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parallel patterns for Window-Based Stateful Operators on Data Streams: An Algorithmic Skeleton Approach

INTERNATIONAL JOURNAL OF parallel PROGRAMMING 2017年第2期45卷 382-401页

作者： De Matteis, Tiziano Mencagli, Gabriele Univ Pisa Dept Comp Sci Largo B Pontecorvo 3 I-56127 Pisa Italy

The topic of Data Stream Processing is a recent and highly active research area dealing with the in-memory, tuple-by-tuple analysis of streaming data. Continuous queries typically consume huge volumes of data received at a great velocity. Solutions that persistently store all the input tuples and then perform off-line computation are impractical. Rather, queries must be executed continuously as data cross the streams. The goal of this paper is to present parallel patterns for window-based stateful operators, which are the most representative class of stateful data stream operators. parallel patterns are presented "A la" Algorithmic Skeleton, by explaining the rationale of each pattern, the preconditions to safely apply it, and the outcome in terms of throughput, latency and memory consumption. The patterns have been implemented in the framework targeting off-the-shelf multicores. To the best of our knowledge this is the first time that a similar effort to merge the Data Stream Processing domain and the field of Structured parallelism has been made.

关键词： parallel patterns Algorithmic skeletons Data stream processing Multi-/many-core architectures

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parallel patterns for heterogeneous CPU/GPU architectures: Structured parallelism from cluster to cloud

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FUTURE GENERATION COMPUTER SYSTEMS-THE INTERNATIONAL JOURNAL OF ESCIENCE 2014年 37卷 354-366页

作者： Campa, Sonia Danelutto, Marco Goli, Mehdi Gonzalez-Velez, Horacio Popescu, Alina Madalina Torquati, Massimo Univ Pisa Dept Comp Sci I-56100 Pisa Italy Robert Gordon Univ IDEAS Res Inst Aberdeen AB9 1FR Scotland Natl Coll Ireland Cloud Competency Ctr Dublin Ireland

The widespread adoption of traditional heterogeneous systems has substantially improved the computing power available and, in the meantime, raised optimisation issues related to the processing of task streams across both CPU and GPU cores in heterogeneous systems. Similar to the heterogeneous improvement gained in traditional systems, cloud computing has started to add heterogeneity support, typically through CPU instances, to the conventional CPU-based cloud resources. This optimisation of cloud resources will arguably have a real impact when running on-demand computationally-intensive applications. In this work, we investigate the scaling of pattern-based parallel applications from physical, "local" mixed CPU/GPU-clusters to a public cloud CPU/GPU infrastructure. Specifically, such parallel patterns are deployed via algorithmic skeletons to exploit a peculiar parallel behaviour while hiding implementation details. We propose a systematic methodology to exploit approximated analytical performance/cost models, and an integrated programming framework that is suitable for targeting both local and remote resources to support the offloading of computations from structured parallel applications to heterogeneous cloud resources, such that performance values not available on local resources may be actually achieved with the remote resources. The amount of remote resources necessary to achieve a given performance target is calculated through the performance models in order to allow any user to hire the amount of cloud resources needed to achieve a given target performance value. Thus, it is therefore expected that such models can be used to devise the optimal proportion of computations to be allocated on different remote nodes for Big Data computations. We present different experiments run with a proof-of-concept implementation based on FastFlow on small departmental clusters as well as on a public cloud infrastructure with CPU and GPU using the Amazon Elastic Compute Cloud

关键词： parallel patterns Algorithmic skeletons Structured parallelism Heterogeneous architectures GPU Performance models Cluster computing Cloud computing

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Finding parallel patterns through static analysis in C plus plus applications

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2018年第6期32卷 779-788页

作者： del Rio Astorga, David Dolz, Manuel F. Miguel Sanchez, Luis Daniel Garcia, J. Danelutto, Marco Torquati, Massimo Univ Carlos III Madrid Dept Comp Sci Leganes 28911 Spain Univ Pisa Dept Comp Sci Pisa Italy

Since the 'free lunch' of processor performance is over, parallelism has become the new trend in hardware and architecture design. However, parallel resources deployed in data centers are underused in many cases, given that sequential programming is still deeply rooted in current software development. To address this problem, new methodologies and techniques for parallel programming have been progressively developed. For instance, parallel frameworks, offering programming patterns, allow expressing concurrency in applications to better exploit parallel hardware. Nevertheless, a large portion of production software, from a broad range of scientific and industrial areas, is still developed sequentially. Considering that these software modules contain thousands, or even millions, of lines of code, an extremely large amount of effort is needed to identify parallel regions. To pave the way in this area, this paper presents parallel Pattern Analyzer Tool, a software component that aids the discovery and annotation of parallel patterns in source codes. This tool simplifies the transformation of sequential source code to parallel. Specifically, we provide support for identifying Map, Farm, and Pipeline parallel patterns and evaluate the quality of the detection for a set of different C++ applications.

关键词： parallel patterns C++11 attributes source code analysis tools

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Exploring stream parallel patterns in distributed MPI environments

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parallel COMPUTING 2019年 84卷 24-36页

作者： Lopez-Gomez, Javier Fernandez Munoz, Javier del Rio Astorga, David Dolz, Manuel F. Daniel Garcia, J. Univ Carlos III Madrid Avda Univ 30 Leganes 28911 Spain Univ Jaume 1 Avda Sos Baynat S-N Castellon de La Plana 12071 Spain

In recent years, the large volumes of stream data and the near real-time requirements of data streaming applications have exacerbated the need for new scalable algorithms and programming interfaces for distributed and shared-memory platforms. To contribute in this direction, this paper presents a new distributed MPI back end for GRPPI, a C++ high-level generic interface of data-intensive and stream processing parallel patterns. This back end, as a new execution policy, supports distributed and hybrid (distributed+shared-memory) parallel executions of the Pipeline and Farm patterns, where the hybrid mode combines the MPI policy with a GRPPI shared-memory one. These patterns internally leverage distributed queues, which can be configured to use two-sided or one-sided MPI primitives to communicate items among nodes. A detailed analysis of the GOP! MPI execution policy reports considerable benefits from the programmability, flexibility and readability points of view. The experimental evaluation of two different streaming applications with different distributed and shared-memory scenarios reports considerable performance gains with respect to the sequential versions at the expense of negligible GRPPI overheads. (C) 2019 Elsevier B.V. All rights reserved.

关键词： parallel patterns Stream processing Distributed patterns C plus plus programming Generic programming

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Using Machine Learning Techniques to Detect parallel patterns of Multi-threaded Applications

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INTERNATIONAL JOURNAL OF parallel PROGRAMMING 2016年第4期44卷 867-900页

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

Multicore hardware and software are becoming increasingly more complex. The programmability problem of multicore software has led to the use of parallel patterns. parallel patterns reduce the effort and time required to develop multicore software by effectively capturing its thread communication and data sharing characteristics. Hence, detecting the parallel pattern used in a multi-threaded application is crucial for performance improvements and enables many architectural optimizations;however, this topic has not been widely studied. We apply machine learning techniques in a novel approach to automatically detect parallel patterns and compare these techniques in terms of accuracy and speed. We experimentally validate the detection ability of our techniques on benchmarks including PARSEC and Rodinia. Our experiments show that the k-nearest neighbor, decision trees, and naive Bayes classifier are the most accurate techniques. Overall, decision trees are the fastest technique with the lowest characterization overhead producing the best combination of detection results. We also show the usefulness of the proposed techniques on synthetic benchmark generation.

关键词： parallel patterns parallel programming Multi-threaded applications Multicore software Pattern detection

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On dynamic memory allocation in sliding-window parallel patterns for streaming analytics

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JOURNAL OF SUPERCOMPUTING 2019年第8期75卷 4114-4131页

作者： Torquati, M. Mencagli, G. Drocco, M. Aldinucci, M. De Matteis, T. Danelutto, M. Univ Pisa Dept Comp Sci Pisa Italy Univ Turin Dept Comp Sci Turin Italy

This work studies the issues related to dynamic memory management in Data Stream Processing, an emerging paradigm enabling the real-time processing of live data streams. In this paper, we consider two streaming parallel patterns and we discuss different implementation variants related to how dynamic memory is managed. The results show that the standard mechanisms provided by modern C++ are not entirely adequate for maximizing the performance. Instead, the combined use of an efficient general purpose memory allocator, a custom allocator optimized for the pattern considered and a custom variant of the C++ shared pointer mechanism, provides a performance improvement up to 16% on the best case.

关键词： Data Stream Processing Modern C plus Dynamic memory allocation Multicores Stream analytics parallel patterns

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Improving the Performance of Actors on Multi-cores with parallel patterns

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INTERNATIONAL JOURNAL OF parallel PROGRAMMING 2020年第4期48卷 692-712页

作者： Rinaldi, Luca Torquati, Massimo De Sensi, Daniele Mencagli, Gabriele Danelutto, Marco Univ Pisa Comp Sci Dept Pisa Italy

The Actor-based programming model is largely used in the context of distributed systems for its message-passing semantics and neat separation between the concurrency model and the underlying hardware platform. However, in the context of a single multi-core node where the performance metric is the primary optimization objective, the "pure" Actor Model is generally not used because Actors cannot exploit the physical shared-memory, thus reducing the optimization options. In this work, we propose to enrich the Actor Model with some well-known parallel patterns to face the performance issues of using the "pure" Actor Model on a single multi-core platform. In the experimental study, conducted on two different multi-core systems by using the C++ Actor Framework, we considered a subset of the Parsec benchmarks and two Savina benchmarks. The analysis of results demonstrates that the Actor Model enriched with suitable parallel patterns implementations provides a robust abstraction layer capable of delivering performance results comparable with those of thread-based libraries (i.e. Pthreads and FastFlow) while offering a safer and versatile programming environment.

关键词： Actors parallel patterns Programming model Multi-cores

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Bringing parallel patterns Out of the Corner: The P³ARSEC Benchmark Suite

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ACM TRANSACTIONS ON ARCHITECTURE AND CODE OPTIMIZATION 2017年第4期14卷 1–26页

作者： De Sensi, Daniele De Matteis, Tiziano Torquati, Massimo Mencagli, Gabriele Danelutto, Marco Univ Pisa Pisa Italy

High-level parallel programming is an active research topic aimed at promoting parallel programming methodologies that provide the programmer with high-level abstractions to develop complex parallel software with reduced time to solution. Pattern-based parallel programming is based on a set of composable and customizable parallel patterns used as basic building blocks in parallel applications. In recent years, a considerable effort has been made in empowering this programming model with features able to overcome shortcomings of early approaches concerning flexibility and performance. In this article, we demonstrate that the approach is flexible and efficient enough by applying it on 12 out of 13 PARSEC applications. Our analysis, conducted on three different multicore architectures, demonstrates that pattern-based parallel programming has reached a good level of maturity, providing comparable results in terms of performance with respect to both other parallel programming methodologies based on pragma-based annotations (i.e., OpenMP and OmpSs) and native implementations (i.e., Pthreads). Regarding the programming effort, we also demonstrate a considerable reduction in lines of code and code churn compared to Pthreads and comparable results with respect to other existing implementations.

关键词： parallel patterns algorithmic skeletons parsec multicore programming benchmarking

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Plasticine: A Reconfigurable Accelerator for parallel patterns

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IEEE MICRO 2018年第3期38卷 20-31页

作者： Prabhakar, Raghu Zhang, Yaqi Koeplinger, David Feldman, Matt Zhao, Tian Hadjis, Stefan Pedram, Ardavan Kozyrakis, Christos Olukotun, Kunle Stanford Univ Dept Comp Sci Stanford CA 94305 USA Stanford Univ Dept Elect Engn Stanford CA 94305 USA Stanford Univ Stanford CA 94305 USA Stanford Univ Elect Engn & Comp Sci Stanford CA 94305 USA

Plasticine is a new spatially reconfigurable architecture designed to efficiently execute applications composed of high-level parallel patterns. With an area footprint of 113 mm(2) in a 28-nm process and a 1-GHz clock, Plasticine has a peak floating-point performance of 12.3 single-precision Tflops and a total on-chip memory capacity of 16 MB, consuming a maximum power of 49 W. Plasticine provides an improvement of up to 76.9X in performance-per-watt over a conventional FPGA over a wide range of dense and sparse applications.

关键词： Field Programmable Gate Arrays Floating Point Arithmetic Logic Design Microprocessor Chips Reconfigurable Architectures Reconfigurable Accelerator Spatially Reconfigurable Architecture Peak Floating Point Performance Single Precision Tflops Total On Chip Memory Capacity Size 113 0 Mm Memory Size 16 0 M Byte Power 49 0 W Phasor Measurement Units Random Access Memory Computer Architecture Reconfigurable Architecture System On Chip Field Programmable Gate Arrays Hardware Accelerators Dataflow Architectures FPGA Coarse Grained Reconfigurable Architectures parallel patterns Reconfigurable Architectures Hardware

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A Comparative Analysis of the Performance of Scalable parallel patterns Applied to Genetic Algorithms and Configured for NVIDIA GPUs

A Comparative Analysis of the Performance of Scalable Parall...

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Complex Adaptive Systems Conference on Engineering Cyber Physical Systems (CAS)

作者： Radford, David Calvert, David Univ Guelph Sch Comp Sci Guelph ON N1G 2W1 Canada

parallel programming patterns are built upon a foundation of serial programming patterns to maximize the efficiency of parallel code and effectively use parallel resources available in a given system. This work focuses on using NVIDIA GPUs with the CUDA C library for parallel computing. The goal is to implement parallel versions of a genetic algorithm using the Map and Fork-Join parallel patterns to improve its performance. The intent is to demonstrate that the parallel patterns can be implemented on the CUDA platform and achieve increases in speedup, efficiency, and scalability with the parallel genetic algorithms. A comparative assessment of the two parallel patterns is conducted by configuring them to evaluate instances of the Travelling Salesman Problem (TSP) using different data sets. This assessment considers each algorithm's run time performance, their use of system resources, and their required overhead. (c) 2017 The Authors. Published by Elsevier B.V.

关键词： parallel patterns Genetic Algorithm parallel Computing Travelling Salesman Problem

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