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High-Performance distributed Multi-Model / Multi-Kernel Simulations: A Case-Study in Jungle Computing

High-Performance Distributed Multi-Model / Multi-Kernel Simu...

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ieee international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Niels Drost Jason Maassen Maarten A.J. van Meersbergen Henri E. Bal F. Inti Pelupessy Simon Portegies Zwart Michael Kliphuis Henk A. Dijkstra Frank J. Seinstra Department of Computer Science VU University Amsterdam Netherlands Leiden Observatory Leiden University Leiden Netherlands Institute for Marine and Atmospheric research Utrecht Department of Physics and Astronomy University of Utrecht Utrecht Netherlands

High-performance scientific applications require more and more compute power. the concurrent use of multiple distributed compute resources is vital for making scientific progress. the resulting distributed system, a so-called Jungle Computing System, is both highly heterogeneous and hierarchical, potentially consisting of grids, clouds, stand-alone machines, clusters, desktop grids, mobile devices, and supercomputers, possibly with accelerators such as GPUs. One striking example of applications that can benefit greatly of Jungle Computing Systems are Multi-Model / Multi-Kernel simulations. In these simulations, multiple models, possibly implemented using different techniques and programming models, are coupled into a single simulation of a physical system. Examples include the domain of computational astrophysics and climate modeling. In this paper we investigate the use of Jungle Computing Systems for such Multi-Model / Multi-Kernel simulations. We make use of the software developed in the Ibis project, which addresses many of the problems faced when running applications on Jungle Computing Systems. We create a prototype Jungle-aware version of AMUSE, an astrophysical simulation framework. We show preliminary experiments with the resulting system, using clusters, grids, stand-alone machines, and GPUs.

关键词： Computational modeling Atmospheric modeling Middleware Couplers Supercomputers Kernel

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Optimizing Data Warehousing applications for GPUs Using Kernel Fusion/Fission

Optimizing Data Warehousing Applications for GPUs Using Kern...

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ieee international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Haicheng Wu Gregory Diamos Jin Wang Srihari Cadambi Sudhakar Yalamanchili Srimat Chakradhar School of ECE Georgia Institute of Technology Atlanta GA USA Nvidia Research Santa Clara CA USA NEC Laboratories of America Inc. Princeton NJ USA

ISBN: (纸本)9781467309745

Data warehousing applications represent an emergent application arena that requires the processing of relational queries and computations over massive amounts of data. Modern general purpose GPUs are high core count architectures that potentially offer substantial improvements in throughput for these applications. However, there are significant challenges that arise due to the overheads of data movement through the memory hierarchy and between the GPU and host CPU. this paper proposes a set of compiler optimizations to address these challenges. Inspired in part by loop fusion/fission optimizations in the scientific computing community, we propose kernel fusion and kernel fission. Kernel fusion fuses the code bodies of two GPU kernels to i) eliminate redundant operations across dependent kernels, ii) reduce data movement between GPU registers and GPU memory, iii) reduce data movement between GPU memory and CPU memory, and iv) improve spatial and temporal locality of memory references. Kernel fission partitions a kernel into segments such that segment computations and data transfers between the GPU and host CPU can be overlapped. Fusion and fission can also be applied concurrently to a set of kernels. We empirically evaluate the benefits of fusion/fission on relational algebra operators drawn from the TPC-H benchmark suite. All kernels are implemented in CUDA and the experiments are performed with NVIDIA Fermi GPUs. In general, we observed data throughput improvements ranging from 13.1% to 41.4% for the SELECT operator and queries Q1 and Q21 in the TPC-H benchmark suite. We present key insights, lessons learned, and opportunities for further improvements.

关键词： Kernel Graphics processing unit Optimization Bandwidth Warehousing throughput Memory management

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Adapting Active Objects to Multicore Architectures

Adapting Active Objects to Multicore Architectures

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international symposium on parallel and distributed Computing

作者： Ludovic Henrio Fabrice Huet Zsolt Istv´n Gheorghe Sebestyen INRIA - CNRS - 13S - Univ Nice Sophia Antipolis France Technical University of Cluj-Napoca Romania

there are several programming paradigms that help programmers write efficient and verifiable code for distributed environments. these solutions, however, often lack proper support for local parallelism. In this article we try to improve existing solutions for providing a distributed, highly parallel framework that is easy to program. We propose an extension to the active object programming model which optimizes the local performance of applications by harnessing the full computing power of multi-core CPUs. the need for explicit locking mechanisms is reduced by the addition of meta-information to the methods in the source code. this paper describes this language-independent meta-information, and the way we intend to use it for parallelizing execution inside an active object.

关键词： parallel processing Programming Synchronization Computational modeling Concurrent computing Java Message systems

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A Server-Level Adaptive Data Layout Strategy for parallel File Systems

A Server-Level Adaptive Data Layout Strategy for Parallel Fi...

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ieee international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Huaiming Song Hui Jin Jun He Xian-He Sun Rajeev thakur Research and Development Center Dawning Information Industry Beijing China Department of Computer Science Illinois Institute of Technology Chicago IL USA Mathematics and Computer Science Division Argonne National Laboratory Argonne IL USA

ISBN: (纸本)9781467309745

parallel file systems are widely used for providing a high degree of I/O parallelism to mask the gap between I/O and memory speed. However, peak I/O performance is rarely attained due to complex data access patterns of applications. Based on the observation that the I/O performance of small requests is often limited by the request service rate, and the performance of large requests is limited by I/O bandwidth, we take into consideration both factors and propose a server-level adaptive data layout strategy. the proposed strategy adopts different stripe sizes for different file servers according to the data access characteristics on each individual server. We let the file servers that can fully utilize bandwidth hold more data, and the file servers that are limited with request service rate hold less data. As a result, heavy-load servers can offload some data accesses to light-load servers for potential improvement of I/O performance. We present a method to measure access cost for each data block and then utilize an equal-depth histogram approach to distributed data blocks across multiple servers adaptively, so as to balance data accesses on all file servers. Analytical and experimental results demonstrate that the proposed server-level adaptive layout strategy can improve I/O performance by as much as 80.3% and is more appropriate for applications with complex data access patterns.

关键词： Servers Layout Bandwidth Optimization Histograms distributed databases Arrays

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An adaptive SOR algorithm and its parallel implementation for power system applications

An adaptive SOR algorithm and its parallel implementation fo...

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international symposium on parallel and distributed processing (IPDPS)

作者： G. Huang W. Ongsakul Department of Electrical Engineering Texas A and M University College Station TX USA

In our earlier papers,we investigated the parallelization and implementation of Gauss-Seidel (G-S) and Successive Overrelaxation (SOR) power flow analysis on shared memory, (SM) and distributed (DM) machines. For the SOR case, constant acceleration factors obtained from experiments are used to speedup convergence. In this paper, we introduce a new adaptive nonlinear SOR (ANSOR) algorithm which uses an approximated optimal acceleration factor obtained during the iteration process. the algorithm is shown to be faster due to the significant reduction in the number of iterations, and to converge robustly under heavily-loaded conditions on large power systems. We also implement parallel and sequential versions of our ANSOR algorithm on the nCUBE2 machine, and show that our algorithm is competitive with the fast decoupled load flow (FDLF) algorithm. Moreover, the portability of the parallel ANSOR code is demonstrated by porting the code to the Intel iPSC/860 hypercube and the Paragon mesh MIMD machines. However, our new algorithm is not a panacea for all problems, as we demonstrate with an example from transient stability analysis.< >

关键词： Acceleration Gaussian distribution Load flow analysis Samarium Delta modulation Convergence Robustness Power systems Load flow Hypercubes

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Application for parallel disks for efficient handling of object-oriented databases

Application for parallel disks for efficient handling of obj...

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international symposium on parallel and distributed processing (IPDPS)

作者： Y.C. Chehadeh A.R. Hurson L.L. Miller S. Pakzad B.N. Jamoussi Department of Computer Science & Engineering Pennsylvania State University University Park PA USA Computer Science Department Iowa State University Ames IA USA

In today's workstation based environment, applications such as design databases, multimedia databases, and knowledge bases do not fit well into the relational data processing framework. the object-oriented data model has been proposed to model and process such complex databases. Due to the nature of the supported applications, object-oriented database systems need efficient mechanisms for the retrieval of complex objects and the navigation along the semantic links among objects. Object clustering and buffering have been suggested as efficient mechanisms for the retrieval of complex objects. However, to improve the efficiency of the aforementioned operations, one has to look at the recent advances in storage technology. this paper is an attempt to investigate the feasibility of using parallel disks for object-oriented databases. It analyzes the conceptual changes needed to map the clustering and buffering schemes proposed on the new underlying architecture. the simulation and performance evaluation of the proposed leveled-clustering and mapping schemes utilizing parallel I/O disks are presented and analyzed.< >

关键词： Object oriented databases Multimedia databases Relational databases Object oriented modeling Workstations Data processing Data models Database systems Information retrieval Navigation

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Load Balancing of Dynamical Nucleation theory Monte Carlo Simulations through Resource Sharing Barriers

Load Balancing of Dynamical Nucleation Theory Monte Carlo Si...

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international symposium on parallel and distributed processing (IPDPS)

作者： Humayun Arafat P. Sadayappan James Dinan Sriram Krishnamoorthy theresa L. Windus Department Computer Science and Engineering Ohio State University USA Mathematics and Computer Science Division Argonne National Laboratory USA Computer Science and Mathematics Division Pacific Northwest National Laboratory USA Department of Chemistry Iowa State University USA

the dynamical nucleation theory Monte Carlo (DNTMC) application from the NW Chem computational chemistry suite utilizes a Markov chain Monte Carlo, two-level parallel structure, with periodic synchronization points that assemble the results of independent finer-grained calculations. Like many such applications, the existing code employs a static partitioning of processes into groups and assigns each group a piece of the finer-grained parallel calculation. A significant cause of performance degradation is load imbalance among groups since the time requirements of the inner-parallel calculation varies widely with the input problem and as a result of the Monte Carlo simulation. We present a novel approach to load balancing such calculations with minimal changes to the application. We introduce the concept of a resource sharing barrier (RSB) - a barrier that allows process groups waiting on other processes' work to actively contribute to their completion. the RSB load balancing technique is applied to the production DNTMC application code, resulting in a small code change of 200 lines and a reduction in execution time of up to 37%.

关键词： Resource management Monte Carlo methods Markov processes Context parallel processing Load modeling Computational modeling

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On the impact of data input sets on statistical compiler tuning 06

On the impact of data input sets on statistical compiler tun...

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international symposium on parallel and distributed processing (IPDPS)

作者： M. Haneda P.M.W. Knijnenburg H.A.G. Wijshoff LIACS-Leiden University Netherlands University of Amsterdam Netherlands

ISBN: (纸本)9781424400546

In recent years, several approaches have been proposed to use profile information in compiler optimization. this profile information can be used at the source level to guide loop transformations as well as in the backend to guide low level optimizations. At the same time, profile guided library generators have been proposed also, like Atlas, Spiral, or FFTW, that tune their routines for the underlying hardware. these approaches have led to excellent performance improvements. However, a possible drawback of these approaches is that applications are optimized using a single or a limited set of data inputs. It is well known that programs can exhibit vastly differing behaviors for different inputs. therefore, it is not clear whether the performance numbers reported are still valid for other input than the input used to optimize the program. In this paper, we address this problem for a specific statistical compiler tuning method. We use three different platforms and several SPECint2000 benchmarks. We show that when we tune the compiler using train data, we obtain a compiler setting that still performs well for reference data. these results suggest that profile guided optimization may be more stable than is sometimes believed and that a limited number of train data sets are sufficient to obtain a well optimized program for all inputs

关键词： Optimizing compilers Libraries Spirals Hardware Program processors Kernel Probes Digital signal processing Feedback Engines

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In-Map/In-Reduce: Concurrent Job Execution in MapReduce

In-Map/In-Reduce: Concurrent Job Execution in MapReduce

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ieee international Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Muhammad Idris Shujaat Hussain Sungyoung Lee College of Electronics and Information Kyung Hee University Republic of Korea

Hadoop based Map Reduce (MR) has emerged as big data processing mechanism in terms of its data intensive applications. In data intensive systems, analysis and visualizations as a result of various algorithms can lead to differentiable and comparable results. Current implementations of MR facilitates to reuse the results of MR jobs in other MR jobs and to distribute the cloud resources among jobs. However, very little work is done in terms of using same data for multiple algorithms at the same time in a single job using either shared resources or dynamic resource allocation based on the data and scheduling of Map Reduce jobs. In this paper we propose a method to execute multiple algorithms on same data in HDFS concurrently and to use the same available resources by dynamically managing the task assignment and results aggregation. Our proposed approach reduces the execution time and supports multiple algorithms execution in parallel. In-Map/In-Reduce shows 200% decrease in execution time.

关键词： Partitioning algorithms Clustering algorithms Big data Algorithm design and analysis distributed databases Educational institutions

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Flexible Linear Algebra Development and Scheduling with Cholesky Factorization

Flexible Linear Algebra Development and Scheduling with Chol...

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ieee international Conference on High Performance Computing and Communications (HPCC)

作者： Azzam Haidar Asim YarKhan Chongxiao Cao Piotr Luszczek Stanimire Tomov Jack Dongarra University of Tennessee Knoxville USA University of Tennessee USA University of Manchester Knoxville UK

Modern high performance computing environments are composed of networks of compute nodes that often contain a variety of heterogeneous compute resources, such as multicore CPUs and GPUs. One challenge faced by domain scientists ishow to efficiently use all these distributed, heterogeneous resources. Inorder to use the GPUs effectively, the workload parallelism needs to be muchgreater than the parallelism for a multicore-CPU. Additionally, effectivelyusing distributed memory nodes brings out another level of complexity where theworkload must be carefully partitioned over the nodes. In this work we areusing a lightweight runtime environment to handle many of the complexities insuch distributed, heterogeneous systems. the runtime environment usestask-superscalar concepts to enable the developer to write serial code whileproviding parallel execution. the task-programming model allows the developerto write resource-specialization code, so that each resource gets theappropriate sized workload-grain. Our task-programming abstraction enables thedeveloper to write a single algorithm that will execute efficiently across the distributed heterogeneous machine. We demonstrate the effectiveness of ourapproach with performance results for dense linear algebra applications, specifically the Cholesky factorization.

关键词： Multicore processing Runtime Hardware Graphics processing units Scalability Linear algebra parallel processing

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