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New Scheduling Strategies and Hybrid Programming for a parallel Right-looking Sparse LU Factorization Algorithm on Multicore Cluster Systems

New Scheduling Strategies and Hybrid Programming for a Paral...

引用

26th IEEE international parallel and distributed Processing symposium (IPDPS) / Workshop on High Performance Data Intensive computing

作者： Yamazaki, Ichitaro Li, Xiaoye S. Univ Calif Berkeley Lawrence Berkeley Natl Lab Computat Res Div Berkeley CA 94720 USA

ISBN: (纸本)9780769546759

parallel sparse LU factorization is a key computational kernel in the solution of a large-scale linear system of equations. In this paper, we propose two strategies to address some scalability issues of a factorization algorithm on modern HPC systems. The first strategy is at the algorithmic-level;we schedule independent tasks as soon as possible to reduce the idle time and the critical path of the algorithm. We demonstrate using thousands of cores that our new scheduling strategy reduces the runtime by nearly three-fold from that of a state-of-the-art pipelined factorization algorithm. The second strategy is at both programming-and architecture-levels;we incorporate light-weight OpenMP threads in each MPI process to reduce both memory and time overheads of a pure MPI implementation on manycore NUMA architectures. Using this hybrid programming paradigm, we obtain a significant reduction in memory usage while achieving a parallel efficiency competitive with that of a pure MPI paradigm. As a result, in comparison to a pure MPI paradigm which failed due to the per-core memory constraint, the hybrid paradigm could utilize more cores on each node and reduce the factorization time on the same number of nodes. We show extensive performance analysis of the new strategies using thousands of cores of the two leading HPC systems, a Cray-XE6 and an IBM iDataPlex.

关键词： Factorization

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Miss-Correlation Folding: Encoding Per-Block Miss Correlations in Compressed DRAM for Data Prefetching

Miss-Correlation Folding: Encoding Per-Block Miss Correlatio...

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26th IEEE international parallel and distributed Processing symposium (IPDPS) / Workshop on High Performance Data Intensive computing

作者： Liu, Gang Peir, Jih-Kwon Lee, Victor Univ Florida Dept Comp & Informat Sci & Eng Gainesville FL 32610 USA Intel Corp Parallel Comp Lab Intel Labs Santa Clara CA USA

ISBN: (纸本)9780769546759

Cache misses frequently exhibit repeated streaming behavior, i.e. a sequence of cache misses has a high tendency of being repeated. Correlation-based prefetchers record the missing streams in a history table for accurate prefetching. Saving a large miss history in off-chip DRAM is a practical implementation, but incurs access latency and consumes memory bandwidth which leads to performance degradation. In this paper, we investigate a new data prefetching mechanism based on per-block miss correlation where a miss is correlated with an earlier miss when the two misses are closely encountered both in time and space. The miss correlations are captured dynamically and saved along with the content of the data block using a simple data compression technique. As a result of this novel combination, our scheme provides unbounded correlation history and its prefetch metadata can be fetched together with demand data without incurring additional latency nor consuming any memory bandwidth. Performance evaluations using data-parallel applications demonstrate that prefetchers based on per-block miss correlations can improve IPC by 42-139% with an average of 88% compared to the IPC without prefetching. In comparison with regular stream prefetcher, sampled temporal streaming prefetcher and spatial-temporal memory streaming prefetcher, up to 115%, 99% and 98% IPC improvement can be obtained with an average about 36%, 26% and 27% respectively.

关键词： prefetch data parallel miss correlation compress cache temporal spatial

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Sequence Alignment on Massively parallel Heterogeneous Systems

Sequence Alignment on Massively Parallel Heterogeneous Syste...

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26th IEEE international parallel and distributed Processing symposium (IPDPS) / Workshop on High Performance Data Intensive computing

作者： Drozd, Aleksandr Maruyama, Naoya Matsuoka, Satoshi Tokyo Inst Technol Grad Sch Informat Sci & Technol Meguro Ku Tokyo 1528552 Japan

ISBN: (纸本)9780769546766

Bioinformatics is a quickly emerging area of science with many important applications to human life. Sequence alignment in various forms is one of the main instruments used in bioinformatics. This work is motivated by the ever-increasing amount of sequence data that requires more and more computation power for its processing. This task calls for new GPU-based systems and their higher computational potential and energy efficiency as compared to CPUs. We address the problem of facilitating faster sequence alignment using modern multi-GPU clusters. Our initial step was to develop a fast and scalable GPU exact short sequence aligner. We used matching algorithm with small memory footprint based on Burrows-Wheeler transform. We developed a mathematical model of computation and communication costs to find optimal memory partitioning strategy for index and queries. Our solution achieves 10 times speedup over previous implementation based on suffix array on one GPU and scales to multiple GPUs. Our next step will be to adapt the suggested data structure and performance model for multi-node multi-GPU approximate sequence alignment. It is also planned to use exact matching to detect common regions in large sequences and use it as an intermediate step in full-scale genome comparison.

关键词： GPU alignment Burrows-Wheeler transform

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Proceedings - 2012 IEEE/ACM 16th international symposium on distributed Simulation and Real Time Applications, DS-RT 2012

Proceedings - 2012 IEEE/ACM 16th International Symposium on ...

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2012 IEEE/ACM 16th international symposium on distributed Simulation and Real Time Applications, DS-RT 2012

ISBN: (纸本)9780769548463

The proceedings contain 33 papers. The topics discussed include: real-time distributed aircraft simulation through HLA;high-performance computing for data analytics;CUDA-enabled optimization of technical analysis parameters;distributed-parallel road traffic simulator for clusters of multi-core computers;execution time optimization using delayed multidimensional retiming;Dijkstra algorithm based reliability model;development of a schedulability analysis framework based on pTPN and UPPAAL with stopwatches;distributed infrastructure for multiscale computing;towards robust routing in HD Tree;distributed infrastructure for multiscale computing;parallel simulation models for the evaluation of future large-scale datacenter networks;pore networks simulation with parallel greedy algorithms;and problem decomposition for evacuation simulation using network flow.

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Highly Efficient Performance Portable Tracking of Evolving Surfaces

Highly Efficient Performance Portable Tracking of Evolving S...

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26th IEEE international parallel and distributed Processing symposium (IPDPS) / Workshop on High Performance Data Intensive computing

作者： Yu, Wei Franchetti, Franz Hoe, James C. Chen, Tsuhan Citadel Investment Grp Chicago IL 60603 USA Carnegie Mellon Univ ECE Pittsburgh PA 15213 USA Cornell Univ ECE Ithaca NY 14850 USA

ISBN: (纸本)9780769546759

In this paper we present a framework to obtain highly efficient implementations for the narrow band level set method on commercial off-the-shelf (COTS) multicore CPU systems with a cache-based memory hierarchy such as Intel Xeon and Atom processors. The narrow-band level set algorithm tracks wave-fronts in discretized volumes (for instance, explosion shock waves), and is computationally very demanding. At the core of our optimization framework is a novel projection-based approach to enhance data locality and enable reuse for sparse surfaces in dense discretized volumes. The method reduces stencil operations on sparse and changing sets of pixels belonging to an evolving surface into dense stencil operations on meta-pixels in a lower-dimensional projection of the pixel space. These meta-pixels are then amenable to standard techniques like time tiling. However, the complexity introduced by ever-changing meta-pixels requires us to revisit and adapt all other necessary optimizations. We apply adapted versions of SIMDization, multi-threading, DAG scheduling for basic tiles, and specialization through code generation to extract maximum performance. The system is implemented as highly parameterized code skeleton that is auto-tuned and uses program generation. We evaluated our framework on a dual-socket 2.8 GHz Xeon 5560 and a 1.6 GHz Atom N270. Our single-core performance reaches 26%-35% of the machine peak on the Xeon, and 12%-20% on the Atom across a range of image sizes. We see up to 6.5x speedup on 8 cores of the dual-socket Xeon. For cache-resident sizes our code outperforms the best available third-party code (C pre-compiled into a DLL) by about 10x and for the largest out-of-cache sizes the speedup approaches around 200x. Experiments fully explain the high speedup numbers.

关键词： level set parallel computing performance tuning scientific applications

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Modeling and Analyzing Key Performance Factors of Shared Memory MapReduce

Modeling and Analyzing Key Performance Factors of Shared Mem...

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26th IEEE international parallel and distributed Processing symposium (IPDPS) / Workshop on High Performance Data Intensive computing

作者： Tiwari, Devesh Solihin, Yan N Carolina State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

ISBN: (纸本)9780769546759

MapReduce parallel programming model has seen wide adoption in data center applications. Recently, lightweight, fast, in-memory MapReduce runtime systems have been proposed for shared memory systems. However, what factors affect performance and what performance bottlenecks exist for a given program, are not well understood. This paper builds an analytical model to capture key performance factors of shared memory MapReduce and investigates important performance trends and behavior. Our study discovers several important findings and implications for system designers, performance tuners, and programmers. Our model quantifies relative contribution of different key performance factors for both map and reduce phases, and shows that performance of MapReduce programs are highly input-content dependent. Our model reveals that performance is heavily affected by the order in which distinct keys are encountered during the Map phase, and the frequency of these distinct keys. Our model points out cases in which reduce phase time dominates the total execution time. We also show that data-structure and algorithm design choices affect map and reduce phases differently and sometimes affecting map phase positively while affecting reduce phase negatively. Finally, we propose an application classification framework that can be used to reason about performance bottlenecks for a given application.

关键词： Performance Factors MAPPING(MATHEMATICAL) maps execution time Phase shared memory Reduce Reduce Modeling Algorithm design parallel programming

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Resolving neighbourhood relations in a parallel fluid dynamic solver

Resolving neighbourhood relations in a parallel fluid dynami...

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2012 11th international symposium on parallel and distributed computing, ISPDC 2012

作者： Frisch, Jérôme Mundani, Ralf-Peter Rank, Ernst Department of Computation in Engineering Technische Universität München Germany

ISBN: (纸本)9780769548050

Computational Fluid Dynamics simulations require an enormous computational effort if a physically reasonable accuracy should be reached. Therefore, a parallel implementation is inevitable. This paper describes the basics of our implemented fluid solver with a special aspect on the hierarchical data structure, unique cell and grid identification, and the neighbourhood relations in-between grids on different processes. A special server concept keeps track of every grid over all processes while minimising data transfer between the nodes. © 2012 IEEE.

关键词： Computational fluid dynamics

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Comparing the Laplace Transform and Parareal Algorithms

Comparing the Laplace Transform and Parareal Algorithms

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11th international symposium on distributed computing and Applications to Business, Engineering and Science (DCABES)

作者： Douglas, Craig C. Douglas, Li Deng Lee, Hyoseop Sheen, Dongwoo Univ Wyoming Laramie WY 82071 USA Alcatel Lucent Bell Labs Seoul Seoul South Korea Seoul Natl Univ Seoul South Korea

ISBN: (纸本)9780769548180

Both the Laplace Transform and the Parareal family of algorithms promise to provide completely parallel in time and space computational results. Given a random time dependent partial differential equation it is unclear which algorithm will run faster. We define the algorithms in question, including more specific details than usual. We define interesting parallel environments, some of which do not exist yet. Finally, we demonstrate some computational environments in which one of the algorithms can be expected to be faster than the other algorithm.

关键词： parallel computing time-space parallelism partial differential equations PDE solvers

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parallel Hierarchical K-means Clustering-based Image Index Construction Method

Parallel Hierarchical K-means Clustering-based Image Index C...

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11th international symposium on distributed computing and Applications to Business, Engineering and Science (DCABES)

作者： Yang, Yuan-feng Wu, Jian Fang, Jing Cui, Zhi-ming JiangSu Prov Support Software Engn R&D Ctr Modern Suzhou 215104 Peoples R China Soochow Univ Inst Intelligent Informat Proc & Applicat Suzhou Peoples R China

ISBN: (纸本)9780769548180

Content-based image retrieval often uses the integration of various features. The characteristic dimensions are up to hundreds of dimensions. The capabilities for image representation, storage, management far exceed that of the database. Retrieval and matching of large-scale image are the urgent problems which need to be solved. To solve the construction of image indexing of large-scale image retrieval, this paper introduces a parallel level K-means clustering method. Firstly, image clustering based on the level K-means reduces the size of matching data in retrieval;secondly, considering the inherent defects in the cluster, we put forward optimization program and calculate cluster rapidly with parallel computing algorithms. The experimental results show that this method can quickly build a massive image index for fast image retrieval.

关键词： image feature index constructing K-means parallel computing

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Teaching parallel programming models on a shallow-water code

Teaching parallel programming models on a shallow-water code

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2012 11th international symposium on parallel and distributed computing, ISPDC 2012

作者： Breuer, Alexander Bader, Michael Department of Informatics Technische Universität München Germany

ISBN: (纸本)9780769548050

We present a software package that supports teaching different parallel programming models in a computational science and engineering context. It implements a Finite Volume solver for the shallow water equations, with application to tsunami simulation in mind. The numerical model is kept simple, using patches of Cartesian grids as computational domain, which can be connected via ghost layers. The Finite Volume method is restricted to piecewise constant approximation in each grid cell, but the computation of fluxes between cells can be based on the simple Lax-Friedrichs method, as well as on versatile approximate Riemann solvers, which allows realistic simulations. We present how this code can be used to study parallelization with CUDA, MPI, OpenMP, and hybrid approaches - and is useful for both introductory lectures in parallel computing and more advanced courses. © 2012 IEEE.

关键词： Teaching

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