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检索条件"任意字段=22nd ACM International Symposium on High-Performance Parallel and Distributed Computing, HPDC 2013"

共 40 条记录，以下是1-10 订阅

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hpdc 2013 - Proceedings of the 22nd acm international symposium on high-performance parallel and distributed computing

HPDC 2013 - Proceedings of the 22nd ACM International Sympos...

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22nd acm international symposium on high-performance parallel and distributed computing, hpdc 2013

ISBN: (纸本)9781450319102

The proceedings contain 30 papers. The topics discussed include: taming massive distributed datasets: data sampling using bitmap indices;MTC envelope: defining the capability of large scale computers in the context of parallel scripting applications;virtual TCP offload: optimizing Ethernet overlay performance on advanced interconnects;scalanytics: a declarative multi-core platform for scalable composable traffic analytics;NUMA-aware shared-memory collective communication for MPI;ACIC: automatic cloud I/O configurator for parallel applications;orthrus: a framework for implementing high-performance collective I/O in the multicore clusters;supporting parallel soft real-time applications in virtualized environment;building and scaling virtual clusters with residual resources from interactive clouds;and SCDA: SLA-aware cloud datacenter architecture for efficient content storage and retrieval.

关键词： Digital storage

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Supporting parallel soft real-time applications in virtualized environment

Supporting parallel soft real-time applications in virtualiz...

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22nd acm international symposium on high-performance parallel and distributed computing, hpdc 2013

作者： Zhou, Like Wu, Song Sun, Huahua Jin, Hai Shi, Xuanhua Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology Wuhan 430074 China

ISBN: (纸本)9781450319102

The prevalence of multicore processors and virtualization technology enables parallel soft real-time applications to run in virtualized environment. However, current hypervisors do not provide adequate support for them because of soft real-time constraints and synchronization problems, which result in frequent deadline misses and serious performance degradation. In this paper, we propose a novel parallel soft real-time scheduling algorithm which addresses them well, and implement a parallel soft real-time scheduler, named Poris, based on Xen. Our evaluation shows that Poris shortens the execution time of PARSEC benchmark by up to 44.12% compared to Credit scheduler. © 2013 Authors.

关键词： Virtualization

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ACIC: Automatic cloud I/O configurator for parallel applications

ACIC: Automatic cloud I/O configurator for parallel applicat...

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22nd acm international symposium on high-performance parallel and distributed computing, hpdc 2013

作者： Liu, Mingliang Jin, Ye Zhai, Jidong Zhai, Yan Shi, Qianqian Ma, Xiaosong Chen, Wenguang Tsinghua University Beijing China NCSU Raleigh NC United States UW-Madison Madison WI United States NCSU ORNL Raleigh NC United States

ISBN: (纸本)9781450319102

To tackle the highlighted I/O bottleneck problem in cloud, we proposes ACIC, a system which automatically searches for optimized I/O system configurations from many candidates for each individual application running on a given cloud platform. The top ACIC-recommended configuration improves the applications? performance by a factor of up to 10.5 (3.0 on average), and cost saving of up to 89% (53% on average), compared with a commonly used baseline. © 2013 Authors.

关键词： Computer architecture

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Modeling communication in cache-coherent SMP systems - A case study with Xeon Phi

Modeling communication in cache-coherent SMP systems - A cas...

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22nd acm international symposium on high-performance parallel and distributed computing, hpdc 2013

作者： Ramos, Sabela Hoefler, Torsten Computer Architecture Group University of A Coruña A Coruña Spain Scalable Parallel Computing Lab. ETH Zurich Zurich Switzerland

ISBN: (纸本)9781450319102

Most multi-core and some many-core processors implement cache coherency protocols that heavily complicate the design of optimal parallel algorithms. Communication is performed implicitly by cache line transfers between cores, complicating the understanding of performance properties. We developed an intuitive performance model for cache-coherent architectures and demonstrate its use with the currently most scalable cache-coherent many-core architecture, Intel Xeon Phi. Using our model, we develop several optimal and optimized algorithms for complex parallel data exchanges. All algorithms that were developed with the model beat the performance of the highly-tuned vendor-specific Intel OpenMP and MPI libraries by up to a factor of 4.3. The model can be simplified to satisfy the tradeoff between complexity of algorithm design and accuracy. We expect that our model can serve as a vehicle for advanced algorithm design. © 2013 acm.

关键词： Electronic data interchange

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IBIS: Interposed big-data I/O scheduler

IBIS: Interposed big-data I/O scheduler

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22nd acm international symposium on high-performance parallel and distributed computing, hpdc 2013

作者： Xu, Yiqi Suarez, Adrian Zhao, Ming Florida International University 11200 S.W. 8th Street Miami FL 33199 United States

ISBN: (纸本)9781450319102

Existing big-data systems (e.g., Hadoop/MapReduce) do not expose management of shared storage I/O resources. As such, application's performance may degrade in unpredictable ways under I/O contention, even with fair sharing of computing resources. This paper proposes IBIS, a new Interposed Big-data I/O Scheduler, to provide performance differentiation for competing applications' I/Os in a shared MapReduce-type big-data system. IBIS is implemented in Hadoop by interposing HDFS I/Os and use an SFQ-based proportional-sharing algorithm. Experiments show that the IBIS provides strong performance isolation for one application against another highly I/O-intensive application. IBIS also enforces good proportional sharing of the global bandwidth among competing parallel applications, by coordinating distributed IBIS schedulers to deal with the uneven distribution of local services in big-data systems. © 2013 Authors.

关键词： Big data

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AI-Ckpt: Leveraging memory access patterns for adaptive asynchronous incremental checkpointing

AI-Ckpt: Leveraging memory access patterns for adaptive asyn...

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22nd acm international symposium on high-performance parallel and distributed computing, hpdc 2013

作者： Nicolae, Bogdan Cappello, Franck IBM Research Dublin Ireland Joint Laboratory for Petascale Computing INRIA France University of Illinois Urbana-Champaign IL United States

ISBN: (纸本)9781450319102

With increasing scale and complexity of supercomputing and cloud computing architectures, faults are becoming a frequent occurrence, which makes reliability a difficult challenge. Although for some applications it is enough to restart failed tasks, there is a large class of applications where tasks run for a long time or are tightly coupled, thus making a restart from scratch unfeasible. Checkpoint-Restart (CR), the main method to survive failures for such applications faces additional challenges in this context: not only does it need to minimize the performance overhead on the application due to checkpointing, but it also needs to operate with scarce resources. Given the iterative nature of the targeted applications, we launch the assumption that first-time writes to memory during asynchronous checkpointing generate the same kind of interference as they did in past iterations. Based on this assumption, we propose novel asynchronous checkpointing approach that leverages both current and past access pattern trends in order to optimize the order in which memory pages are flushed to stable storage. Large scale experiments show up to 60% improvement when compared to state-of-art checkpointing approaches, all this achievable with an extra memory requirement of less than 5% of the total application memory. © 2013 acm.

关键词： Fault tolerance

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MTC envelope: Defining the capability of large scale computers in the context of parallel scripting applications

MTC envelope: Defining the capability of large scale compute...

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22nd acm international symposium on high-performance parallel and distributed computing, hpdc 2013

作者： Zhang, Zhao Katz, Daniel S. Wilde, Michael Wozniak, Justin M. Foster, Ian Department of Computer Science University of Chicago Chicago IL United States Computation Institute University of Chicago Argonne National Laboratory Chicago IL United States Mathematics and Computer Science Division Argonne National Laboratory Argonne IL United States

ISBN: (纸本)9781450319102

Many scientific applications can be efficiently expressed with the parallel scripting (many-task computing, MTC) paradigm. These applications are typically composed of several stages of computation, with tasks in different stages coupled by a shared file system abstraction. However, we often see poor performance when running these applications on large scale computers due to the applications' frequency and volume of filesystem I/O and the absence of appropriate optimizations in the context of parallel scripting applications. In this paper, we show the capability of existing large scale computers to run parallel scripting applications by first defining the MTC envelope and then evaluating the envelope by benchmarking a suite of shared filesystem performance metrics. We also seek to determine the origin of the performance bottleneck by profiling the parallel scripting applications' I/O behavior and mapping the I/O operations to the MTC envelope. We show an example shared filesystem envelope and present a method to predict the I/O performance given the applications' level of I/O concurrency and I/O amount. This work is instrumental in guiding the development of parallel scripting applications to make efficient use of existing large scale computers, and to evaluate performance improvements in the hardware/software stack that will better facilitate parallel scripting applications. © 2013 acm.

关键词： Benchmarking

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A comparative study of high-performance computing on the cloud

A comparative study of high-performance computing on the clo...

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22nd acm international symposium on high-performance parallel and distributed computing, hpdc 2013

作者： Marathe, Aniruddha Harris, Rachel Lowenthal, David K. De Supinski, Bronis R. Rountree, Barry Schulz, Martin Yuan, Xin Dept. of Computer Science University of Arizona Tucson AZ United States Lawrence Livermore National Laboratory Livermore CA United States Dept. of Computer Science Florida State University Tallahassee FL United States

ISBN: (纸本)9781450319102

The popularity of Amazon's EC2 cloud platform has increased in recent years. However, many high-performance computing (HPC) users consider dedicated high-performance clusters, typically found in large compute centers such as those in national laboratories, to be far superior to EC2 because of significant communication overhead of the latter. Our view is that this is quite narrow and the proper metrics for comparing high-performance clusters to EC2 is turnaround time and cost. In this paper, we compare the top-of-the-line EC2 cluster to HPC clusters at Lawrence Livermore National Laboratory (LLNL) based on turnaround time and total cost of execution. When measuring turnaround time, we include expected queue wait time on HPC clusters. Our results show that although as expected, standard HPC clusters are superior in raw performance, EC2 clusters may produce better turnaround times. To estimate cost, we developed a pricing model - relative to EC2's node-hour prices - to set node-hour prices for (currently free) LLNL clusters. We observe that the cost-effectiveness of running an application on a cluster depends on raw performance and application scalability. © 2013 acm.

关键词： Cost effectiveness

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VGRIS: Virtualized GPU resource isolation and scheduling in cloud gaming

VGRIS: Virtualized GPU resource isolation and scheduling in ...

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22nd acm international symposium on high-performance parallel and distributed computing, hpdc 2013

作者： Yu, Miao Zhang, Chao Qi, Zhengwei Yao, Jianguo Wang, Yin Guan, Haibing School of Software Shanghai Jiao Tong University Shanghai China HP Labs. Palo Alto CA United States Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University Shanghai China Cylab Carnegie Mellon University United States

ISBN: (纸本)9781450319102

Fueled by the maturity of virtualization technology for Graphics Processing Unit (GPU), there is an increasing number of data centers dedicated to GPU-related computation tasks in cloud gaming. However, GPU resource sharing in these applications is usually poor. This stems from the fact that the typical cloud gaming service providers often allocate one GPU exclusively for one game. To achieve the efficiency of computational resource management, there is a demand for cloud computing to employ the multi-task scheduling technologies to improve the utilization of GPU. In this paper, we propose VGRIS, a resource management framework for Virtualized GPU Resource Isolation and Scheduling in cloud gaming. By leveraging the mature GPU paravirtualization architecture, VGRIS resides in the host through library API interception, while the guest OS and the GPU computing applications remain unmodified. In the proposed framework, we implemented three scheduling algorithms in VGRIS for different objectives, i.e., Service Level Agreement (SLA)-aware scheduling, proportional-share scheduling, and hybrid scheduling that mixes the former two. By designing such a scheduling framework, it is possible to handle different kinds of GPU computation tasks for different purposes in cloud gaming. Our experimental results show that each scheduling algorithm can achieve its goals under various workloads. © 2013 acm.

关键词： Graphics processing unit

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Taming massive distributed datasets: Data sampling using bitmap indices

Taming massive distributed datasets: Data sampling using bit...

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22nd acm international symposium on high-performance parallel and distributed computing, hpdc 2013

作者： Su, Yu Agrawal, Gagan Woodring, Jonathan Myers, Kary Wendelberger, Joanne Ahrens, James Computer Science and Engineering Ohio State University Columbus OH 43210 United States Los Alamos National Laboratory Los Alamos NM 87544 United States

ISBN: (纸本)9781450319102

With growing computational capabilities of parallel machines, scientific simulations are being performed at finer spatial and temporal scales, leading to a data explosion. The growing sizes are making it extremely hard to store, manage, disseminate, analyze, and visualize these datasets, especially as neither the memory capacity of parallel machines, memory access speeds, nor disk bandwidths are increasing at the same rate as the computing power. Sampling can be an effective technique to address the above challenges, but it is extremely important to ensure that dataset characteristics are preserved, and the loss of accuracy is within acceptable levels. In this paper, we address the data explosion problems by developing a novel sampling approach, and implementing it in a flexible system that supports server-side sampling and data subsetting. We observe that to allow subsetting over scientific datasets, data repositories are likely to use an indexing technique. Among these techniques, we see that bitmap indexing can not only effectively support subsetting over scientific datasets, but can also help create samples that preserve both value and spatial distributions over scientific datasets. We have developed algorithms for using bitmap indices to sample datasets. We have also shown how only a small amount of additional metadata stored with bitvectors can help assess loss of accuracy with a particular subsampling level. Some of the other properties of this novel approach include: 1) sampling can be flexibly applied to a subset of the original dataset, which may be specified using a value-based and/or a dimension-based subsetting predicate, and 2) no data reorganization is needed, once bitmap indices have been generated. We have extensively evaluated our method with different types of datasets and applications, and demonstrated the effectiveness of our approach. © 2013 acm.

关键词： Big data

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