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Journal articles on the topic 'Streaming Dataflows'

Author: Grafiati
Published: 4 June 2021
Last updated: 6 September 2023

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1

Chen, Qinyu, Yuxiang Fu, Wenqing Song, et al. "An Efficient Streaming Accelerator for Low Bit-Width Convolutional Neural Networks." Electronics 8, no. 4 (2019): 371. http://dx.doi.org/10.3390/electronics8040371.

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Convolutional Neural Networks (CNNs) have been widely applied in various fields, such as image recognition, speech processing, as well as in many big-data analysis tasks. However, their large size and intensive computation hinder their deployment in hardware, especially on the embedded systems with stringent latency, power, and area requirements. To address this issue, low bit-width CNNs are proposed as a highly competitive candidate. In this paper, we propose an efficient, scalable accelerator for low bit-width CNNs based on a parallel streaming architecture. With a novel coarse grain task pa
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2

Barish, G., and C. A. Knoblock. "An Expressive Language and Efficient Execution System for Software Agents." Journal of Artificial Intelligence Research 23 (June 1, 2005): 625–66. http://dx.doi.org/10.1613/jair.1548.

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Software agents can be used to automate many of the tedious, time-consuming information processing tasks that humans currently have to complete manually. However, to do so, agent plans must be capable of representing the myriad of actions and control flows required to perform those tasks. In addition, since these tasks can require integrating multiple sources of remote information ? typically, a slow, I/O-bound process ? it is desirable to make execution as efficient as possible. To address both of these needs, we present a flexible software agent plan language and a highly parallel execution
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Pan, Zhenxuan, Tao Wu, Qingwen Zhao, et al. "GeaFlow: A Graph Extended and Accelerated Dataflow System." Proceedings of the ACM on Management of Data 1, no. 2 (2023): 1–27. http://dx.doi.org/10.1145/3589771.

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GeaFlow is a distributed dataflow system optimized for streaming graph processing, and has been widely adopted at Ant Group, serving various scenarios ranging from risk control of financial activities to analytics on social networks and knowledge graphs. It is built on top of a base with full-fledged stateful stream processing capabilities, extended with a series of graph-aware optimizations to address the space explosion and programming complexity issues of conventional join-based approaches. We propose new state backends and streaming operators that facilitate processing on dynamic graph-str
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Vo, Huy T., Daniel K. Osmari, Brian Summa, João L. D. Comba, Valerio Pascucci, and Cláudio T. Silva. "Streaming-Enabled Parallel Dataflow Architecture for Multicore Systems." Computer Graphics Forum 29, no. 3 (2010): 1073–82. http://dx.doi.org/10.1111/j.1467-8659.2009.01704.x.

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5

Imran, Muhammad, Gábor E. Gévay, Jorge-Arnulfo Quiané-Ruiz, and Volker Markl. "Fast datalog evaluation for batch and stream graph processing." World Wide Web 25, no. 2 (2022): 971–1003. http://dx.doi.org/10.1007/s11280-021-00960-w.

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AbstractImplementing complex algorithms for big data, artificial intelligence, and graph processing requires enormous effort. Succinct, declarative programs to solve complex problems that can be efficiently executed for batching and streaming data are in demand. This paper presents Nexus, a distributed Datalog evaluation system. It evaluates Datalog programs using the semi-naive algorithm for batch and streaming data using incremental and asynchronous iteration. Furthermore, we evaluate Datalog programs with aggregates to determine the advantages of implementing the semi-naive algorithm using
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Timcheck, Stephen, and Jeremy Buhler. "Reducing queuing impact in streaming applications with irregular dataflow." Parallel Computing 109 (March 2022): 102863. http://dx.doi.org/10.1016/j.parco.2021.102863.

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7

Satyanarayan, Arvind, Ryan Russell, Jane Hoffswell, and Jeffrey Heer. "Reactive Vega: A Streaming Dataflow Architecture for Declarative Interactive Visualization." IEEE Transactions on Visualization and Computer Graphics 22, no. 1 (2016): 659–68. http://dx.doi.org/10.1109/tvcg.2015.2467091.

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8

Wei, Haitao, Stéphane Zuckerman, Xiaoming Li, and Guang R. Gao. "A Dataflow Programming Language and its Compiler for Streaming Systems." Procedia Computer Science 29 (2014): 1289–98. http://dx.doi.org/10.1016/j.procs.2014.05.116.

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SCORZELLI, GIORGIO, ALBERTO PAOLUZZI, and VALERIO PASCUCCI. "PARALLEL SOLID MODELING USING BSP DATAFLOW." International Journal of Computational Geometry & Applications 18, no. 05 (2008): 441–67. http://dx.doi.org/10.1142/s0218195908002714.

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We introduce a parallel approach to geometric modeling of complex objects and scenes, combining a dataflow streaming of BSP trees with a partition of the object space into independent portions, to be evaluated in parallel with minimal interprocess communication. Binary Space Partition (BSP) is a space index used in graphics for hidden-surface removal and animation. We use BSP trees with fuzzy leaves as a progressive representation of solid meshes. Our approach is implemented as a dataflow with processes that progress concurrently, where each refinement of the input to a process is mapped insta
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Guerriero, Michele, Damian Andrew Tamburri, and Elisabetta Di Nitto. "StreamGen." ACM Transactions on Software Engineering and Methodology 30, no. 1 (2021): 1–30. http://dx.doi.org/10.1145/3408895.

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Distributed streaming applications, i.e., applications that process massive streams of data in a distributed fashion, are becoming increasingly popular to tame the velocity and the volume of Big Data . Nevertheless, the widespread adoption of data-intensive processing is still limited by the non-trivial design paradigms involved, which deal with the unboundedness and volume of involved data streams and by the many distributed streaming platforms, each with its own characteristics and APIs. In this article, we present StreamGen, a Model-Driven Engineering tool to simplify the design of such str
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11

Misale, Claudia, Maurizio Drocco, Marco Aldinucci, and Guy Tremblay. "A Comparison of Big Data Frameworks on a Layered Dataflow Model." Parallel Processing Letters 27, no. 01 (2017): 1740003. http://dx.doi.org/10.1142/s0129626417400035.

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In the world of Big Data analytics, there is a series of tools aiming at simplifying programming applications to be executed on clusters. Although each tool claims to provide better programming, data and execution models–for which only informal (and often confusing) semantics is generally provided–all share a common underlying model, namely, the Dataflow model. The model we propose shows how various tools share the same expressiveness at different levels of abstraction. The contribution of this work is twofold: first, we show that the proposed model is (at least) as general as existing batch a
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12

Kim, Hweesoo, Sunjung Lee, Jaewan Choi, and Jung Ho Ahn. "Row-Streaming Dataflow Using a Chaining Buffer and Systolic Array+ Structure." IEEE Computer Architecture Letters 20, no. 1 (2021): 34–37. http://dx.doi.org/10.1109/lca.2021.3054371.

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13

Korkhov, Vladimir, Dmitry Vasyunin, Adianto Wibisono, et al. "VLAM-G: Interactive Data Driven Workflow Engine for Grid-Enabled Resources." Scientific Programming 15, no. 3 (2007): 173–88. http://dx.doi.org/10.1155/2007/812036.

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Grid brings the power of many computers to scientists. However, the development of Grid-enabled applications requires knowledge about Grid infrastructure and low-level API to Grid services. In turn, workflow management systems provide a high-level environment for rapid prototyping of experimental computing systems. Coupling Grid and workflow paradigms is important for the scientific community: it makes the power of the Grid easily available to the end user. The paradigm of data driven workflow execution is one of the ways to enable distributed workflow on the Grid. The work presented in this p
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14

Hosseinabady, Mohammad, and Jose Luis Nunez-Yanez. "A Streaming Dataflow Engine for Sparse Matrix-Vector Multiplication Using High-Level Synthesis." IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems 39, no. 6 (2020): 1272–85. http://dx.doi.org/10.1109/tcad.2019.2912923.

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15

Akidau, Tyler, Edmon Begoli, Slava Chernyak, et al. "Watermarks in stream processing systems." Proceedings of the VLDB Endowment 14, no. 12 (2021): 3135–47. http://dx.doi.org/10.14778/3476311.3476389.

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Streaming data processing is an exercise in taming disorder: from oftentimes huge torrents of information, we hope to extract powerful and timely analyses. But when dealing with streaming data, the unbounded and temporally disordered nature of real-world streams introduces a critical challenge: how does one reason about the completeness of a stream that never ends? In this paper, we present a comprehensive definition and analysis of watermarks , a key tool for reasoning about temporal completeness in infinite streams. First, we describe what watermarks are and why they are important, highlight
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16

Nelson, Andrew, Kees Goossens, and Benny Akesson. "Dataflow formalisation of real-time streaming applications on a Composable and Predictable Multi-Processor SOC." Journal of Systems Architecture 61, no. 9 (2015): 435–48. http://dx.doi.org/10.1016/j.sysarc.2015.04.001.

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17

Wu, Yao, Long Zheng, Brian Heilig, and Guang R. Gao. "HAMR: A dataflow-based real-time in-memory cluster computing engine." International Journal of High Performance Computing Applications 31, no. 5 (2016): 361–74. http://dx.doi.org/10.1177/1094342016672080.

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As the attention given to big data grows, cluster computing systems for distributed processing of large data sets become the mainstream and critical requirement in high performance distributed system research. One of the most successful systems is Hadoop, which uses MapReduce as a programming/execution model and takes disks as intermedia to process huge volumes of data. Spark, as an in-memory computing engine, can solve the iterative and interactive problems more efficiently. However, currently it is a consensus that they are not the final solutions to big data due to a MapReduce-like programm
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18

Atkinson, Eric, Guillaume Baudart, Louis Mandel, Charles Yuan, and Michael Carbin. "Statically bounded-memory delayed sampling for probabilistic streams." Proceedings of the ACM on Programming Languages 5, OOPSLA (2021): 1–28. http://dx.doi.org/10.1145/3485492.

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Probabilistic programming languages aid developers performing Bayesian inference. These languages provide programming constructs and tools for probabilistic modeling and automated inference. Prior work introduced a probabilistic programming language, ProbZelus, to extend probabilistic programming functionality to unbounded streams of data. This work demonstrated that the delayed sampling inference algorithm could be extended to work in a streaming context. ProbZelus showed that while delayed sampling could be effectively deployed on some programs, depending on the probabilistic model under con
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19

Chaturvedi, Shilpa, Sahil Tyagi, and Yogesh Simmhan. "Cost-effective Sharing of Streaming Dataflows for IoT Applications." IEEE Transactions on Cloud Computing, 2019, 1. http://dx.doi.org/10.1109/tcc.2019.2921371.

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20

Fradet, Pascal, Alain Girault, Ruby Krishnaswamy, Xavier Nicollin, and Arash Shafiei. "RDF: A Reconfigurable Dataflow Model of Computation." ACM Transactions on Embedded Computing Systems, June 27, 2022. http://dx.doi.org/10.1145/3544972.

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Dataflow Models of Computation (MoCs) are widely used in embedded systems, including multimedia processing, digital signal processing, telecommunications, and automatic control. In a dataflow MoC, an application is specified as a graph of actors connected by FIFO channels. One of the first and most popular dataflow MoCs, Synchronous Dataflow (SDF), provides static analyses to guarantee boundedness and liveness, which are key properties for embedded systems. However, SDF and most of its variants lacks the capability to express the dynamism needed by modern streaming applications. In particular,
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21

Timcheck, Stephen, and Jeremy Buhler. "Interruptible Nodes: Reducing Queueing Costs in Irregular Streaming Dataflow Applications on Wide-SIMD Architectures." International Journal of Parallel Programming, December 5, 2022. http://dx.doi.org/10.1007/s10766-022-00745-2.

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AbstractStreaming dataflow applications are an attractive target to parallelize on wide-SIMD processors such as GPUs. These applications can be expressed as a pipeline of compute nodes connected by edges, which feed outputs from one node to the next. Streaming applications often exhibit irregular dataflow, where the amount of output produced for one input is unknown a priori. Inserting finite queues between pipeline nodes can ameliorate the impact of irregularity and improve SIMD lane occupancy. The sizing of these queues is driven by both performance and safety considerations- relative queue
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22

Leach, Stephen, and James A. D. W. Anderson. "Slipstream Transcomputation of the Fast Fourier Transform." Transmathematica, December 24, 2019. http://dx.doi.org/10.36285/tm.29.

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 We have been designing and investigating a family of fine-grained architectures that exploits exception-free arithmetic to implement a statically assigned, systolic dataflow technique that we refer to as ‘slip-streaming’. In this paper, we report on the simulation of a slip-streamed implementation of the fast Fourier transformation, its properties and compilation challenges. The algorithm is extended to include dynamically computing a match against a fixed profile of weights.
 
 
 
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23

Milan, Petro Junior, Hongqian Rong, Craig Michaud, Naoufal Layad, Zhengchun Liu, and Ryan Coffee. "Enabling real-time adaptation of machine learning models at x-ray Free Electron Laser facilities with high-speed training optimized computational hardware." Frontiers in Physics 10 (October 17, 2022). http://dx.doi.org/10.3389/fphy.2022.958120.

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The emergence of novel computational hardware is enabling a new paradigm for rapid machine learning model training. For the Department of Energy’s major research facilities, this developing technology will enable a highly adaptive approach to experimental sciences. In this manuscript we present the per-epoch and end-to-end training times for an example of a streaming diagnostic that is planned for the upcoming high-repetition rate x-ray Free Electron Laser, the Linac Coherent Light Source-II. We explore the parameter space of batch size and data parallel training across multiple Graphics Proce
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Ioannou, Lenos, and Suhaib A. Fahmy. "Streaming Overlay Architecture for Lightweight LSTM Computation on FPGA SoCs." ACM Transactions on Reconfigurable Technology and Systems, June 21, 2022. http://dx.doi.org/10.1145/3543069.

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Long-Short Term Memory (LSTM) networks, and Recurrent Neural Networks (RNNs) in general, have demonstrated their suitability in many time series data applications, especially in Natural Language Processing (NLP). Computationally, LSTMs introduce dependencies on previous outputs in each layer that complicate their computation and the design of custom computing architectures, compared to traditional feed-forward networks. Most neural network acceleration work has focused on optimising the core matrix-vector operations on highly capable FPGAs in server environments. Research that considers the em
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Leon, Hany, Maged Georgy, and Moheeb Ibrahim. "MULTIPLE-ACTOR COST CONTROL AND MANAGEMENT SYSTEM: THE UML BLUEPRINT." Proceedings of International Structural Engineering and Construction 2, no. 1 (2015). http://dx.doi.org/10.14455/isec.res.2015.175.

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Proper cost control and management is pivotal for the success of construction projects and companies alike. However, several of the systems in usage are fragmented, especially in regards to how cost data at the project, department and company levels are portrayed. This is particularly observed when companies carry a sizeable portfolio of projects in diverse and sometimes remote locations. This calls for establishing an integrated cost control and management system capable of addressing these deficiencies while portraying the cost status at various levels of the company’s chain of command. Pape
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