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检索条件"任意字段=International Symposium on Parallel and Distributed Computing"

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EMOSS'22: Workshop on Emerging Open Storage Systems and Solutions for Data Intensive computing 22

EMOSS'22: Workshop on Emerging Open Storage Systems and Solu...

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31st international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Narasimhamurthy, Sai Lockwood, Glenn Seagate Syst Marlow Bucks England Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781450391993

Exciting changes are coming to the fore in the world of Storage and I/O for High Performance and Data Intensive computing. Existing data access and data retrieval methods that suitably support the new generation of data intensive applications in the realm of HPC and AI are being re-assessed. These new applications need to support both evolutionary as well as revolutionary approaches to data access and storage. Open source is enabling the adoption of these new techniques. This workshop looks at cutting edge trends in storage systems and solutions for data intensive HPC and AI applications with specific focus primarily on community driven initiatives and commercial products that may inspire the community. The EMOSS'22 workshop proceedings are available at: https://***/***?id=3526061

关键词： HPC Storage Data Intensive computing Cloud computing Exascale I/O

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DroMPI: parallel Computation Over Drop computing

DroMPI: Parallel Computation Over Drop Computing

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Cluster, Cloud and Internet computing Workshops (CCGridW), IEEE/ACM international symposium on

作者： George-Mircea Grosu Silvia-Elena Nistor Radu-Ioan Ciobanu Ciprian Dobre Florin Pop National University of Science and Technology Politehnica Bucharest Romania National Institute for Research and Development in Informatics (ICI) Bucharest Romania Academy of Romanian Scientists Bucharest Romania

ISBN: (数字)9798350377514

ISBN: (纸本)9798350377521

With the advancement of technology and the spread of multi-core systems, the need for parallelization arises and the interest in programming models is growing. At the same time, new distributed computing models have been proposed, being in fierce competition to obtain the highest possible performance. The Drop computing Paradigm proposes the idea of decentralized computing over ad-hoc opportunistic networks of mobile and Edge devices. In this respect, the Drop computing model does not only aim to achieve a minimum turnaround time but also to optimize other characteristics related to mobile devices, such as limited resources and opportunistic communication. Therefore, it is necessary to define a new programming model called DroMPI that intends to extend the capabilities of current parallel and distributed programming models, based on the Drop computing paradigm. Therefore, the solution aims to develop a library that takes advantage of hardware capabilities in the interest of the Drop computing paradigm and also provides programmers with a high-level programming interface. The library’s features will be based on the Message Passing Interface (MPI) standard, which will be responsible for inter-node parallelization. The name of the library, DroMPI, is an acronym for Drop computing and MPI. The implementation of the model will be responsible for the management of communication between nodes and for providing an Application Programming Interface (API) for the development of parallel applications in the Drop computing paradigm.

关键词： Productivity Adaptation models parallel programming Terminology Computational modeling Message passing Libraries Resource management Standards Application programming interfaces

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Informed Prefetching in I/O Bounded distributed Deep Learning

Informed Prefetching in I/O Bounded Distributed Deep Learnin...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Ruan, Xiaojun Chen, Haiquan Calif State Univ East Bay Dept Comp Sci Hayward CA 94542 USA Calif State Univ Sacramento Dept Comp Sci Sacramento CA 95819 USA

ISBN: (纸本)9781665435772

Deep learning research has been growing rapidly in the past decade for the significant performance improvement on GPUs. While the computing capability of current GPUs is tremendous, data pre-processing/loading becomes a potential bottleneck that incurs major training latency and adds overhead in both CPU and memory, especially when datasets are too large to fit in memory. When datasets are stripped on distributed file systems, access to a remote storage node may deteriorate I/O performance significantly due to network I/O latency in cloud. Moreover, some deep learning workloads may be assigned to remote GPU servers in Edge computing which results in even higher network I/O ***, it is desirable to provide efficient parallel and distributed prefetching solution which is able to reduce the I/O cost of data pre-processing before feeding the data into GPUs for training on distributed storage systems of Cloud or Edge. Although the current deep learning frameworks like PyTorch or TensorFlow offer multiprocessing data loading functionalities, their approaches come at the price of high computing resource usage and memory usage. In this paper, we presented a novel thread-level Informed Prefetching Data Loader framework, IPDL, in support of efficient data prefetching from remote storage nodes in distributed deep learning environments and possibly in Edge computing. Compared to its counterparts in PyTorch, IPDL is able to provide accelerated I/O performance for data loading while consuming lower computing resource and memory space at the same time. Extensive experiments on both an individual server and a cluster computing system have shown the superiority of IPDL over the latest implementation of PyTorch.

关键词： Prefetching Deep Learning Neural Network Virtual distributed File System

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Peachy parallel Assignments (EduPar 2023)

Peachy Parallel Assignments (EduPar 2023)

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

作者： Alina Lazar Virginia Niculescu David P. Bunde Department of SCSIET Youngstown State University Youngstown OH USA Department of Computer Science Babeş-Bolyai University Cluj-Napoca Romania Department of Computer Science Knox College Galesburg IL USA

The presentation of Peachy parallel Assignments at parallel and distributed computing education workshops is an effort to promote the reuse of high-quality assignments, both saving precious faculty time and improving the quality of course assignments. These assignments must have been used in class and are selected for being easy to adopt by other instructors and for being “cool and inspirational” so that students spend time on them and talk about them with others. The assignments and their materials are also archived on the Peachy parallel Assignments *** this paper, we present two new assignments. The first has students implement the Mandelbrot set in Python, combining an interesting image with Python’s ease of use. The second assignment is a substantial project to implement a programming contest judge. It requires that students use many parallel and distributed computing concepts, with the added benefit of solving a “real problem” and creating software with which students may have personally interacted.

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NetANNS: A High-Performance distributed Search Framework Based On In-Network computing 19

NetANNS: A High-Performance Distributed Search Framework Bas...

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19th IEEE international symposium on parallel and distributed Processing with Applications (IEEE ISPA)

作者： Zhang, Penghao Pan, Heng Li, Zhenyu Xie, Gaogang Cui, Penglai Chinese Acad Sci Inst Comp Technol Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Purple Mt Labs Nanjing Peoples R China Chinese Acad Sci Comp Network Informat Ctr Beijing Peoples R China

ISBN: (纸本)9781665435741

Approximate nearest neighbor search (ANNS) is the most basic and important algorithm in Database, Machine Learning and other applications. With the expansion of cloud computing, the academia focuses on the study of how to optimize distributed frameworks based on approximate nearest neighbor search such as MapReduce, and Memcached. We implement a new distributed ANNS search framework (NetANNS). The main contributions of NetANNS are to accelerate the data preprocessing with programmable switch, and integrate a variety of efficient ANNS algorithms so that it can choose the most suitable algorithm for each datasets. The experiments show that the search efficiency of NetANNS is about 2x than the common distributed ANNS frameworks which are implemented based on the framework of MapReduce.

关键词： approximate nearest neighbor search distributed search in-network computation

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A new Ad-Hoc parallel file system for HPC environments based on the Expand parallel file system

A new Ad-Hoc parallel file system for HPC environments based...

引用

international symposium on parallel and distributed computing

作者： Felix Garcia-Carballeira Diego Camarmas-Alonso Alejandro Caderon-Mateos Jesus Carretero Computer Science and Engineering Departament Universidad Carlos III de Madrid Leganes Spain

An Ad-Hoc File System dynamically virtualizes storage on compute nodes into a fast storage volume to reduce congestion on parallel file systems used as backends in HPC environments and improve data locality. This paper presents Expand Ad-Hoc, a version of the Expand parallel file system, for use as an Ad-Hoc storage system for HPC environments. Such an update seeks to take better advantage of new storage technologies (on SSDs local to nodes, for example) and to adapt to new demands of current parallel applications (e.g., optimizing data access by analyzing data locality). The paper describes the new system’s features and presents a first evaluation comparing the performance of Expand with another Ad-Hoc File System (GekkoFS), and GPFS. The first results are quite satisfactory and demonstrate the good performance of Expand as an Ad-Hoc storage system.

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parallel Quantum Circuit Extraction from MBQC-Patterns

Parallel Quantum Circuit Extraction from MBQC-Patterns

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

作者： Marcel Quanz Korbinian Staudacher Karl Fürlinger Ludwig-Maximilians-Universität München Munich Germany MNM-Team Ludwig-Maximilians-Universität München Munich Germany

ISBN: (数字)9798350364606

ISBN: (纸本)9798350364613

In the model of measurement-based quantum computing (MBQC), computations are performed via sequential measurements on a highly entangled graph state. MBQC is a natural model for photonic quantum computing and has been shown to be useful for tasks like optimization and verification of general quantum computations. Therefore, it is often necessary to translate between MBQC and the predominantly used quantum circuit model in a fast and reliable way. While there are algorithms with linear complexity to extract quantum circuits from measurement patterns using additional ancilla qubits, efficient ancilla-free extraction has shown to be more costly. We develop strategies to parallelize an existing extraction algorithm based on ZX-calculus by exploiting the graph structure of measurement patterns and evaluate the performance on patterns obtained from a benchmark set of quantum circuits. Our results suggest that possible parallelization speedups are closely related to the graph structure of a pattern.

关键词： distributed processing Quantum entanglement Computational modeling Qubit Quantum circuit Integrated circuit modeling Task analysis

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HPPython: Extending Python with HPspmd for Data parallel Programming 23

HPPython: Extending Python with HPspmd for Data Parallel Pro...

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Proceedings of the 2023 2nd international symposium on computing and Artificial Intelligence

作者： Judah Josiah Nava Jaden Jinu Lee Hanku Lee Computer Science & Information Systems Department Minnesota State University Moorhead USA

ISBN: (纸本)9798400708954

In light of previous endeavors and trends in the realm of parallel programming, HPPython emerges as an essential superset that enhances the accessibility of parallel programming for developers, facilitating scalability across multiple nodes. Despite Python's popularity as a programming language in scientific and engineering applications and its native support for executing various processes, HPPython brings substantial simplification to the development of parallel programs and empowers program distribution across heterogeneous clusters consisting of multiple physical computers. HPPython leverages the MPI standard for its underlying communication, thereby harnessing the benefits of the SPMD model. Additionally, HPPython introduces novel syntax and constructs, such as parallel loops and distributed lists, while endeavoring to retain the natural essence of the original language. This paper delves into the distinct components of HPPython and elucidates their integration, establishing HPPython as a viable solution for parallel programming in today's data-driven world.

关键词： Data parallel Language distributed computing High-Performance computing parallel computing

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Communication-Avoiding and Memory-Constrained Sparse Matrix-Matrix Multiplication at Extreme Scale 35

Communication-Avoiding and Memory-Constrained Sparse Matrix-...

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35th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Hussain, Md Taufique Selvitopi, Oguz Buluc, Aydin Azad, Ariful Indiana Univ Bloomington IN 47405 USA Lawrence Berkeley Natl Lab Berkeley CA USA

ISBN: (纸本)9781665440660

We present a distributed-memory algorithm for sparse matrix-matrix multiplication (SpGEMM) of extremely large matrices where the generated output is larger than the aggregated memory of a target supercomputer. We address this challenge by splitting the computation into batches with each batch generating a set of output columns. We developed a distributed symbolic step to understand the memory requirement and determine the number of batches beforehand. We integrated the multiplication in each batch with an existing communication avoiding techniques to reduce the communication overhead while multiplying matrices in a 3-D process grid. Furthermore, we made the in-node computations faster by designing a sort-free SpGEMM and merging algorithm. Incorporating all the proposed approaches, our SpGEMM scales for large protein-similarity networks using 262,144 cores on a Cray XC40 supercomputer while achieving a 10x speedup using 16x more nodes. Our code is available as part of the Combinatorial BIAS library (https://***/PASSIONLab/CombBLAS).

关键词： Proteins Three-dimensional displays Social networking (online) Scientific computing Memory management Genomics parallel processing

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parallel Program Scaling Analysis using Hardware Counters 21

Parallel Program Scaling Analysis using Hardware Counters

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30th international symposium on High-Performance parallel and distributed computing (HPDC)

作者： Jagga, Shobhit Malakar, Preeti Indian Inst Technol Kanpur Kanpur Uttar Pradesh India

ISBN: (纸本)9781450382175

We present a lightweight library that automatically collects several hardware counters for MPI applications. We analyze the effect of strong and weak scaling on the counters. We first correlate the counter values obtained from each process count, and then cluster the counters to identify counters that are affected similarly due to scaling. We noted that the effect of last-level cache misses is more pronounced for some applications such as miniFE.

关键词： Performance analysis hardware counter code characterization

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