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OpenMP as runtime for providing high-level stream parallelism on multi-cores

JOURNAL OF SUPERCOMPUTING 2022年第6期78卷 7655-7676页

作者： Hoffmann, Renato B. Loff, Junior Griebler, Dalvan Fernandes, Luiz G. Pontifical Catholic Univ Rio Grande do Sul PUCRS Sch Technol Porto Alegre RS Brazil Tres de Maio Educ Soc Setrem Lab Adv Res Cloud Comp LARCC Tres De Maio Brazil

OpenMP is an industry and academic standard for parallel programming. However, using it for developing parallel stream processing applications is complex and challenging. OpenMP lacks key programming mechanisms and abstractions for this particular domain. To tackle this problem, we used a high-level parallel programming framework (named SPar) for automatically generating parallel OpenMP code. We achieved this by leveraging SPar's language and its domain-specific code annotations for simplifying the complexity and verbosity added by OpenMP in this application domain. Consequently, we implemented a new compiler algorithm in SPar for automatically generating parallel code targeting the OpenMP runtime using source-to-source code transformations. The experiments in four different stream processing applications demonstrated that the execution time of SPar was improved up to 25.42% when using the OpenMP runtime. Additionally, our abstraction over OpenMP introduced at most 1.72% execution time overhead when compared to handwritten parallel codes. Furthermore, SPar significantly reduces the total source lines of code required to express parallelism with respect to plain OpenMP parallel codes.

关键词： parallel programming Stream processing C plus plus parallel patterns Pipeline Code transformation

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A Compiler for Agnostic Programming and Deployment of Big Data Analytics on Multiple Platforms

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IEEE TRANSACTIONS ON parallel AND DISTRIBUTED SYSTEMS 2019年第9期30卷 1920-1931页

作者： Di Martino, Beniamino Esposito, Antonio D'Angelo, Salvatore Maisto, Salvatore Augusto Nacchia, Stefania CINI Consorzio Interuniv Nazl Informat Roma I-00198 Rome Lazio Italy Univ Campania Luigi Vanvitelli Dept Engn I-81100 Caserta CE Italy

To run proper Big Data Analytics, small and medium enterprises (SMEs) need to acquire expertise, hardware and software, which often translates to relevant initial investments for activities not directly connected to the company's business. To reduce such investments, the TOREADOR project proposes a Big Data Analytics framework which supports users in devising their own Big Data solutions by keeping the inherent costs at a minimum, and leveraging pre-existent knowledge and expertise. Among the objectives of the TOREADOR framework is supporting developers in parallelizing and deploying their Big Data algorithms, in order to develop their own analytics solutions. This paper describes the Code-Based approach, adopted within the TOREADOR framework to parallelize users' algorithms and deploy them on distributed platforms, via the annotation of parallelizable code portions with parallelization primitives. The approach, which relies on the guidance of parallel patterns to implement the parallelization, and on Skeletons to automatically build execution and deployment templates, is realized through a source-to-source Compiler, also described in the present paper.

关键词： TOREADOR parallel compiler skeletons parallel patterns parallel primitives big data analytics

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DSParLib: A C plus plus Template Library for Distributed Stream parallelism

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INTERNATIONAL JOURNAL OF parallel PROGRAMMING 2022年第5-6期50卷 454-485页

作者： Loff, Junior Hoffmann, Renato B. Pieper, Ricardo Griebler, Dalvan Fernandes, Luiz G. Pontifical Catholic Univ Rio Grande do Sul PUCRS Sch Technol Porto Alegre RS Brazil Tres de Maio Fac SETREM Lab Adv Res Cloud Comp LARCC Tres De Maio Brazil

Stream processing applications deal with millions of data items continuously generated over time. Often, they must be processed in real-time and scale performance, which requires the use of distributed parallel computing resources. In C/C++, the current state-of-the-art for distributed architectures and High-Performance Computing is Message Passing Interface (MPI). However, exploiting stream parallelism using MPI is complex and error-prone because it exposes many low-level details to the programmer. In this work, we introduce a new parallel programming abstraction for implementing distributed stream parallelism named DSParLib. Our abstraction of MPI simplifies parallel programming by providing a pattern-based and building block-oriented development to inter-connect, model, and parallelize data streams found in modern applications. Experiments conducted with five different stream processing applications and the representative PARSEC Ferret benchmark revealed that DSParLib is efficient and flexible. Also, DSParLib achieved similar or better performance, required less coding, and provided simpler abstractions to express parallelism with respect to handwritten MPI programs.

关键词： parallel programming Distributed systems Stream processing parallel patterns MPI

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VPPE: A Novel Visual parallel Programming Environment

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INTERNATIONAL JOURNAL OF parallel PROGRAMMING 2019年第5-6期47卷 1117-1151页

作者： Quiroz-Fabian, Jose L. Roman-Alonso, Graciela Castro-Garcia, Miguel A. Buenabad-Chavez, Jorge Boukerche, Azzedine Aguilar-Cornejo, Manuel UAM Iztapalapa Dept Elect Engn Mexico City DF Mexico Univ Manchester Sch Comp Sci Manchester M13 9PL Lancs England Univ Ottawa Sch Informat Technol & Engn Ottawa ON Canada

parallel programming continues to be a challenging task despite the many advances in parallel architectures and their wide availability in the cloud. The need both to partition the workload among various processing elements and to specify communication between them to share code and data, and to coordinate their tasks, requires from the developer a deep understanding of the problem, the parallel architecture and the programming language used in order to develop efficient parallel applications. This problem can be reduced significantly through the use of visual programming languages to hide most aspects related to the specification of communication and processes management. This paper presents VPPE, a novel Visual parallel Programming Environment that allows developers to program parallel applications through organising workflows of interconnected icons. VPPE is a cloud environment that supports icons for specifying: I/O operations, workflow organisation, communication, and processing. Processing computing patterns supported so far include Single Program Multiple Data, Multiple Program Multiple Data, Pipeline, and Master-Slave. The paper highlights the design of VPPE based on a context-free graph grammar, its current implementation based on Java-MPI, its use in developing various parallel applications, and its evaluation compared to Java-MPI text-based programming.

关键词： parallel patterns Workflow Graph grammar Hyperedge replacement grammar Cloud computing

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Distributed-Memory FastFlow Building Blocks

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INTERNATIONAL JOURNAL OF parallel PROGRAMMING 2023年第1期51卷 1-21页

作者： Tonci, Nicolo Torquati, Massimo Mencagli, Gabriele Danelutto, Marco Univ Pisa Comp Sci Dept Pisa Italy

We present the new distributed-memory run-time system (RTS) of the C++-based open-source structured parallel programming library FastFlow. The new RTS enables the execution of FastFlow shared-memory applications written using its Building Blocks (BBs) on distributed systems with minimal changes to the original program. The changes required are all high-level and deal with introducing distributed groups (dgroup), i.e., logical partitions of the BBs composing the application streaming graph. A dgroup, which in turn is implemented using FastFlow's BBs, can be deployed and executed on a remote machine and communicate with other dgroups according to the original shared-memory FastFlow streaming programming model. We present how to define the distributed groups and how we faced the problem of data serialization and communication performance tuning through transparent messages' batching and their scheduling. Finally, we present a study of the overhead introduced by dgroups considering some benchmarks on a sixteen-node cluster.

关键词： High-level parallel programming Distributed programming parallel patterns Algorithmic skeletons Building blocks

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A gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2020年 106卷 304-319页

作者： Santiago-Duran, Miguel Gonzalez-Compean, J. L. Brinkmann, Andre Reyes-Anastacio, Hugo G. Carretero, Jesus Montella, Raffaele Toscano Pulido, Gregorio Cinvestav Tamaulipas Cd Victoria Tamps Mexico Univ Carlos III Madrid Madrid Spain Parthenope Univ Naples Naples Italy Johannes Gutenberg Univ Mainz Mainz Germany

Software pipelines enable organizations to chain applications for adding value to contents (e.g., confidentially, reliability, and integrity) before either sharing them with partners or sending them to the cloud. However, the pipeline components add overhead when processing large volumes of data, which can become critical in real-world scenarios. This paper presents a gearbox model for processing large volumes of data by using pipeline systems encapsulated into virtual containers. In this model, the gears represent applications, whereas gearboxes represent software pipelines. This model was implemented as a collaborative system that automatically performs Gear up (by using parallel patterns) and/or Gear down (by using in-memory storage) until all gears produce uniform data processing velocities. This model reduces delays and bottlenecks produced by the heterogeneous performance of applications included in software pipelines. The new container tool capsule has been designed to encapsulate both the collaborative system and the software pipelines into a virtual container and deploy it on IT infrastructures. We conducted case studies to evaluate the performance of capsule when processing medical images and PDF repositories. The incorporation of a capsule to a cloud storage service for pre-processing medical imagery was also studied. The experimental evaluation revealed the feasibility of applying the gearbox model to the deployment of software pipelines in real-world scenarios as it can significantly improve the end-user service experience when pre-processing large-scale data in comparison with state-of-the-art solutions such as Sacbe and Parsl. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Software pipelines Continuous delivery In-memory storage parallel patterns Cloud storage Virtual containers

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MINIME: Pattern-Aware Multicore Benchmark Synthesizer

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IEEE TRANSACTIONS ON COMPUTERS 2015年第8期64卷 2239-2252页

作者： Deniz, Etem Sen, Alper Kahne, Brian Holt, Jim Bogazici Univ Dept Comp Engn TR-34342 Istanbul Turkey Freescale Semicond Inc Austin TX USA Intel Corp Design Technol Grp Austin TX USA

We present a novel automated multicore benchmark synthesis framework with characterization and generation components. Our framework uses parallel patterns in capturing important characteristics of multi-threaded applications and generates synthetic multicore benchmarks from those applications. The resulting synthetic benchmarks are small, fast, portable, human-readable, and they accurately reflect microarchitecture dependent and independent characteristics of the original multicore applications. Also, they can use either Pthreads or MCA libraries. We implement our techniques in the MINIME tool and generate synthetic benchmarks from PARSEC, Rodinia, and EEMBC Multibench (TM) benchmarks on x86 and Power Architecture (R) platforms. We show that synthetic benchmarks are representative across a range of multicore machines with different architectures, while being on average 21 x faster and 14 x smaller than original benchmarks.

关键词： Multicore systems parallel patterns synthetic benchmarks

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INTERNATIONAL JOURNAL OF parallel PROGRAMMING 2024年第3期52卷 207-230页

作者： Tonci, Nicolo Rivault, Sebastien Bamha, Mostafa Robert, Sophie Limet, Sebastien Torquati, Massimo Univ Pisa Comp Sci Dept Pisa Italy Univ Orleans Orleans France

Similarity joins are recognized to be among the most used data processing and analysis operations. We introduce a C++-based high-level parallel pattern implemented on top of FastFlow Building Blocks to provide the programmer with ready-to-use similarity join computations. The SimilarityJoin pattern is implemented according to the MapReduce paradigm enriched with locality sensitive hashing (LSH) to optimize the whole computation. The new parallel pattern can be used with any C++ serializable data structure and executed on shared- and distributed-memory machines. We present experimental validations of the proposed solution considering two different clusters and small and large input datasets to evaluate in-core and out-of-core executions. The performance assessment of the SimilarityJoin pattern has been conducted by comparing the execution time against the one obtained from the original hand-tuned Hadoop-based implementation of the LSH-based similarity join algorithms as well as a Spark-based version. The experiments show that the SimilarityJoin pattern: (1) offers a significant performance improvement for small and medium datasets;(2) is competitive also for computations using large input datasets producing out-of-core executions.

关键词： LSH Similarity join High-level parallel programming Distributed programming parallel patterns Big data MapReduce

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parallel visual data restoration on multi-GPGPUs using stencil-reduce pattern

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2015年第4期29卷 461-472页

作者： Aldinucci, Marco Pezzi, Guilherme Peretti Drocco, Maurizio Spampinato, Concetto Torquati, Massimo Univ Turin Dept Comp Sci I-10149 Turin Italy Univ Catania Dept Elect Elect & Comp Engn I-95124 Catania Italy Univ Pisa Dept Comp Sci I-56100 Pisa Italy

In this paper, a highly effective parallel filter for visual data restoration is presented. The filter is designed following a skeletal approach, using a newly proposed stencil-reduce, and has been implemented by way of the FastFlow parallel programming library. As a result of its high-level design, it is possible to run the filter seamlessly on a multicore machine, on multi-GPGPUs, or on both. The design and implementation of the filter are discussed, and an experimental evaluation is presented.

关键词： Impulsive noise Gaussian noise image restoration image filtering GPGPUs parallel patterns skeletons structured parallel programming iterative stencil stencil-reduce MapReduce

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Thread-level synthetic benchmarks for multicore systems

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MICROPROCESSORS AND MICROSYSTEMS 2015年第7期39卷 471-479页

作者： Sen, Alper Deniz, Etem Bogazici Univ Dept Comp Engn Istanbul Turkey

One of the commonly used techniques to speedup early architectural exploration and performance evaluation of new hardware architectures is to use synthetic benchmarks. This paper presents a novel automated thread-level synthetic benchmark generation framework with characterization and generation components. The resulting thread-level synthetic benchmarks are fast, portable, human-readable, and they accurately mimic the micro-architecture dependent and independent characteristics of each thread in original application. We demonstrate that we can generate multi-threaded synthetic benchmarks for real-life PARSEC and Rodinia benchmarks, while being faster (on average 147 x) and smaller (on average 11x) than originals. The obtained results show that synthetic benchmarks not only accurately preserve thread-level micro-architecture dependent and independent characteristics but also parallel programming patterns, which are high-quality solutions to frequently occurring problems in parallel programming. (C) 2015 Elsevier B.V. All rights reserved.

关键词： Multicore systems parallel patterns Synthetic benchmarks

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