engine 10 Dataflow Computing • Single worker builds a single • Each component is added to bicycle from a group of parts the bicycle in a production line.
    [Show full text]
  • Graceful Language Extensions and Interfaces
    Graceful Language Extensions and Interfaces by Michael Homer A thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy Victoria University of Wellington 2014 Abstract Grace is a programming language under development aimed at ed- ucation. Grace is object-oriented, imperative, and block-structured, and intended for use in first- and second-year object-oriented programming courses. We present a number of language features we have designed for Grace and implemented in our self-hosted compiler. We describe the design of a pattern-matching system with object-oriented structure and minimal extension to the language. We give a design for an object-based module system, which we use to build dialects, a means of extending and restricting the language available to the programmer, and of implementing domain-specific languages. We show a visual programming interface that melds visual editing (à la Scratch) with textual editing, and that uses our dialect system, and we give the results of a user experiment we performed to evaluate the usability of our interface. ii ii Acknowledgments The author wishes to acknowledge: • James Noble and David Pearce, his supervisors; • Andrew P. Black and Kim B. Bruce, the other designers of Grace; • Timothy Jones, a coauthor on a paper forming part of this thesis and contributor to Minigrace; • Amy Ruskin, Richard Yannow, and Jameson McCowan, coauthors on other papers; • Daniel Gibbs, Jan Larres, Scott Weston, Bart Jacobs, Charlie Paucard, and Alex Sandilands, other contributors to Minigrace; • Gilad Bracha, Matthias Felleisen, and the other (anonymous) review- ers of papers forming part of this thesis; • the participants in his user study; • David Streader, John Grundy, and Laurence Tratt, examiners of the thesis; • and Alexandra Donnison, Amy Chard, Juanri Barnard, Roma Kla- paukh, and Timothy Jones, for providing feedback on drafts of this thesis.
    [Show full text]
  • Graceful Language Extensions and Interfaces
    Graceful Language Extensions and Interfaces by Michael Homer A thesis submitted to the Victoria University of Wellington in fulfilment of the requirements for the degree of Doctor of Philosophy Victoria University of Wellington 2014 Abstract Grace is a programming language under development aimed at ed- ucation. Grace is object-oriented, imperative, and block-structured, and intended for use in first- and second-year object-oriented programming courses. We present a number of language features we have designed for Grace and implemented in our self-hosted compiler. We describe the design of a pattern-matching system with object-oriented structure and minimal extension to the language. We give a design for an object-based module system, which we use to build dialects, a means of extending and restricting the language available to the programmer, and of implementing domain-specific languages. We show a visual programming interface that melds visual editing (à la Scratch) with textual editing, and that uses our dialect system, and we give the results of a user experiment we performed to evaluate the usability of our interface. ii ii Acknowledgments The author wishes to acknowledge: • James Noble and David Pearce, his supervisors; • Andrew P. Black and Kim B. Bruce, the other designers of Grace; • Timothy Jones, a coauthor on a paper forming part of this thesis and contributor to Minigrace; • Amy Ruskin, Richard Yannow, and Jameson McCowan, coauthors on other papers; • Daniel Gibbs, Jan Larres, Scott Weston, Bart Jacobs, Charlie Paucard, and Alex Sandilands, other contributors to Minigrace; • Gilad Bracha, Matthias Felleisen, and the other (anonymous) review- ers of papers forming part of this thesis; • the participants in his user study; • and Roma Klapaukh, Juanri Barnard, Alexandra Donnison, Amy Chard, and Timothy Jones for providing feedback on drafts of this thesis.
    [Show full text]
  • Proceedings of the 8Th European Lisp Symposium Goldsmiths, University of London, April 20-21, 2015 Julian Padget (Ed.) Sponsors
    Proceedings of the 8th European Lisp Symposium Goldsmiths, University of London, April 20-21, 2015 Julian Padget (ed.) Sponsors We gratefully acknowledge the support given to the 8th European Lisp Symposium by the following sponsors: WWWLISPWORKSCOM i Organization Programme Committee Julian Padget – University of Bath, UK (chair) Giuseppe Attardi — University of Pisa, Italy Sacha Chua — Toronto, Canada Stephen Eglen — University of Cambridge, UK Marc Feeley — University of Montreal, Canada Matthew Flatt — University of Utah, USA Rainer Joswig — Hamburg, Germany Nick Levine — RavenPack, Spain Henry Lieberman — MIT, USA Christian Queinnec — University Pierre et Marie Curie, Paris 6, France Robert Strandh — University of Bordeaux, France Edmund Weitz — University of Applied Sciences, Hamburg, Germany Local Organization Christophe Rhodes – Goldsmiths, University of London, UK (chair) Richard Lewis – Goldsmiths, University of London, UK Shivi Hotwani – Goldsmiths, University of London, UK Didier Verna – EPITA Research and Development Laboratory, France ii Contents Acknowledgments i Messages from the chairs v Invited contributions Quicklisp: On Beyond Beta 2 Zach Beane µKanren: Running the Little Things Backwards 3 Bodil Stokke Escaping the Heap 4 Ahmon Dancy Unwanted Memory Retention 5 Martin Cracauer Peer-reviewed papers Efficient Applicative Programming Environments for Computer Vision Applications 7 Benjamin Seppke and Leonie Dreschler-Fischer Keyboard? How quaint. Visual Dataflow Implemented in Lisp 15 Donald Fisk P2R: Implementation of
    [Show full text]
  • Visualocv: Refined Dataflow Programming Interface for Opencv
    Southern Adventist University KnowledgeExchange@Southern MS in Computer Science Project Reports School of Computing Summer 8-17-2020 VisualOCV: Refined Dataflow ogrPr amming Interface for OpenCV John Boggess [email protected] Follow this and additional works at: https://knowledge.e.southern.edu/mscs_reports Part of the Graphics and Human Computer Interfaces Commons, and the Programming Languages and Compilers Commons Recommended Citation Boggess, John, "VisualOCV: Refined Dataflow ogrPr amming Interface for OpenCV" (2020). MS in Computer Science Project Reports. 5. https://knowledge.e.southern.edu/mscs_reports/5 This Article is brought to you for free and open access by the School of Computing at KnowledgeExchange@Southern. It has been accepted for inclusion in MS in Computer Science Project Reports by an authorized administrator of KnowledgeExchange@Southern. For more information, please contact [email protected]. VISUALOCV: REFINED DATAFLOW PROGRAMMING INTERFACE FOR OPENCV by John R. W. Boggess A PROJECT DEFENSE Presented to the Faculty of The School of Computing at the Southern Adventist University In Partial Fulfilment of Requirements For the Degree of Master of Science Major: Computer Science Under the Supervision of Professor Hall Collegedale, Tennessee August 4, 2020 VisualOVC: Refined Dataflow Programming Interface for OpenCV Approved by: Professor Tyson Hall, Adviser Professor Scot Anderson Professor Robert Ord´o˜nez Date Approved VISUALOCV: REFINED DATAFLOW PROGRAMMING INTERFACE FOR OPENCV John R. W. Boggess Southern Adventist University, 2020 Adviser: Tyson Hall, Ph.D. OpenCV is a popular tool for developing computer vision algorithms; however, prototyping OpenCV-based algorithms is a time consuming and iterative process. VisualOCV is an open source tool to help users better understand and create computer vision algorithms.
    [Show full text]
  • Synchronous Functional Programming: the Lucid Synchrone Experiment ∗
    Synchronous Functional Programming: The Lucid Synchrone Experiment ∗ Paul Caspi † Gr´egoire Hamon Marc Pouzet ‡ VERIMAG The MathWorks LRI, Univ. Paris-Sud 11 Grenoble Boston Orsay 1 Introduction Lucid Synchrone is a programming language dedicated to the design of reactive systems. It is based on the synchronous model of Lustre [25] which it extends with features usually found in functional languages such as higher-order or constructed data-types. The language is equipped with several static analysis, all expressed as special type-systems and used to ensure the absence of certain run-time errors on the final application. It provides, in particular, automatic type and clock inference and statically detects initialization issues or dead-locks. Finally, the language offers both data-flow and automata-based programming inside a unique framework. This chapter is a tutorial introduction to the language. Its aim is to show that techniques can be applied, at the language level, to ease the specification, implementation, and verification of reactive systems. 1.1 Programming Reactive Systems We are interested in programming languages dedicated to the design of reactive systems [31]. Such systems interact continuously with their external environment and have to meet strong temporal constraints: emergency braking systems, plane autopilots, electronic stopwatches, etc. In the mid-70’s, with the shift from mechanical and electronic systems to logical systems, the question of the computer-based implementation of reactive systems arose. At first, these implementations used available general-purpose programming languages, such as assembly, C, or Ada. Low-level languages were prominent as they give strong and precise control over the resources.
    [Show full text]
  • Network Programming Languages
    1 Network Programming Languages Robert Soulé University of Lugano 2 Languages A programming language provides abstractions and ways to compose those abstractions The programming languages you are familiar with are models of computer systems They provide abstractions for data and computation 3 Abstractions abstractions compositions addresses, registers, Assembly languages sequences of instructions instructions, labels booleans, arithmetic, loops, sequences of statements, Procedural languages arrays, procedures procedure calls objects, methods, fields, method invocation, OO languages classes inheritance first-class functions, function application, type Functional languages algebraic data types constructors 4 Domain Specific Languages A general-purpose language provides abstractions for modeling computation A domain-specific language provides abstractions for other domains 5 Benefits of a DSL Can enable optimizations Can ensure program correctness Provide abstractions tailored to a particular domain Allow programmers to use emerging technologies 6 Enabling Optimizations StreamIt: a streaming language designed for static optimization … float->float pipeline ABC { A add float->float filter A() { FIFO queue work pop … push 2 { 2 /*details elided*/ } 3 B pops 3 per firing } B add float->float filter B() { B pushes 1 per firing work pop 3 push 1 { 1 /*details elided*/ } 2 } C add float->float filter C() { work pop 2 push … { … /*details elided*/ } } } Statically known push/pop rates for every operator. 7 Enabling Optimizations … A A A A A A 2 3 B
    [Show full text]
  • Dataflow Supercomputers
    Dataflow Supercomputers Michael J. Flynn Maxeler Technologies and Stanford University Outline ◦ History • Dataflow as a supercomputer technology • openSPL: generalizing the dataflow programming model • Optimizing the hardware for dataflow 3 The great parallel precessor debate of 1967 • Amdahl espouses to sequential machine and posits Amdahl’s law • Danial Slotnick (father of ILLIAC IV) posits the parallel approach while recognizing the problem of programming 4Slotnick’s law “The parallel approach to computing does require that some original thinking be done about numerical analysis and data management in order to secure efficient use. In an environment which has represented the absence of the need to think as the highest virtue this is a decided disadvantage.” -Daniel Slotnick (1967) ….Speedup in parallel processing is achieved by programming effort…… Michael J Flynn The (multi core) Parallel Processor Problem • Efficient distribution of tasks • Inter-node communications (data assembly & dispatch) reduces computational efficiency: speedup/nodes • Memory bottleneck limitations • The sequential programming model: Layers of abstraction hide critical sources of and limits to efficient parallel execution Is there a magic compiler bullet? May’s laws May’s first law: Software efficiency halves every 18 months, exactly compensating for Moore’s Law May’s second law: Compiler technology doubles efficiency no faster than once a decade David May 7 Dataflow as a supercomputer technology • Looking for another way. Some experience from Maxeler • Dataflow is an old technology (70’s and 80’s), conceptually creates and ideal machine to match the program but the interconnect problem was insurmountable for the day. • Today’s FPGAs have come a long way and enable an emulation of the dataflow machine.
    [Show full text]
  • Developments in Dataflow Programming
    University of Exeter Department of Computer Science Developments in Dataflow Programming Daniel Julius Maxwell April 2018 Supervised by Dr Antony Galton & Prof. Jonathan Fieldsend Submitted by Daniel Julius Maxwell to the University of Exeter as a thesis for the degree of Doctor of Philosophy in Computer Science, April 2018. This thesis is available for Library use on the understanding that it is copyright material and that no quotation from the thesis may be published without proper acknowledgement. I certify that all material in this thesis which is not my own work has been identi- fied and that no material has previously been submitted and approved for the award of a degree by this or any other University. (signature) ................................................................................................. Abstract Dataflow has historically been motivated either by parallelism or programmability or some combination of the two. This work, rather than being directed primarily at parallelism or programmability, is instead aimed at maximising the overall utility to the programmer of the system at large. This means that it aims to result in a system in which it is easy to create well-constructed, flexible programs that comply with the principles of software engineering and architecture, but also that the proposed system should be capable at performing practical real-life tasks and should be as widely applicable as can be achieved. With those aims in mind, this project has four goals: • to argue for a unified global dataflow coordination system, extensible to be able to accommodate components of any form that may exist now or in the future; • to establish a link between the design of such a system and the principles of software engineering and architecture; • to design a dataflow coordination system based on those principles, aiming where possible to embed them in the design so that they become easy or unthinking for programmers to apply; and • to implement and test components of the proposed system, using it to build a set of three sample algorithms.
    [Show full text]
  • Easy Dataflow Programming in Clusters with UPC++ Depspawn
    This is the author's version of an article that has been published in this journal. Changes were made to this version by the publisher prior to publication. The final version of record is available at http://dx.doi.org/10.1109/TPDS.2018.2884716 IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 1 Easy Dataflow Programming in Clusters with UPC++ DepSpawn Basilio B. Fraguela, Diego Andrade Abstract—The Partitioned Global Address Space (PGAS) programming model is one of the most relevant proposals to improve the ability of developers to exploit distributed memory systems. However, despite its important advantages with respect to the traditional message-passing paradigm, PGAS has not been yet widely adopted. We think that PGAS libraries are more promising than languages because they avoid the requirement to (re)write the applications using them, with the implied uncertainties related to portability and interoperability with the vast amount of APIs and libraries that exist for widespread languages. Nevertheless, the need to embed these libraries within a host language can limit their expressiveness and very useful features can be missing. This paper contributes to the advance of PGAS by enabling the simple development of arbitrarily complex task-parallel codes following a dataflow approach on top of the PGAS UPC++ library, implemented in C++. In addition, our proposal, called UPC++ DepSpawn, relies on an optimized multithreaded runtime that provides very competitive performance, as our experimental evaluation shows. Index Terms—libraries, parallel programming models, distributed memory, multithreading, programmability, dataflow F 1 INTRODUCTION HILE the exploitation of parallelism is never trivial, Namely, the private space is always the one that can be more W this is particularly true in the case of distributed efficiently accessed and the shared local space is accessible memory systems such as clusters.
    [Show full text]