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Message from the HCW Steering Committee Chair

Message from the HCW Steering Committee Chair

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Behrooz Shiraz Washington State University Pullman Washington USA

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

These are the proceedings of the “29th Heterogeneity in Computing Workshop,” also known as HCW 2020. A few years ago, the title of the workshop was changed from the original title of “Heterogeneous Computing Workshop” to reflect the breadth of the impact of heterogeneity, as well as to stress that the focus of the workshop is on the management and exploitation of heterogeneity. All of this is, of course, taken in the context of the parent conference, the international parallel and distributed Processing symposium (IPDPS), and so explores heterogeneity in parallel and distributed computing systems.

关键词： Conferences distributed processing Heterogeneous networks distributed computing Task analysis Program processors Stress

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Message from the HCW General Chair

Message from the HCW General Chair

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： John K. Antonio University of Oklahoma Norman Oklahoma USA

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Welcome to the 29th international Heterogeneity in Computing Workshop (HCW). Heterogeneity is, more than ever, one of the key features of modern computing systems. From HPC infrastructure to embedded systems or cloud environments, heterogeneity is pervasive. Modeling, managing, programming, and controlling such systems raise many key research problems and opportunities for innovation.

关键词： Conferences distributed processing Embedded systems Computational modeling Programming Control systems Technological innovation

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Towards Native Execution of Deep Learning on a Leadership-Class HPC System 33

Towards Native Execution of Deep Learning on a Leadership-Cl...

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33rd IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Yoginath, Srikanth B. Alam, Maksudul Ramanathan, Arvind Bhowmik, Debsindhu Laanait, Nouamane Perumalla, Kalyan S. Oak Ridge Natl Lab Oak Ridge TN 37830 USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781538655559

Large parallel machines generally offer the best parallel performance with "native execution" that is achieved using codes developed with the optimized compilers, communication libraries, and runtimes offered on the machines. In this paper, we report and analyze performance results from native execution of deep learning on a leadership-class high-performance computing (HPC) system. Using our new code called DEEPEX, we present a study of the parallel speed up and convergence rates of learning achieved with native parallel execution. In the trade-off between computational parallelism and synchronized convergence, we first focus on maximizing parallelism while still obtaining convergence. Scaling results are reported from execution on up to 15,000 GPUs using two scientific data sets from atom microscopy and protein folding applications, and also using the popular ImageNet data set. In terms of the traditional measure of parallel speed up, excellent scaling is observed up to 12,000 GPUs. Additionally, accounting for convergence rates of deep learning accuracy or error, a deep learning-specific metric called "learning speed up" is also tracked. The performance results indicate the need to evaluate parallel deep learning execution in terms of learning speed up, and point to additional directions for improved exploitation of high-end HPC systems.

关键词： Deep Learning Massively parallel systems parallel Speedup Learning Speedup

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BigSpa: An Efficient Interprocedural Static Analysis Engine in the Cloud 33

BigSpa: An Efficient Interprocedural Static Analysis Engine ...

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33rd IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Zuo, Zhiqiang Gu, Rong Jiang, Xi Wang, Zhaokang Huang, Yihua Wang, Linzhang Li, Xuandong Nanjing Univ State Key Lab Novel Software Technol Nanjing Peoples R China

ISBN: (纸本)9781728112466

Static program analysis is widely used in various application areas to solve many practical problems. Although researchers have made significant achievements in static analysis, it is still too challenging to perform sophisticated interprocedural analysis on large-scale modern software. The underlying reason is that interprocedural analysis for large-scale modern software is highly computation- and memory-intensive, leading to poor scalability. We aim to tackle the scalability problem by proposing a novel big data solution for sophisticated static analysis. Specifically, we propose a data-parallel algorithm and a join-process-filter computation model for the CFL-reachability based interprocedural analysis and develop an efficient distributed static analysis engine in the cloud, called BigSpa. Our experiments validated that BigSpa running on a cluster scales greatly to perform precise interprocedural analyses on millions of lines of code, and runs an order of magnitude or more faster than the existing state-of-the-art analysis tools.

关键词： Interprocedural static analysis distributed systems data-parallel computation

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A System for High Performance Mining on GDELT Data

A System for High Performance Mining on GDELT Data

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Konstantin Pogorelov Daniel Thilo Schroeder Petra Filkukova Johannes Langguth Simula Research Laboratory Fornebu Norway Simula Metropolitan Center for Digital Engineering Oslo Norway

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

We design a system for efficient in-memory analysis of data from the GDELT database of news events. The specialization of the system allows us to avoid the inefficiencies of existing alternatives, and make full use of modern parallel high-performance computing hardware. We then present a series of experiments showcasing the system's ability to analyze correlations in the entire GDELT 2.0 database containing more than a billion news items. The results reveal large scale trends in the world of today's online news.

关键词： Data mining Publishing Social network services Media Monitoring distributed databases

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Methods and Experiences for Developing Abstractions for Data-intensive, Scientific Applications

Methods and Experiences for Developing Abstractions for Data...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Andre Luckow Shantenu Jha Ludwig-Maximilian University Munich Germany RADICAL ECE Rutgers University Piscataway NJ USA

ISBN: (数字)9781728174457

ISBN: (纸本)9781728174570

Developing software for scientific applications that require the integration of diverse types of computing, instruments, and data present challenges that are distinct from commercial software. These applications require scale, and the need to integrate various programming and computational models with evolving and heterogeneous infrastructure. Pervasive and effective abstractions for distributed infrastructures are thus critical; however, the process of developing abstractions for scientific applications and infrastructures is not well understood. While theory-based approaches for system development are suited for well-defined, closed environments, they have severe limitations for designing abstractions for scientific systems and applications. The design science research (DSR) method provides the basis for designing practical systems that can handle real-world complexities at all levels. In contrast to theory-centric approaches, DSR emphasizes both practical relevance and knowledge creation by building and rigorously evaluating all artifacts. We show how DSR provides a well-defined framework for developing abstractions and middleware systems for distributed systems. Specifically, we address the critical problem of distributed resource management on heterogeneous infrastructure over a dynamic range of scales, a challenge that currently limits many scientific applications. We use the pilot-abstraction, a widely used resource management abstraction for high-performance, high throughput, big data, and streaming applications, as a case study for evaluating the DSR activities. For this purpose, we analyze the research process and artifacts produced during the design and evaluation of the pilot-abstraction. We find DSR provides a concise framework for iteratively designing and evaluating systems. Finally, we capture our experiences and formulate different lessons learned.

关键词： Computational modeling Resource management Computer architecture Benchmark testing Software distributed databases Complexity theory

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A new Julia-based parallel time-domain simulation algorithm for analysis of power system dynamics 20

A new Julia-based parallel time-domain simulation algorithm ...

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Proceedings of the IEEE/ACM 24th international symposium on distributed Simulation and Real Time Applications

作者： Michael Kyesswa Philipp Schmurr Hüseyin K. Çakmak Uwe Kühnapfel Veit Hagenmeyer Karlsruhe Institute of Technology Karlsruhe Germany

The present paper describes a new parallel time-domain simulation algorithm using a high performance computing environment - Julia - for the analysis of power system dynamics in large networks. The parallel algorithm adapts a parallel-in-space decomposition scheme to a previously sequential algorithm in order to develop a new parallelizable numerical solution of the power system equations. The parallel-in-space decomposition is based on the block bordered diagonal form, which reformulates the network admittance matrix into sub-blocks that can be solved in parallel. For the optimal spatial decomposition of the network, a new extended graph partitioning strategy is developed for load balancing and minimizing the communication between subnetworks. The new parallel simulation algorithm is tested using standard test networks of varying complexity. The simulation results are compared to those obtained from a sequential implementation in order to validate the solution accuracy and to determine the performance improvement in terms of computational speedup. Test simulations are conducted using the ForHLR II supercomputing cluster and show a huge potential in computational speedup with increasing network complexity.

关键词： time-domain simulation transient stability analysis parallel computing power systems graph partitioning

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Portal: A High-Performance Language and Compiler for parallel N-body Problems 33

Portal: A High-Performance Language and Compiler for Paralle...

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33rd IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Beni, Laleh Aghababaie Ramanan, Saikiran Chandramowlishwaran, Aparna Univ Calif Irvine Irvine CA 92717 USA

ISBN: (纸本)9781728112466

There is a big gap between the algorithm one designs on paper and the code that runs efficiently on a parallel system. Our goal is to combine the body of work in compilers, performance optimization, and the domain of N-body problems to build a system where domain scientists can write programs at a high level while attaining performance of code written by experts at the low level. This paper presents Portal, a domain-specific language and compiler designed to enable high-performance implementations of N-body problems on modern multicore systems. Our goal in the development of Portal is three-fold, (a) to implement scalable, fast algorithms that have O (n log n) and O (n) complexity, (b) to design an intuitive language to enable rapid implementations of a variety of problems, and (c) to enable parallel large-scale problems to run on multicore systems. We target N-body problems in various domains from machine learning to scientific computing that can be expressed in Portal to obtain an out-of-the-box optimized parallel implementation. Experimental results on 6 N-body problems show that Portal is within a factor of 5% on average of expert hand-optimized C++ code on a dual-socket AMD EPYC processor. To our knowledge, there are no known libraries or frameworks that implement parallel asymptotically optimal algorithms for the class of generalized N-body problems and Portal aims to fill this gap. Moreover, the Portal language and intermediate algorithm representation are portable and easily extensible to different platforms.

关键词： Domain-specific language LLVM N-body problems tree-based algorithms

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ArrOW: Experiencing a parallel Cloud-based De Novo Assembler Workflow 33

ArrOW: Experiencing a Parallel Cloud-based De Novo Assembler...

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33rd IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Ocana, Kary Guedes, Thaylon de Oliveira, Daniel Natl Lab Sci Comp LNCC Petropolis RJ Brazil Fluminense Fed Univ UFF Inst Comp Niteroi RJ Brazil

ISBN: (纸本)9781538655559

Advances in next generation sequencing technologies has resulted in the generation of unprecedented volume of sequence data. DNA segments are combined into a reconstruction of the original genome using computer software called genome assemblers. Therefore, assembly now presents new challenges in terms of data management, query, and analysis due the huge number of read sequences and computing intensive CPU-memory algorithms. This restriction reduces the chances to uniformly cover space for exploring statistics, k-mer, software or eukaryotic genomes assembly. To address these issues, we present ArrOW, a cloud-based de novo Assembly clOud Workflow that explores the potential of provenance analytics and parallel computation provided by scientific workflow management systems as SciCumulus. We evaluate the overall performance of ArrOW using up to 256 cores in the Amazon AWS cloud. ArrOW reaches improvements up to 88.3% executing 1,000 reads of genomics datasets. We also highlight how data provenance analytics improved the efficiency for recovering assembling features of genomes.

关键词： component workflow cloud assembly

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Large scale querying and processing for property graphs PhD symposium 22

Large scale querying and processing for property graphs PhD ...

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22nd international Workshop on Design, Optimization, Languages and Analytical Processing of Big Data, DOLAP 2020

作者： Ragab, Mohamed Data Systems Group University of Tartu Tartu Estonia

Recently, large scale graph data management, querying and processing have experienced a renaissance in several timely application domains (e.g., social networks, bibliographical networks and knowledge graphs). However, these applications still introduce new challenges with large-scale graph processing. Therefore, recently, we have witnessed a remarkable growth in the prevalence of work on graph processing in both academia and industry. Querying and processing large graphs is an interesting and challenging task. Recently, several centralized/distributed large-scale graph processing frameworks have been developed. However, they mainly focus on batch graph analytics. On the other hand, the state-of-the-art graph databases can't sustain for distributed efficient querying for large graphs with complex queries. In particular, online large scale graph querying engines are still limited. In this paper, we present a research plan shipped with the state-of-the-art techniques for large-scale property graph querying and processing. We present our goals and initial results for querying and processing large property graphs based on the emerging and promising Apache Spark framework, a defacto standard platform for big data processing. In principle, the design of this research plan is revolving around two main goals. The first goal focuses on designing an adequate and efficient graph-based storage backend that can be integrated with the Apache Spark framework. The second goal focuses on developing various Graph-aware optimization techniques (e.g., graph indexing, graph materialized views), and extending the default relational Spark Catalyst optimizer with Graph-aware cost-based optimizations. Achieving these contributions can significantly enhance the performance of executing graph queries on top of Apache Spark. © Copyright 2020 for this paper held by its author(s).

关键词： Graph databases

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