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IEEE Symposium on Visual Languages and Human-Centric Computing (VL/HCC)

作者： Altendeitering, Marcel Schimmler, Sonja Fraunhofer ISST Dortmund Germany Fraunhofer FOKUS Weizenbaum Inst Berlin Germany

ISBN: (纸本)9781728169019

Visual programming languages are a common way for enabling end users to create simple, customized programs in their smart homes. Although visual programming aims at lowering the syntactic and cognitive barriers existing languages lack a simple method for accessing services of smart spaces surrounding the home and therefore restrain the innovative capabilities of users. For example, accessing information about energy consumption or smart parking is often cumbersome and limited to users with advanced technical skills. Based on expert interviews, we extended the existing visual programming language Node-RED with functionalities that enable users to easily access data and services within and beyond their smart homes. Using this prototype, we performed a first evaluation of our solution with some inhabitants of smart homes.

关键词： visual programming languages data-flow programming smart homes smart spaces smart services

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End-user Development for Smart Spaces: A Comparison of Block and data-flow programming 11

End-user Development for Smart Spaces: A Comparison of Block...

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11th International Conference on Smart Cities and Green ICT Systems (SMARTGREENS)

作者： Altendeitering, Marcel Schimmler, Sonja Fraunhofer ISST Dortmund Germany Fraunhofer FOKUS Berlin Germany

ISBN: (纸本)9789897585722

Block and data-flow programming are well-known concepts for enabling end users to visually create their own customized solutions. They both offer comprehensive visual interfaces and are becoming popular within the smart spaces domain. However, there is currently no systematic, comparative evaluation of the two concepts in the domain. In this user study, we implemented two prototypes for block and data-flow programming and compared their performance on typical usage scenarios in common smart spaces. We used a mixed methods approach of quantitative and qualitative analysis to gain an in-depth understanding of the user experience. Our results show that data-flow programming is overall better received by users than block programming and is considered being state-of-the art and visually more appealing. For block programming, our results reveal that participants appreciate the playful character and the feedback provided. Our study contributes to the improvement of block and data-flow solutions in place and discusses aspects relevant to the future advancement of end-user development in smart spaces.

关键词： Block programming data-flow programming Visual Languages Smart Spaces

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LOOPED SCHEDULES FOR data-flow DESCRIPTIONS OF MULTIRATE SIGNAL-PROCESSING ALGORITHMS

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FORMAL METHODS IN SYSTEM DESIGN 1994年第3期5卷 183-205页

作者： BHATTACHARYYA, SS LEE, EA UNIV CALIF BERKELEY DEPT ELECT ENGN & COMP SCIBERKELEYCA 94720

The synchronous dataflow (SDF) programming paradigm has been used extensively in design environments for multirate signal processing applications. In this paradigm, the repetition of computations is specified by the relative rates at which the computations consume and produce data. This implicit specification of iteration allows a compiler to easily explore alternative nested loop structures for the target code with respect to their effects on code size, buffering requirements and throughput. In this paper, we develop important relationships between the SDF description of an algorithm and the range of looping structures offered by this description, and we discuss how to improve code efficiency by applying these relationships.

关键词： data-flow programming MULTIRATE SIGNAL PROCESSING OPTIMIZING COMPILERS

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DDF Library: Enabling functional programming in a task-based model

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2021年 151卷 112-124页

作者： Ponce, Lucas M. Lezzi, Daniele Badia, Rosa M. Guedes, Dorgival Univ Fed Minas Gerais UFMG Belo Horizonte MG Brazil Ctr Nacl Supercomp BSC CNS Barcelona Supercomp Ctr Barcelona Spain CSIC Spanish Natl Res Council Barcelona Spain

In recent years, the areas of High-Performance Computing (HPC) and massive data processing (also know as Big data) have been in a convergence course, since they tend to be deployed on similar hardware. HPC systems have historically performed well in regular, matrix-based computations;on the other hand, Big data problems have often excelled in fine-grained, data parallel workloads. While HPC programming is mostly task-based, like COMPSs, popular Big data environments, like Spark, adopt the functional programming paradigm. A careful analysis shows that there are pros and cons to both approaches, and integrating them may yield interesting results. With that reasoning in mind, we have developed DDF, an API and library for COMPSs that allows developers to use Big data techniques while using that HPC environment. DDF has a functional-based interface, similar to many data Science tools, that allows us to use dynamic evaluation to adapt the task execution in run time. It brings some of the qualities of Big data programming, making it easier for application domain experts to write data Analysis jobs. In this article we discuss the API and evaluate the impact of the techniques used in its implementation that allow a more efficient COMPSs execution. In addition, we present a performance comparison with Spark in several application patterns. The results show that each technique significantly impacts the performance, allowing COMPSs to outperform Spark in many use cases. (C) 2021 Elsevier Inc. All rights reserved.

关键词： COMPSs Big data Performance evaluation data-flow programming

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Optimizing Iterative data-flow Scientific Applications Using Directed Cyclic Graphs

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IEEE ACCESS 2023年 11卷 51971-51984页

作者： Alvarez, David Beltran, Vicenc Barcelona Supercomp Ctr Barcelona 08034 Spain

data-flow programming models have become a popular choice for writing parallel applications as an alternative to traditional work-sharing parallelism. They are better suited to write applications with irregular parallelism that can present load imbalance. However, these programming models suffer from overheads related to task creation, scheduling and dependency management, limiting performance and scalability when tasks become too small. At the same time, many HPC applications implement iterative methods or multi-step simulations that create the same directed acyclic graphs of tasks on each iteration. By giving application programmers a way to express that a specific loop is creating the same task pattern on each iteration, we can create a single task directed acyclic graph (DAG) once and transform it into a cyclic graph. This cyclic graph is then reused for successive iterations, minimizing task creation and dependency management overhead. This paper presents the taskiter, a new construct we propose for the OmpSs-2 and OpenMP programming models, allowing the use of directed cyclic task graphs (DCTG) to minimize runtime overheads. Moreover, we present a simple immediate successor locality-aware heuristic that minimizes task scheduling overhead by bypassing the runtime task scheduler. We evaluate the implementation of the taskiter and the immediate successor heuristic in 8 iterative benchmarks. Using small task granularities, we obtain a geometric mean speedup of 2.56x over the reference OmpSs-2 implementation, and a 3.77x and 5.2x speedup over the LLVM and GCC OpenMP runtimes, respectively.

关键词： Taskiter data-flow programming ompss-2 openmp iterative applications

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Highly optimized quantum circuits synthesized via data-flow engines

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JOURNAL OF COMPUTATIONAL PHYSICS 2024年 500卷

作者： Rakyta, Peter Morse, Gregory Nadori, Jakab Majnay-Takacs, Zita Mencer, Oskar Zimboras, Zoltan Eotvos Lorand Univ Dept Phys Complex Syst Pazmany Peter Setany 1-A H-1117 Budapest Hungary Wigner Res Ctr Phys 29-33 Konkoly Thege Mikl Str H-1121 Budapest Hungary Eotvos Lorand Univ Dept Programming Languages & Compilers Pazmany Peter setany 1-a H-1117 Budapest Hungary Groq Co Maxeler Technol 16192 Coastal Hwy Lewes DE 19958 USA Algorithmiq Ltd Kanavakatu 3C Helsinki 00160 Finland

The formulation of quantum programs in terms of the fewest number of gate operations is crucial to retrieve meaningful results from the noisy quantum processors accessible these days. In this work, we demonstrate a use-case for Field Programmable Gate Array (FPGA) based data-flow engines (DFEs) to scale up variational quantum compilers to synthesize circuits up to 9-qubit programs. This gate decomposer utilizes a newly developed DFE quantum computer simulator that is designed to simulate arbitrary quantum circuit consisting of single qubit rotations and controlled two-qubit gates on FPGA chips. In our benchmark with the QISKIT package, the depth of the circuits produced by the SQUANDER package (with the DFE accelerator support) were less by 97% on average, while the fidelity of the circuits was still close to unity up to an error of similar to 10-4.

关键词： Quantum compilation Quantum computer simulation Circuit compression data-flow programming FPGA

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Integrating model-based optimization and program transformation to generate efficient parallel programs

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JOURNAL OF SYSTEMS ARCHITECTURE 1999年第6-7期45卷 465-482页

作者： Mitschele-Thiel, A Univ Erlangen Nurnberg D-91058 Erlangen Germany

The paper gives an overview on the DSPL programming environment, an integrated approach to automate system design and implementation of applications run on dedicated parallel systems. The programming environment consists of a data-flow language and an integrated set of tools. The tools automatically derive a software model from the given application program. Based on the model, the design decisions as the network topology, the task mapping and schedule as well as the optimal use of buffers are computed. Finally, the design decisions are automatically implemented by transforming the application program in executable code for the chosen processor network. The DSPL programming environment integrates model-based optimization techniques and program transformation techniques. The integration allows to include new aspects in the optimization process. Especially optimizations crucial to the semantics of the program can be included. The most important examples of such optimizations are the enforcement of the schedule in case of data-dependent execution of tasks and the transformation of buffered communication to unbuffered communication. Both aspects are crucial to the generation of efficient parallel implementations. The integration of the two aspects is supported by a formal framework. This allows to formally prove the correctness of the program optimizations performed by the programming environment. (C) 1999 Elsevier Science B.V. All rights reserved.

关键词： branch-and-bound data-flow programming parallel systems program transformation programming environments scheduling

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Scalable Task Parallelism for NUMA: A Uniform Abstraction for Coordinated Scheduling and Memory Management 16

Scalable Task Parallelism for NUMA: A Uniform Abstraction fo...

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International Conference on Parallel Architectures and Compilation (PACT)

作者： Drebes, Andi Pop, Antoniu Heydemann, Karine Cohen, Albert Drach, Nathalie Univ Manchester Sch Comp Sci Oxford Rd Manchester M13 9PL Lancs England UPMC Univ Paris 06 Sorbonne Univ UMR 7606 CNRSLIP6 4 Pl Jussieu F-75252 Paris 05 France INRIA 45 Rue Ulm F-75005 Paris France Ecole Normale Super 45 Rue Ulm F-75005 Paris France

ISBN: (纸本)9781450341219

Dynamic task-parallel programming models are popular on shared-memory systems, promising enhanced scalability, load balancing and locality. Yet these promises are undermined by non-uniform memory access (NUMA). We show that using NUMA-aware task and data placement, it is possible to preserve the uniform abstraction of both computing and memory resources for task-parallel programming models while achieving high data locality. Our data placement scheme guarantees that all accesses to task output data target the local memory of the accessing core. The complementary task placement heuristic improves the locality of task input data on a best effort basis. Our algorithms take advantage of data-flow style task parallelism, where the privatization of task data enhances scalability by eliminating false dependences and enabling fine-grained dynamic control over data placement. The algorithms are fully automatic, application independent, performance-portable across NUMA machines, and adapt to dynamic changes. Placement decisions use information about inter-task data dependences readily available in the run-time system and placement information from the operating system. We achieve 94% of local memory accesses on a 192-core system with 24 NUMA nodes, up to 5x higher performance than NUMA-aware hierarchical work stealing, and even 5.6x compared to static interleaved allocation. Finally, we show that state-of-the-art dynamic page migration by the operating system cannot catch up with frequent affinity changes between cores and data and thus fails to accelerate task-parallel applications.

关键词： Task-parallel programming NUMA Scheduling Memory allocation data-flow programming

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An approach to the development of programming software for distributed computing and information processing systems

An approach to the development of programming software for d...

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3rd International Conference on Software and data Technologies

作者： Kutepov, V. P. Malanin, V. N. Pankov, N. A. Tech Univ Moscow Power Res Inst Moscow Russia

The problem of software development for distributed and parallel computing systems is considered in the paper. Its original issue is in extending our projecting work on parallel programming languages and operational t... 详细信息

ISBN: (纸本)9789898111517

关键词： flow-graph stream parallel programming language distributed computing systems data-flow programming scheduling

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LOOP TRANSFORMATIONS FOR INTERFACE-BASED HIERARCHIES IN SDF GRAPHS

LOOP TRANSFORMATIONS FOR INTERFACE-BASED HIERARCHIES IN SDF ...

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21st IEEE International Conference on Application-Specific Systems, Architectures and Processors

作者： Piat, Jonathan Bhattacharyya, Shuvra S. Raulet, Mickael CNRS UMR 6164 IETR INSA Image & Remote Sensing Lab F-35043 Rennes France

ISBN: (纸本)9781424469673

data-flow has proven to be an attractive computation model for programming digital signal processing (DSP) applications. A restricted version of data-flow, termed synchronous data-flow (SDF), offers strong compile-time predictability properties, but has limited expressive power. A new type of hierarchy (Interface-based SDF) has been proposed allowing more expressivity while maintaining its predictability. One of the main problems with this hierarchical SDF model is the lack of trade-off between parallelism and network clustering. This paper presents a systematic method for applying an important class of loop transformation techniques in the context of interface-based SDF semantics. The resulting approach provides novel capabilities for integrating parallelism extraction properties of the targeted loop transformations with the useful modeling, analysis, and code reuse properties provided by SDF.

关键词： data-flow programming SDF graph Scheduling Code Generation Loop parallelization

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