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An overview of the ECO project 06

An overview of the ECO project

引用

international symposium on parallel and distributed processing (IPDPS)

作者： J. Chame Chun Chen P. Diniz M. Hall Yoon-Ju Lee R.F. Lucas Information Sciences Institute University of Southern California Marina del Rey CA USA

ISBN: (纸本)9781424400546

In this paper, we describe a compilation system that automates much of the process of performance tuning that is currently done manually by application programmers interested in high performance. Our approach combines compiler models and heuristics with guided empirical search to take advantage of their complementary strengths. the models and heuristics limit the search to a small number of candidate implementations, and the empirical results provide the most accurate information to the compiler to select among candidates and tune optimization parameter values. the overall approach can be employed to alleviate some of the performance problems that lead to inefficiencies in key applications today: register pressure, cache conflict misses, and the trade-off between synchronization, parallelism and locality in SMPs. the main focus of the paper is an algorithm for simultaneously optimizing across multiple levels of the memory hierarchy for dense-matrix computations. We have developed an initial compiler implementation, and present automatically-generated results on matrix multiply. Results on two architectures, SGI R10000 and Sun UltraSparc IIe, outperform the native compiler, and either outperform or achieve comparable performance as the ATLAS self-tuning library and the hand-tuned vendor BLAS library. this paper describes other components of the ECO system, including supporting tools and experiments with programmer-guided performance tuning. this approach has provided a foundation for a general framework for systematic optimization of domain-specific applications. Specifically, we are developing an optimization system for signal and image processing that exploits signal properties, and we are using machine learning and a knowledge-rich representation can be exploited to optimize molecular dynamics simulation

关键词： Tuning Libraries Signal processing Programming profession Optimizing compilers Registers parallel processing Focusing Computer architecture Sun

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A Novel Associative Model of Data: Toward a distributed Large-Scale Data processing Scheme for Future Computer Clouds

A Novel Associative Model of Data: Toward a Distributed Larg...

引用

ieee international symposium on Network Computing and applications

作者： Amir H. Basirat Asad I. Khan Clayton School of IT Monash University Melbourne Australia

Existing cloud frameworks involve isolating low-level operations within an application for data distribution and partitioning. this limits their applicability for many applications with complex data dependency considerations. this paper aims to explore new methods of partitioning and distributing data in the cloud by fundamentally re-thinking the way in which future data management models will need to be developed on the Internet. Loosely-coupled associative computing techniques, which have so far not been considered, can provide the break-through needed for a distributed data management scheme. Using a novel lightweight associative memory algorithm known as Edge Detecting Hierarchical Graph Neuron (Edge HGN), data retrieval/processing can be modeled as a pattern recognition problem, conducted across multiple records within a single-cycle, utilizing a parallel approach. the proposed model envisions a distributed data management scheme for large-scale data processing and database updating that is capable of providing scalable real-time recognition and processing with high accuracy while being able to maintain low computational cost in its function.

关键词： Pattern recognition Image edge detection Neurons distributed databases Arrays Accuracy Associative memory

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Introducing an Intelligent MapReduce Framework for distributed Data processing in Clouds

Introducing an Intelligent MapReduce Framework for Distribut...

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ieee international symposium on Network Computing and applications

作者： Amir H. Basirat Asad I. Khan Clayton School of IT Monash University Melbourne Australia

ISBN: (纸本)9781479906055

the world of big data is in need of high levels of scalability and the question, how to effectively process large-scale data sets is becoming increasingly relevant. Furthermore the existing data management schemes do not work well when data is partitioned among numerous available nodes dynamically. Approaches towards parallel data processing in cloud, which offer greater portability, manageability and compatibility of applications and data, are yet to be fully-explored. With this in mind, in this paper we would like to explore the possibility to evolve a new type of data processing approach that will efficiently partition and distribute data for clouds. For this matter, loosely-coupled associative techniques, not considered so far, can be the key to effectively partitioning and distributing data in the clouds. Ability to partition data optimally and automatically will allow elastic scaling of system resources and remove one of the main obstacles in provisioning data centric software-as-a-service (SaaS).

关键词： Pattern recognition Neurons Image edge detection distributed databases Associative memory Scalability Data models

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parallel user interfaces for parallel applications

Parallel user interfaces for parallel applications

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international symposium on High Performance distributed Computing

作者： M.T. Vandevoorde D. Kapur Laboratory for Computer Science Massachusetts Institute of Technology Cambridge MA USA Computer Science Department State University of New York Albany NY USA

Many parallel applications are designed to conceal parallelism from the user. We investigate a different approach where the user controls many tasks running in parallel. the idea is to let a user accomplish his goal more quickly by trying competing alternatives in parallel (or-parallelism) and by working on subgoals in parallel (and-parallelism). To help the user manage a large number of parallel tasks, the application must provide features to generate many tasks easily, to summarize the state of all tasks, to broadcast commands to related tasks, and to abort tasks that are no longer needed. A parallel interface to an application thus becomes crucial to enhance the user's productivity. We demonstrate this approach using DLP, a parallel, distributed version of the Larch Prover, an interactive theorem prover. DLP supports explicit parallelism and runs on a network of workstations. Users control DLP through a multi-window interface on a bit-map color-display. Many theorem proving problems that would otherwise take considerable user effort to solve have been done with relative ease using DLP.

关键词： User interfaces parallel processing Productivity Application software Computer science Laboratories Broadcasting Workstations Displays Contracts

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Generalizing Run-Time Tiling with the Loop Chain Abstraction

Generalizing Run-Time Tiling with the Loop Chain Abstraction

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international symposium on parallel and distributed processing (IPDPS)

作者： Michelle Mills Strout Fabio Luporini Christopher D. Krieger Carlo Bertolli Gheorghe-Teodor Bercea Catherine Olschanowsky J. Ramanujam Paul H.J. Kelly Colorado State University Imperial College London IBM T.J. Watson Research Imperial College London London London GB Louisiana State University

ISBN: (纸本)9781479938018

Many scientific applications are organized in a data parallel way: as sequences of parallel and/or reduction loops. this exposes parallelism well, but does not convert data reuse between loops into data locality. this paper focuses on this issue in parallel loops whose loop-to-loop dependence structure is data-dependent due to indirect references such as A[B[i]]. Such references are a common occurrence in sparse matrix computations, molecular dynamics simulations, and unstructured-mesh computational fluid dynamics (CFD). Previously, sparse tiling approaches were developed for individual benchmarks to group iterations across such loops to improve data locality. these approaches were shown to benefit applications such as moldyn, Gauss-Seidel, and the sparse matrix powers kernel, however the run-time routines for performing sparse tiling were hand coded per application. In this paper, we present a generalized full sparse tiling algorithm that uses the newly developed loop chain abstraction as input, improves inter-loop data locality, and creates a task graph to expose shared-memory parallelism at runtime. We evaluate the overhead and performance impact of the generalized full sparse tiling algorithm on two codes: a sparse Jacobi iterative solver and the Airfoil CFD benchmark.

关键词： Arrays parallel processing Sparse matrices Indexes Jacobian matrices Benchmark testing

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On improving performance and energy profiles of sparse scientific applications 06

On improving performance and energy profiles of sparse scien...

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international symposium on parallel and distributed processing (IPDPS)

作者： K. Malkowski Ingyu Lee P. Raghavan M.J. Irwin Department of Computer Science and Engineering Pennsylvania State University University Park PA USA

ISBN: (纸本)9781424400546

In many scientific applications, the majority of the execution time is spent within a few basic sparse kernels such as sparse matrix vector multiplication (SMV). Such sparse kernels can utilize only a fraction of the available processing speed because of their relatively large number of data accesses per floating point operation, and limited data locality and data re-use. Algorithmic changes and tuning of codes through blocking and loop unrolling schemes can improve performance but such tuned versions are typically not available in benchmark suites such as the SPEC CFP 2000. In this paper, we consider sparse SMV kernels with different levels of tuning that are representative of this application space. We emulate certain memory subsystem optimizations using SimpleScalar and Wattch to evaluate improvements in performance and energy metrics. We also characterize how such an evaluation can be affected by the interplay between code tuning and memory subsystem optimizations. Our results indicate that the optimizations reduce execution time by over 40%, and the energy by over 85%, when used with power control modes of CPUs and caches. Furthermore, the relative impact of the same set of memory subsystem optimizations can vary significantly depending on the level of code tuning. Consequently, it may be appropriate to augment traditional benchmarks by tuned kernels typical of high performance sparse scientific codes to enable comprehensive evaluations of future systems.

关键词： Kernel Sparse matrices Application software Computer architecture Computational modeling Power control Scientific computing Microprocessors Computer science Power engineering and energy

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Adapting Particle Filter Algorithms to Many-Core Architectures

Adapting Particle Filter Algorithms to Many-Core Architectur...

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international symposium on parallel and distributed processing (IPDPS)

作者： Mehdi Chitchian Alexander S. van Amesfoort Andrea Simonetto Tamás Keviczky Henk J. Sips Parallel and Distributed Systems Group Delft University of Technology Delft Netherlands Delft Center for Systems and Control Delft University of Technology Delft Netherlands

ISBN: (纸本)9781467360661

the particle filter is a Bayesian estimation technique based on Monte Carlo simulation. It is ideal for non-linear, nonGaussian dynamical systems with applications in many areas, such as computer vision, robotics, and econometrics. Practical use has so far been limited, because of steep computational requirements. In this study, we investigate how to design a particle filter framework for complex estimation problems using many-core architectures. We develop a robotic arm application as a highly flexible estimation problem to push estimation rates and accuracy to new levels. By varying filtering and model parameters, we evaluate our particle filter extensively and derive rules of thumb for good configurations. Using our robotic arm application, we achieve a few hundred state estimations per second with one million particles. With our framework, we make a significant step towards a wider adoption of particle filters and enable studies into filtering setups for even larger estimation problems.

关键词： Estimation Accuracy Graphics processing units Atmospheric measurements Particle measurements Hardware Particle filters

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Fast deterministic sorting on large parallel machines

Fast deterministic sorting on large parallel machines

引用

international symposium on parallel and distributed processing (IPDPS)

作者： T. Dachraoui L. Narayanan Department of Computer Science Concordia University Montreal QUE Canada

Many sorting algorithms that perform well on uniformly distributed data suffer significant performance degradation on non-random data. Unfortunately many real-world applications require sorting on data that is not uniformly distributed. In this paper we consider distributions of varying entropies. We describe A-Ranksort, a new sorting algorithm for parallel machines, whose behavior on input distributions of different entropies is relatively stable. Our algorithm is based on a deterministic strategy to find approximate ranks for all keys. We implemented A-Ranksort, B-Flashsort, Radixsort, and Bitonic sort on a 2048 processor Maspar MP-1. Our experiments show that A-Ranksort out-performs all the other algorithms on a variety of input distributions, when the output is required to be balanced. We are also able to provide bounds on the average-case and worst-case complexities of our algorithm, in terms of the costs of some chosen primitive operations. the predicted performance is very close to the empirical results, thus justifying our model.

关键词： Sorting parallel machines Algorithm design and analysis Computer science Application software Entropy Costs Predictive models Concurrent computing Topology

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A compiler-enabled model- and measurement-driven adaptation environment for dependability and performance

A compiler-enabled model- and measurement-driven adaptation ...

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international symposium on parallel and distributed processing (IPDPS)

作者： V.S. Adve A. Agbaria M.A. Hiltunen R.K. Iyer K.R. Joshi Z. Kalbarczyk R.M. Lefever R. Plante W.H. Sanders R.D. Schlichting University of Illinois Urbana-Champaign Urbana-Champaign IL USA AT and T Research Laboratories Florham Park NJ USA

Traditional techniques for building dependable, high-performance distributed systems are too expensive for most non-critical systems, often causing dependability to be sidelined as a design goal. Nevertheless, systems are expected to be dependable, and if dependability could be provided at a lower cost, many applications would stand to benefit. We believe that compiler techniques can be used to create novel and enhance existing dependability mechanisms to create a wider range of cost/dependability tradeoffs than is currently available. Similarly, compilers can assist in the area of error detection by expanding the range of errors that can be detected. New compiler techniques, combined with model-driven adaptation and control mechanisms, can be used to dynamically guide a system as it makes choices, with cost, dependability, and performance tradeoffs, in response to the occurrence of faults and changes in the environment. this paper reports on a new project that is exploring the approach. the broad goal of the work is to create a powerful yet flexible runtime environment for dependable and high-performance systems that operate within much lower cost constraints than is currently possible.

关键词： Adaptation model Costs Military computing distributed computing Grid computing Runtime environment Computer applications Predictive models Area measurement Buildings

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Ad-hoc distributed spatial joins on mobile devices 06

Ad-hoc distributed spatial joins on mobile devices

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international symposium on parallel and distributed processing (IPDPS)

作者： P. Kalnis N. Mamoulis S. Bakiras Xiaochen Li Department of Computer Science National University of Singapore Singapore Department of Computer Science University of Hong Kong Hong Kong China Department of Computer Science Hong Kong University of Science and Technology Hong Kong China

ISBN: (纸本)9781424400546

PDAs, cellular phones and other mobile devices are now capable of supporting complex data manipulation operations. Here, we focus on ad-hoc spatial joins of datasets residing in multiple non-cooperative servers. Assuming that there is no mediator available, the spatial joins must be evaluated on the mobile device. Contrary to common applications that consider the cost at the server side, our main issue is the minimization of the transferred data, while meeting the resource constraints of the device. We show that existing methods, based on partitioning and pruning, are inadequate in many realistic situations. then, we present novel algorithms that estimate the data distribution before deciding the physical operator independently for each partition. Our experiments with a prototype implementation on a WiFi-enabled PDA, suggest that the proposed methods outperform the competitors in terms of efficiency and applicability

关键词： Personal digital assistants Costs Computer science Spatial databases Cellular phones Mobile computing Partitioning algorithms Prototypes Mobile handsets Navigation

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