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检索条件"主题词=Parallel File system"

共 120 条记录，以下是91-100 订阅

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A NEW FRAMEWORK OF CLUSTER-BASED parallel PROCESSING system FOR HIGH-PERFORMANCE GEO-COMPUTING

A NEW FRAMEWORK OF CLUSTER-BASED PARALLEL PROCESSING SYSTEM ...

引用

IEEE International Geoscience and Remote Sensing Symposium

作者： Ma, Yan Liu, Dingsheng Li, Jingshan Chinese Acad Sci Ctr Earth Observat & Digital Earth Beijing 100086 Peoples R China

ISBN: (纸本)9781424433940

Up to now, It still remains a big challenge for us to build a high performance geo-computing system with high processing speed and also be easy of use by domain researchers. The unprecedented scale data and various complex algorithms pose many computational and management challenges To properly settle these main issues above, a new system framework for high performance gco-computing is presented in this paper A High Performance Geo-data Object Storage system (HPGOSS) base on parallel file system is used for eliminating I/O performance bottleneck and deal with the data managing problem result from the close relevancy between geo-information and remote sensing image data. parallel programming models for fast parallelization of geo-computing algorithms are proposed. In addition, the job scheduling strategy and workflow engine are also discussed. Finally, such system could provide a parallel geo-computing environment with high performance, easy to use, optimal resource utilization, and high scalability.

关键词： high-performance computing geo-computing remote sensing image processing system framework parallel file system parallel programming model

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Characterizing Machine Learning I/O Workloads on Leadership Scale HPC systems 29

Characterizing Machine Learning I/O Workloads on Leadership ...

引用

29th International Symposium on the Modeling, Analysis, and Simulation of Computer and Telecommunication systems (MASCOTS)

作者： Paul, Arnab K. Karimi, Ahmad Maroof Wang, Feiyi Oak Ridge Natl Lab POB 2009 Oak Ridge TN 37831 USA

ISBN: (纸本)9781665458382

High performance computing (HPC) is no longer solely limited to traditional workloads such as simulation and modeling. With the increase in the popularity of machine learning (ML) and deep learning (DL) technologies, we are observing that an increasing number of HPC users are incorporating ML methods into their workflow and scientific discovery processes, across a wide spectrum of science domains such as biology, earth science, and physics. This gives rise to a diverse set of I/O patterns than the traditional checkpoint/restart-based HPC I/O behavior. The details of the I/O characteristics of such ML I/O workloads have not been studied extensively for large-scale leadership HPC systems. This paper aims to fill that gap by providing an in-depth analysis to gain an understanding of the I/O behavior of ML I/O workloads using darshan - an I/O characterization tool designed for lightweight tracing and profiling. We study the darshan logs of more than 23,000 HPC ML I/O jobs over a time period of one year running on Summit - the second-fastest supercomputer in the world. This paper provides a systematic I/O characterization of ML I/O jobs running on a leadership scale supercomputer to understand how the I/O behavior differs across science domains and the scale of workloads, and analyze the usage of parallel file system and burst buffer by ML I/O workloads.

关键词： Burst Buffer Darshan High Performance Computing HPC Storage IBM Spectrum Scale I/O Characterization Machine Learning parallel file system

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Cost-aware Client-side file Caching for Data-intensive Applications

Cost-aware Client-side File Caching for Data-intensive Appli...

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5th IEEE International Conference on Cloud Computing Technology and Science (IEEE CloudCom)

作者： Huang, Yaning Jin, Hai Shi, Xuanhua Wu, Song Chen, Yong Huazhong Univ Sci & Technol Sch Comp Sci & Technol Cluster & Grid Comp Lab Serv Comp Technol & Syst Lab Wuhan 430074 Peoples R China Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

parallel and distributed file systems are widely used to provide high throughput in high-performance computing and Cloud computing systems. To increase the parallelism, I/O requests are partitioned into multiple sub-requests (or 'flows') and distributed across different data nodes. The performance of file systems is extremely poor if data nodes have highly unbalanced response time. Client-side caching offers a promising direction for addressing this issue. However, current work has primarily used client-side memory as a read cache and employed a write-through policy which requires synchronous update for every write and significantly under-utilizes the client-side cache when the applications are write-intensive. Realizing that the cost of an I/O request depends on the struggler sub-requests, we propose a cost-aware client-side file caching (CCFC) strategy, that is designed to cache the sub-requests with high I/O cost on the client end. This caching policy enables a new trade-off across write performance, consistency guarantee and cache size dimensions. Using benchmark workloads MADbench2, we evaluate our new cache policy alongside conventional write-through. We find that the proposed CCFC strategy can achieve up to 110% throughput improvement compared to the conventional write-through policies with the same cache size on an 85-node cluster.

关键词： parallel file system client-side file caching data-intensive computing high-performance computing Cloud computing

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Dynamically Adaptable I/O Semantics for High Performance Computing 1

引用

30th International Supercomputing Conference on High Performance Computing (ISC High Performance)

作者： Kuhn, Michael Univ Hamburg Hamburg Germany

ISBN: (数字)9783319201191

ISBN: (纸本)9783319201191;9783319201184

While an input/output (I/O) interface's syntax describes the available operations, its semantics determines how these operations behave and which assumptions developers can make about them. There are several different interface standards in existence, some of them dating back decades and having been designed for local file systems;one such representative is POSIX. Many parallel distributed file systems implement a POSIX-compliant interface to improve portability. All currently available interfaces follow a fixed approach regarding semantics, making them only suitable for a subset of use cases and workloads. While the interfaces do not allow application developers to influence the I/O semantics, applications could benefit greatly from the possibility of being able to adapt them to their requirements. The work presented in this paper includes the design of a novel I/O interface and a file system called JULEA. They offer support for dynamically adaptable semantics and are suited specifically for HPC applications. The introduced concept allows applications to adapt the file system behavior to their exact I/O requirements instead of the other way around. The general goal is an interface that allows developers to specify what operations should do and how they should behave - leaving the actual realization and possible optimizations to the underlying file system. JULEA has been evaluated using both synthetic benchmarks and real-world applications. Overall, JULEA provides data and metadata performance comparable to that of other established parallel distributed file systems. However, in contrast to the existing solutions, its flexible semantics allows it to cover a wider range of use cases in an efficient way. The results demonstrate that there is need for I/O interfaces that can adapt to the requirements of applications. Even though POSIX facilitates portability, it does not seem to be suited for contemporary HPC demands.

关键词： I/O semantics I/O interface parallel file system

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Scheduling Distributed I/O Resources in HPC systems 30th

Scheduling Distributed I/O Resources in HPC Systems

引用

30th European Conference on parallel and Distributed Processing (Euro-Par)

作者： Bandet, Alexis Boito, Francieli Pallez, Guillaume Univ Bordeaux CNRS Bordeaux INP InriaLaBRIUMR 5800 F-33400 Talence France INRIA Rennes France

ISBN: (纸本)9783031695766;9783031695773

This paper presents a comprehensive investigation on optimizing I/O performance in the access to distributed I/O resources in high-performance computing (HPC) environments. I/O resources, such as the I/O forwarding nodes and object storage targets (OST), are shared by applications. Each application has access to a subset of them, and multiple applications can access the same resources. We propose heuristics to schedule these distributed I/O resources in two steps: for each application, determining how many (allocation) and which (placement) resources to use. We discuss a wide range of information about applications' characteristics that can be used by the scheduling algorithms. Despite the fact that a higher level of application knowledge is associated with better performance, we demonstrate the robustness of our solutions in scenarios where information is limited or inaccurate. This research provides insights into the trade-offs between the depth of application characterization and the practicality of scheduling I/O resources.

关键词： HPC parallel I/O parallel file system object storage targets I/O forwarding scheduling resource allocation

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Optimizing a hybrid SSD/HDD HPC storage system based on file size distributions

Optimizing a hybrid SSD/HDD HPC storage system based on file...

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29th IEEE Symposium on Massive Storage systems and Technologies (MSST) - Research Track

作者： Welch, Brent Noer, Geoffrey Panasas Inc Sunnyvale CA 94085 USA

ISBN: (纸本)9781479902170;9781479902187

We studied file size distributions from 65 customer installations and a total of nearly 600 million files. We found that between 25% and 90% of all files are 64 Kbytes or less in size, yet these files account for less than 3% of the capacity in most cases. In extreme cases 5% to 15% of capacity is occupied by small files. We used this information to size the ratio of SSD to HDD capacity on our latest HPC storage system. Our goal is to automatically allocate all of the block-level and file-level metadata, and all of the small files onto SSD, and use the much cheaper HDD storage for large file extents. The unique storage blade architecture of the Panasas system that couples SSD, HDD, processor, memory, and networking into a scalable building block makes this approach very effective. Response time measured by metadata intensive benchmarks is several times better in our systems that couple SSD and HDD. The paper describes the measurement methodology, the results from our customer survey, and the performance benefits of our approach.

关键词： file systems OSD SSD parallel file system HPC

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A2FL: Autonomous and Adaptive file Layout in HPC through Real-time Access Pattern Analysis 38

A2FL: Autonomous and Adaptive File Layout in HPC through Rea...

引用

International parallel and Distributed Processing Symposium (IPDPS)

作者： Sung, Dong Kyu Son, Yongseok Sim, Alex Wu, Kesheng Byna, Suren Tang, Houjun Eom, Hyeonsang Kim, Changjong Kim, Sunggon Seoul Natl Univ Seoul South Korea Chung Ang Univ Seoul South Korea Lawrence Berkeley Natl Lab Berkeley CA USA Ohio State Univ Columbus OH USA Seoul Natl Univ Sci & Technol Seoul South Korea

ISBN: (纸本)9798350387117;9798350387124

Various scientific applications with different I/O characteristics are executed in HPC systems. However, underlying parallel file systems are unaware of these characteristics of applications, and using a single fixed file layout for all applications can degrade the performance of HPC systems. In this paper, we propose A2FL, an autonomous and adaptive file layout adjustment scheme that optimizes parallel file system configurations by analyzing the access pattern of the applications. The key steps of A2FL are as follows: (1) A2FL initially intercepts the I/O operations of the application, recording their access patterns in real-time. (2) The access patterns are then transformed into a graphical representation used for predicting I/O performance and providing adjustment recommendations. (3) A2FL autonomously adjusts the file layout based on the prediction results, delivering an optimal file layout within the parallel file system. Moreover, we propose A2FL-Compound which analyzes an access pattern by dividing it into smaller components to optimize the file layout in a fine-grained manner. Our evaluations demonstrate that A2FL significantly enhances I/O performance, with improvements of up to 65.9x compared to the default file layout.

关键词： HPC I/O Access Pattern Machine Learning Storage parallel file system

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ALDM: Algorithm of Adaptive Loading Data Migration in Distributed file system

ALDM: Algorithm of Adaptive Loading Data Migration in Distri...

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Digest Asia-Pacific Magnetic Recording Conference (APMRC)

作者： Tan Zhipeng Zhou Wei Zhang Wenhua Feng Dan Huazhong Univ Sci & Technol Sch Comp Sci Wuhan Natl Lab Optoelect Wuhan Peoples R China

ISBN: (纸本)9781467347341

This paper analyzes the problems of load balance caused by data access in parallel file system, and then proposes an Adaptive Loading Data Migration (ALDM) strategy to balance the load of data server. Experiment result shows that ALDM has little impact on system performance during data migration process, and the performance improves about 10 percent after data migration completed. What's more, ALDM also has good adaptability to variety loads in different storage systems.

关键词： Data Migration Load detection Load balance parallel file system

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A Cached Middleware to Improve I/O Performance using POSIX Interface

A Cached Middleware to Improve I/O Performance using POSIX I...

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International Conference on Computational Modelling and Security (CMS)

作者： Ambareesh, S. Fathima, Jesna Vemana Inst Technol Dept Comp Sci & Engn Bengaluru Karnataka India

The performance of computing devices is increasing day by day, the storage devices are not capable to reach with this benchmark. The compute and storage resources are forming two parts of the shared network. This paper is presenting a storage middleware called HyCache+ to reduce the high bi-section bandwidth of the parallel computing systems. The middleware uses Programmable Operating system (POSIX) interface to the end users to swap the data with the high capacity network attached storage. The system uses a 2-layer scheduling approach. The HyCache+ can be deployed on the IBM Blue Gene / P supercomputers for the performance comparison. (C) 2016 Published by Elsevier B.V.

关键词： Middleware parallel file system POSIX

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Improving Large-scale Storage system Performance via Topology-aware and Balanced Data Placement 20

Improving Large-scale Storage System Performance via Topolog...

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20th IEEE International Conference on parallel and Distributed systems (ICPADS)

作者： Wang, Feiyi Oral, Sarp Gupta, Saurabh Tiwari, Devesh Vazhkudai, Sudharshan S. Oak Ridge Natl Lab Natl Ctr Computat Sci Oak Ridge TN 37830 USA

ISBN: (纸本)9781479976157

With the advent of big data, the I/O subsystems of large-scale compute clusters are becoming a center of focus. More applications are putting greater demands on end-to-end I/O performance. These subsystems are often complex in design. They comprise of multiple hardware and software layers to cope with the increasing capacity, capability, and scalability requirements of data intensive applications. However, the sharing nature of storage resources and the intrinsic interactions across these layers make it a great challenge to realize end-to-end performance gains. This paper proposes a topology-aware strategy to balance the load across resources, to improve the per-application I/O performance. We demonstrate the effectiveness of our algorithm on an extreme-scale compute cluster, Titan, at the Oak Ridge Leadership Computing Facility (OLCF). Our experiments with both synthetic benchmarks and a real-world application show that, even under congestion, our proposed algorithm can improve large-scale application I/O performance significantly, resulting in both a reduction in application run time as well as a higher resolution of simulation run.

关键词： Storage Area Network parallel file system High Performance Computing Performance Evaluation

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