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检索条件"任意字段=23rd ACM Symposium on High-Performance Parallel and Distributed Computing, HPDC 2014"

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hpdc 2014 - Proceedings of the 23rd International symposium on high-performance parallel and distributed computing

HPDC 2014 - Proceedings of the 23rd International Symposium ...

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23rd acm symposium on high-performance parallel and distributed computing, hpdc 2014

ISBN: (纸本)9781450327480

The proceedings contain 34 papers. The topics discussed include: Snapify: capturing snapshots of offload applications on Xeon Phi Manycore processors;transparent checkpoint-restart over infiniband;ConCOrd easily exploiting memory content redundancy through the content-aware service command;FT-ScaLAPACK: correcting soft errors on-line for ScaLAPACK Cholesky, QR, and LU factorization routines;CMCP: a novel page replacement policy for system level hierarchical memory management on many-cores;TOP-PIM: throughput-oriented programmable processing in memory;improving energy efficiency of embedded DRAM caches for high-end computing systems;OpenARC: open accelerator research compiler for directive-based, efficient heterogeneous computing;MIC-Check: a distributed checkpointing framework for the Intel many integrated cores architecture;efficient task placement and routing of nearest neighbor exchanges in dragonfly networks;and design and evaluation of the GeMTC framework for GPU-enabled many-task computing.

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A scalable distributed skip list for range queries 14

A scalable distributed skip list for range queries

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23rd acm symposium on high-performance parallel and distributed computing, hpdc 2014

作者： Alam, Sarwar Kamal, Humaira Wagner, Alan Dept. of Computer Science University of British Columbia Vancouver BC Canada

ISBN: (纸本)9781450327480

In this paper we present a distributed, message passing implementation of a dynamic dictionary structure for range queries. The structure is based on a distributed fine-grain implementation of skip lists that can scale across a cluster of multicore machines. Our implementation makes use of the unique features of Fine-Grain MPI and introduces novel algorithms and techniques to achieve scalable performance on a cluster of multicore machines. Unlike concurrent data structures the distributed skip list operations are deterministic and atomic. Range-queries are implemented in a way that parallelizes the operation and takes advantage of the recursive properties of the skip list structure. We report on the performance of the skip list for range-queries, on a medium sized cluster with two hundred cores. Copyright © 2014 acm.

关键词： Message passing

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Next generation job management systems for extreme-scale ensemble computing 14

Next generation job management systems for extreme-scale ens...

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23rd acm symposium on high-performance parallel and distributed computing, hpdc 2014

作者： Wang, Ke Zhou, Xiaobing Chen, Hao Lang, Michael Raicu, Ioan Illinois Institute of Technology Chicago IL 60616 United States Los Alamos National Laboratory Los Alamos UT 87544 United States

ISBN: (纸本)9781450327480

With the exponential growth of supercomputers in parallelism, applications are growing more diverse, including traditional largescale HPC MPI jobs, and ensemble workloads such as finergrained many-task computing (MTC) applications. Delivering high throughput and low latency for both workloads requires developing a distributed job management system that is magnitudes more scalable than today's centralized ones. In this paper, we present a distributed job launch prototype, SLURM++, which is comprised of multiple controllers with each one managing a partition of SLURM daemons, while ZHT (a distributed key-value store) is used to store the job and resource metadata. We compared SLURM++ with SLURM using microbenchmarks of different job sizes up to 500 nodes, with excellent results showing 10X higher throughput. We also studied the potential of distributed scheduling through simulations up to millions of nodes. Copyright © 2014 acm.

关键词： Supercomputers

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OpenARC: Open Accelerator Research Compiler for directive-based, efficient heterogeneous computing 14

OpenARC: Open Accelerator Research Compiler for directive-ba...

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23rd acm symposium on high-performance parallel and distributed computing, hpdc 2014

作者： Lee, Seyong Vetter, Jeffrey S. Oak Ridge National Laboratory Oak Ridge TN 37831 United States Georgia Institute of Technology Atlanta GA 30332 United States

ISBN: (纸本)9781450327480

This paper presents Open Accelerator Research Compiler (OpenARC): an open-source framework that supports the full feature set of OpenACC V1.0 and performs source-tosource transformations, targeting heterogeneous devices, such as NVIDIA GPUs. Combined with its high-level, extensible Intermediate Representation (IR) and rich semantic annotations, OpenARC serves as a powerful research vehicle for prototyping optimization, source-to-source transformations, and instrumentation for debugging, performance analysis, and autotuning. Infact, OpenARC is equipped with various capabilities for advanced analyses and transformations, as well as built-in performance and debugging tools. We explain the overall design and implementation of OpenARC, and we present key analysis techniques necessary to efficiently port OpenACC applications. Porting various OpenACC applications to CUDA GPUs using OpenARC demonstrates that OpenARC performs similarly to a commercial compiler, while serving as a general research framework. Copyright © 2014 acm.

关键词： Graphics processing unit

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Domino: An incremental computing framework In cloud with eventual synchronization 14

Domino: An incremental computing framework In cloud with eve...

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23rd acm symposium on high-performance parallel and distributed computing, hpdc 2014

作者： Dai, Dong Chen, Yong Kimpe, Dries Ross, Rob Zhou, Xuehai Dept. of Computer Science Texas Tech University United States Mathematics and Computer Science Division Argonne National Laboratory United States Computer Science College University of Science and Technology of China China

ISBN: (纸本)9781450327480

In recent years, more and more applications in cloud have needed to process large-scale on-line data sets that evolve over time as entries are added or modified. Several programming frameworks, such as Percolator and Oolong, are proposed for such incremental data processing and can achieve efficient updates with an event-driven abstraction. However, these frameworks are inherently asynchronous, leaving the heavy burden of managing synchronization to applications developers. Such a limitation significantly restricts their usability. In this paper, we introduce a trigger-based incremental computing framework, called Domino, with a flexible synchronization mechanism and runtime optimizations to coordinate parallel triggers efficiently. With this new framework, both synchronous and asynchronous applications can be seamlessly developed. Use cases and current evaluation results confirm that the new Domino programming model delivers sufficient performance and is easy to use in large-scale distributed computing. Copyright © 2014 acm.

关键词： MapReduce

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Improving parallel I/O autotuning with performance modeling 14

Improving parallel I/O autotuning with performance modeling

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23rd acm symposium on high-performance parallel and distributed computing, hpdc 2014

作者： Behzad, Babak Byna, Surendra Wild, Stefan M. Prabhat Snir, Marc Univ. of Illinois Urbana-Champaign United States Lawrence Berkeley Laboratory United States Argonne National Laboratory United States

ISBN: (纸本)9781450327480

Various layers of the parallel I/O subsystem offer tunable parameters for improving I/O performance on large-scale computers. However, searching through a large parameter space is challenging. We are working towards an autotuning framework for determining the parallel I/O parameters that can achieve good I/O performance for different data write patterns. In this paper, we characterize parallel I/O and discuss the development of predictive models for use in effectively reducing the parameter space. Applying our technique on tuning an I/O kernel derived from a large-scale simulation code shows that the search time can be reduced from 12 hours to 2 hours, while achieving 54X I/O performance speedup. Copyright © 2014 acm.

关键词： optimization autotuning performance modeling performance parallel i/o

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Data filtering for scalable high-dimensional k-NN search on multicore systems 14

Data filtering for scalable high-dimensional k-NN search on ...

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23rd acm symposium on high-performance parallel and distributed computing, hpdc 2014

作者： Tang, Xiaoxin Mills, Steven Eyers, David Leung, Kai-Cheung Huang, Zhiyi Guo, Minyi Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China Department of Computer Science University of Otago New Zealand

ISBN: (纸本)9781450327480

K Nearest Neighbors (k-NN) search is a widely used category of algorithms with applications in domains such as computer vision and machine learning. With the rapidly increasing amount of data available, and their high dimensionality, k-NN algorithms scale poorly on multicore systems because they hit a memory wall. In this paper, we propose a novel data filtering strategy, named Subspace Clustering for Filtering (SCF), for k-NN search algorithms on multicore platforms. By excluding unlikely features in k-NN search, this strategy can reduce memory footprint as well as computation. Experimental results on four k-NN algorithms show that SCF can improve their performance on two modern multicore platforms with insignificant loss of search precision. Copyright © 2014 acm.

关键词： Nearest neighbor search

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Scalable Matrix inversion using MapReduce 14

Scalable Matrix inversion using MapReduce

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23rd acm symposium on high-performance parallel and distributed computing, hpdc 2014

作者： Xiang, Jingen Meng, Huangdong Aboulnaga, Ashraf SAP Waterloo ON Canada University of Toronto Toronto ON Canada Qatar Computing Research Institute Doha Qatar

ISBN: (纸本)9781450327480

Matrix operations are a fundamental building block of many computational tasks in fields as diverse as scientific computing, machine learning, and data mining. Matrix inversion is an important matrix operation, but it is difficult to implement in today's popular parallel dataflow programming systems, such as MapReduce. The reason is that each element in the inverse of a matrix depends on multiple elements in the input matrix, so the computation is not easily partitionable. In this paper, we present a scalable and efficient technique for matrix inversion in MapReduce. Our technique relies on computing the LU decomposition of the input matrix and using that decomposition to compute the required matrix inverse. We present a technique for computing the LU decomposition and the matrix inverse using a pipeline of MapReduce jobs. We also present optimizations of this technique in the context of Hadoop. To the best of our knowledge, our technique is the first matrix inversion technique using MapReduce. We show experimentally that our technique has good scalability, enabling us to invert a 105 ×105 matrix in 5 hours on Amazon EC2. We also show that our technique outperforms ScaLAPACK, a state-of-the-art linear algebra package that uses MPI. Copyright © 2014 acm.

关键词： MapReduce

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MIC-check: A distributed checkpointing framework for the Intel many integrated cores architecture 14

MIC-check: A distributed checkpointing framework for the Int...

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23rd acm symposium on high-performance parallel and distributed computing, hpdc 2014

作者： Rajachandrasekar, Raghunath Potluri, Sreeram Venkatesh, Akshay Hamidouche, Khaled Wasi-Ur-Rahman, Md. Panda, Dhabaleswar K. Ohio State University United States

ISBN: (纸本)9781450327480

The advent of many-core architectures like Intel MIC is enabling the design of increasingly capable supercomputers within reasonable power budgets. Fault-tolerance is becoming more important with the increased number of components and the complexity in these heterogeneous clusters. Checkpoint-restart mechanisms have been traditionally used to enhance the dependability of applications, and to enable dynamic task rescheduling in the face of system failures. Naive checkpointing protocols, which are predominantly I/O-intensive, face severe performance bottlenecks on the Xeon Phi architecture due to several inherent and acquired limitations. Consequently, existing checkpointing frameworks are not capable of serving distributed MPI applications that leverage heterogeneous hardware architectures. This paper discusses the I/O limitations on the Xeon Phi system, and describes the architecture and design of a novel distributed checkpointing framework, namely MIC-Check, for HPC applications running on it. Copyright © 2014 acm.

关键词： Fault tolerance

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Bobolang - A language for parallel streaming applications 14

Bobolang - A language for parallel streaming applications

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23rd acm symposium on high-performance parallel and distributed computing, hpdc 2014

作者： Falt, Zbynek Bednárek, David Kruliš, Martin Yaghob, Jakub Zavoral, Filip Charles University in Prague Faculty of Mathematics and Physics Czech Republic

ISBN: (纸本)9781450327480

At present time, the programmers may choose from a number of streaming languages. They cover various aspects of the development process of streaming applications;however, specification of complex or runtime-dependent parts of the applications still remains a great challenge. We have analysed a large amount of requirements raised by the development of multiple data streaming parallel applications and proposed a novel language called Bobolang. It contains syntactic and semantic features which allow the programmer to naturally solve most of the problems, which we met in the design of streaming applications. The language is used to specify the structure of the whole application as well as the inner structure of each operator. Thanks to the properties of the language, Bobolang can create an optimized evaluation plan which is capable of making the best use of the available hardware resources. The language has been employed in several practical problems and it has proven itself to be a very powerful tool for the development of data-intensive parallel applications. Copyright © 2014 acm.

关键词： Semantics

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