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检索条件"主题词=Dynamic loop scheduling"

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dynamic loop scheduling Using MPI Passive-Target Remote Memory Access 27

Dynamic Loop Scheduling Using MPI Passive-Target Remote Memo...

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27th Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP)

作者： Eleliemy, Ahmed Ciorba, Florina M. Univ Basel Dept Math & Comp Sci Basel Switzerland

ISBN: (纸本)9781728116440

Scientific applications often contain large computationally-intensive parallel loops. loop scheduling techniques aim to achieve load balanced executions of such applications. For distributed-memory systems, existing dynamic loop scheduling (DLS) libraries are typically MPI-based, and employ a master-worker execution model to assign variably-sized chunks of loop iterations. The master-worker execution model may adversely impact performance due to the master-level contention. This work proposes a distributed chunk-calculation approach that does not require the master-worker execution scheme. Moreover, it considers the novel features in the latest MPI standards, such as passive-target remote memory access, shared-memory window creation, and atomic read-modify-write operations. To evaluate the proposed approach, five well-known DLS techniques, two applications, and two heterogeneous hardware setups have been considered. The DLS techniques implemented using the proposed approach outperformed their counterparts implemented using the traditional master-worker execution model.

关键词： dynamic loop scheduling Distributed-memory systems Master-worker execution model MPI Passive-target remote memory access

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Performance Reproduction and Prediction of Selected dynamic loop scheduling Experiments 16

Performance Reproduction and Prediction of Selected Dynamic ...

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International Conference on High Performance Computing & Simulation (HPCS)

作者： Mohammed, Ali Eleliemy, Ahmed Ciorba, Florina M. Univ Basel Dept Math & Comp Sci Basel Switzerland

ISBN: (纸本)9781538678794

Scientific applications are complex, large, and often exhibit irregular and stochastic behavior. The use of efficient loop scheduling techniques, from static to fully dynamic, in computationally-intensive applications is crucial for improving their performance, often degraded by load imbalance, on highperformance computing (HPC) platforms. A number of dynamic loop scheduling (DLS) techniques have been proposed between the late 1980' s and early 2000' s, and efficiently used in scientific applications. In most cases, the computing systems on which they have been tested and validated are no longer available. This work is concerned with the minimization of the sources of uncertainty in the implementation of DLS techniques to avoid unnecessary influences on the performance of scientific applications. Therefore, it is important to ensure that the DLS techniques employed in scientific applications today adhere to their original design goals and specifications. The goal of this work is to attain and increase the trust in the implementation of DLS techniques in today's studies. To achieve this goal, the performance of a selection of scheduling experiments from the 1992 original work that introduced factoring is reproduced and predicted via both, simulative and native experimentation. The scientific challenge is the reproduction of the performance of the past experiments with incomplete information, such as the computing system characteristics and the implementation details. The experiments show that the simulation reproduces the performance achieved on the past computing platform and accurately predicts the performance achieved on the present computing platform. The performance reproduction and prediction confirm that the present implementation of the DLS techniques considered both, in simulation and natively, adheres to their original description. The results confirm the hypothesis that reproducing experiments of identical scheduling scenarios on past and modern hardware l

关键词： dynamic loop scheduling performance reproduction performance prediction simulation native experimentation shared-memory manycore architecture

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Examining the Reproducibility of Using dynamic loop scheduling Techniques in Scientific Applications 31

Examining the Reproducibility of Using Dynamic Loop Scheduli...

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31st IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPS)

作者： Hoffeins, Franziska Ciorba, Florina M. Banicescu, Ioana Tech Univ Dresden Ctr Informat Serv & High Performance Comp D-01062 Dresden Germany Univ Basel Dept Math & Comp Sci CH-4051 Basel Switzerland Mississippi State Univ Dept Comp Sci & Engn Mississippi State MS 39762 USA

ISBN: (纸本)9780769561493

Reproducibility of the execution of scientific applications on parallel and distributed systems is a growing concern, underlying the trustworthiness of the experiments and the conclusions derived from experiments. dynamic loop scheduling (DLS) techniques are an effective approach towards performance improvement of scientific applications via load balancing. These techniques address algorithmic and systemic sources of load imbalance by dynamically assigning tasks to processing elements. The DLS techniques have demonstrated their effectiveness when applied in real applications. Complementing native experiments, simulation is a powerful tool for studying the behavior of parallel and distributed applications. In earlier work, the scalability [1], robustness [2], and resilience [3] of the DLS techniques were investigated using the MSG interface of the SimGrid simulation framework [4]. The present work complements the earlier work and concentrates on the verification via reproducibility of the implementation of the DLS techniques in SimGrid-MSG. This work describes the challenges of verifying the performance of using DLS techniques in earlier implementations of scientific applications. The verification is performed via reproducibility of simulations based on SimGrid-MSG. To simulate experiments selected from earlier literature, the reproduction process begins by extracting the information needed from the earlier literature and converting it into the input required by SimGrid- MSG. The reproducibility study is carried out by comparing the performance of SimGrid-MSG-based experiments with those reported in two selected publications in which the DLS techniques were originally proposed. While the reproduction was not successful for experiments from one of the selected publications, it was successful for experiments from the other. This successful reproduction implies the verification of the DLS implementation in SimGrid- MSG for the considered applications and systems, and th

关键词： Verification via Reproducibility Simulation scheduling dynamic loop scheduling Scientific Applications SimGrid-MSG

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Experimental Verification and Analysis of dynamic loop scheduling in Scientific Applications 17

Experimental Verification and Analysis of Dynamic Loop Sched...

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17th Annual International Symposium on Parallel and Distributed Computing (ISPDC)

作者： Mohammed, Ali Eleliemy, Ahmed Ciorba, Florina M. Kasielke, Franziska Banicescu, Ioana Univ Basel Dept Math & Comp Sci Basel Switzerland Tech Univ Dresden Ctr Informat Serv & High Performance Comp Dresden Germany Mississippi State Univ Dept Comp Sci & Engn Starkville MS USA

ISBN: (纸本)9781538653302

Scientific applications are often irregular and characterized by large computationally-intensive parallel loops. dynamic loop scheduling (DLS) techniques improve the performance of computationally-intensive scientific applications via load balancing of their execution on high-performance computing (HPC) systems. Identifying the most suitable choices of data distribution strategies, system sizes, and DLS techniques which improve the performance of a given application, requires intensive assessment and a large number of exploratory native experiments (using real applications on real systems), which may not always be feasible or practical due to associated time and costs. In such cases, simulative experiments are more appropriate for studying the performance of applications. This motivates the question of 'How realistic are the simulations of executions of scientific applications using DLS on HPC platforms?' In the present work, a methodology is devised to answer this question. It involves the experimental verification and analysis of the performance of DLS in scientific applications. The proposed methodology is employed for a computer vision application executing using four DLS techniques on two different HPC platforms, both via native and simulative experiments. The evaluation and analysis of the native and simulative results indicate that the accuracy of the simulative experiments is strongly influenced by the approach used to extract the computational effort of the application (FLOP- or time-based), the choice of application model representation into simulation (data or task parallel), and the available HPC subsystem models in the simulator (multi-core CPUs, memory hierarchy, and network topology). The minimum and the maximum percent errors achieved between the native and the simulative experiments are 0.95% and 8.03%, respectively.

关键词： dynamic loop scheduling Self-scheduling Parallel spin-image Simulation Performance MSG SimDag

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Communication-Computation Overlapping with dynamic loop scheduling for Preconditioned Parallel Iterative Solvers on Multicore and Manycore Clusters 46

Communication-Computation Overlapping with Dynamic Loop Sche...

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46th International Conference on Parallel Processing Workshops (ICPPW)

作者： Nakajima, Kengo Hanawa, Toshihiro Univ Tokyo Informat Technol Ctr Bunkyo Ku 2-11-16 Yayoi Tokyo 1138658 Japan

ISBN: (纸本)9781538610442

Preconditioned parallel solvers based on the Krylov iterative method are widely used in scientific and engineering applications. Communication overhead is a critical issue when executing these solvers on large-scale massively parallel supercomputers. In this work, we introduced communication-computation (CC) overlapping with dynamic loop scheduling of OpenMP to the sparse matrix-vector multiplication (SpMV) process of a parallel iterative solver. We then used the solver to evaluate the performance of a parallel finite element application (GeoFEM/Cube) on multicore and manycore clusters. The dynamic loop scheduling of OpenMP improved the efficiency of CC overlapping in halo exchanges, and the developed method attained a significant performance improvement of 40-50% for parallel iterative solvers in strong scaling using up to 16,384 cores of a Fujitsu PRIMEHPC FX10 supercomputer and an Intel Xeon Phi (KNL) cluster. Finally, the developed method was applied to GeoFEM/Cube using a parallel BiCGSTAB solver with sparse approximate inverse (SAI) preconditioning, and a 15-20% performance improvement was obtained on 12,288 cores of the Fujitsu FX10 and the KNL cluster.

关键词： parallel computing iterative solvers hybrid parallel programming model communication-computation overlapping dynamic loop scheduling

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Efficient Generation of Parallel Spin-images Using dynamic loop scheduling

Efficient Generation of Parallel Spin-images Using Dynamic L...

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IEEE 19th International Conference on High Performance Computing and Communications (HPCC)

作者： Eleliemy, Ahmed Mohammed, Ali Ciorba, Florina M. Univ Basel Dept Math & Comp Sci Basel Switzerland

ISBN: (纸本)9781538659304

High performance computing (HPC) systems underwent a significant increase in their processing capabilities. Modern HPC systems combine large numbers of homogeneous and heterogeneous computing resources. Scalability is, therefore, an essential aspect of scientific applications to efficiently exploit the massive parallelism of modern HPC systems. This work introduces an efficient version of the parallel spin-image algorithm (PSIA), called EPSIA. The PSIA is a parallel version of the spin-image algorithm (SIA). The (P)SIA is used in various domains, such as 3D object recognition, categorization, and 3D face recognition. EPSIA refers to the extended version of the PSIA that integrates various well-known dynamic loop scheduling (DLS) techniques. The present work: (1) Proposes EPSIA, a novel flexible version of PSIA;(2) Showcases the benefits of applying DLS techniques for optimizing the performance of the PSIA;(3) Assesses the performance of the proposed EPSIA by conducting several scalability experiments. The performance results are promising and show that using well-known DLS techniques, the performance of the EPSIA outperforms the performance of the PSIA by a factor of 1.2 and 2 for homogeneous and heterogeneous computing resources, respectively.

关键词： Spin-image algorithm Static loop scheduling dynamic loop scheduling Self scheduling Guided self scheduling Factoring Efficient performance

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Investigating the Resilience of dynamic loop scheduling in Heterogeneous Computing Systems 14

Investigating the Resilience of Dynamic Loop Scheduling in H...

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IEEE 14th International Symposium on Parallel and Distributed Computing (ISPDC)

作者： Sukhija, Nitin Banicescu, Ioana Ciorba, Florina M. Mississippi State Univ Dept Comp Sci & Engn Mississippi State MS 39762 USA Tech Univ Dresden Ctr Informat Serv & High Performance Comp D-01062 Dresden Germany

ISBN: (纸本)9781467371483

To improve the performance of complex scientific applications, dynamic loop scheduling (DLS) techniques are often employed for load balancing. However, it is a challenge to select the most resilient scheduling technique for guaranteeing optimized performance of scientific applications on large-scale computing systems. Such systems comprise widely distributed and highly heterogeneous resources, and often are prone to failures. Hence, in this work we perform a comprehensive study of resilience of DLS techniques. In our study, we employed SimGrid-based simulations. The use of a simulation framework assists in overcoming the limits of quantifying the resilience and evaluating the performance of the DLS techniques on real test beds by allowing us to model, control, and reproduce large-scale computing systems with irregular behavior in order to analyze the resilience of DLS techniques on computationally intensive scientific applications. The results are used to compare the resilience of scheduling techniques under different case scenarios comprising of variable problem sizes, system sizes, characteristics of the variations in the application task computation times, and those of the processor availabilities and failures.

关键词： dynamic loop scheduling resilience SimGrid

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Towards the Scalability of dynamic loop scheduling Techniques via Discrete Event Simulation

Towards the Scalability of Dynamic Loop Scheduling Technique...

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26th IEEE International Parallel and Distributed Processing Symposium (IPDPS) / Workshop on High Performance Data Intensive Computing

作者： Balasubramaniam, Mahadevan Sukhija, Nitin Ciorba, Florina M. Banicescu, Ioana Srivastava, Srishti Mississippi State Univ Dept Comp Sci & Engn Mississippi State MS 39762 USA Tech Univ Dresden Ctr Informat Serv & High Performance Comp ZIH D-01062 Dresden Germany

ISBN: (纸本)9780769546766

To improve their performance, scientific applications often use loop scheduling algorithms as techniques for load balancing data parallel computations. Over the years, a number of dynamic loop scheduling (DLS) techniques have been developed. These techniques are based on probabilistic analyses, and are effective in addressing unpredictable load imbalances in the system arising from various sources, such as, variations in application, algorithmic, and systemic characteristics. Modern, high-end computing facilities can now offer petascale performance (10(15) flops), and several initiatives have already begun with the goal of achieving exascale performance (10(18) flops) towards the end of the current decade. Efficient and scalable algorithms are therefore required to utilize the petascale and exascale resources. In this paper, a study of the scalability of DLS techniques via discrete event simulation is presented, both in terms of number of processors, and problem size. To facilitate the scalability study, a dynamic loop scheduler was designed and was implemented using the SimGrid [1] simulation framework. The results of the study demonstrate the scalability of the DLS techniques and their effectiveness in addressing load imbalance in large scale computing systems.

关键词： dynamic loop scheduling scalability simgrid

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Portfolio-based Selection of Robust dynamic loop scheduling Algorithms Using Machine Learning 28

Portfolio-based Selection of Robust Dynamic Loop Scheduling ...

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28th IEEE International Parallel & Distributed Processing Symposium Workshops (IPDPSW)

作者： Sukhija, Nitin Malone, Brandon Srivastava, Srishti Banicescu, Ioana Ciorba, Florina M. Mississippi State Univ Dept Comp Sci & Engn Mississippi State MS 39762 USA Univ Helsinki Helsinki Inst Informat Technol Dept Comp Sci Helsinki Finland Tech Univ Dresden Ctr Informat Serv & High Performance Comp D-01062 Dresden Germany

ISBN: (纸本)9781479941162

The execution of computationally intensive parallel applications in heterogeneous environments, where the quality and quantity of computing resources available to a single user continuously change, often leads to irregular behavior, in general due to variations of algorithmic and systemic nature. To improve the performance of scientific applications, loop scheduling algorithms are often employed for load balancing of their parallel loops. However, it is a challenge to select the most robust scheduling algorithms for guaranteeing optimized performance of scientific applications on large-scale computing systems that comprise resources which are widely distributed, highly heterogeneous, often shared among multiple users, and have computing availabilities that cannot always be guaranteed or predicted. To address this challenge, in this work we focus on a portfolio-based approach to enable the dynamic selection and use of the most robust dynamic loop scheduling (DLS) algorithm from a portfolio of DLS algorithms, depending on the given application and current system characteristics including workload conditions. Thus, in this paper we provide a solution to the algorithm selection problem and experimentally evaluate its quality. We propose the use of supervised machine learning techniques to build empirical robustness prediction models that are used to predict DLS algorithm's robustness for given scientific application characteristics and system availabilities. Using simulated scientific applications characteristics and system availabilities, along with empirical robustness prediction models, we show that the proposed portfolio-based approach enables the selection of the most robust DLS algorithm that satisfies a user-specified tolerance on the given application's performance obtained in the particular computing system with a certain variable availability. We also show that the portfolio-based approach offers higher guarantees regarding the robust performance of the applic

关键词： dynamic loop scheduling robustness algorithm selection empirical robustness prediction models machine learning techniques variable system availability

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An Autonomic Approach for the Selection of Robust dynamic loop scheduling Techniques 16

An Autonomic Approach for the Selection of Robust Dynamic Lo...

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16th International Symposium on Parallel and Distributed Computing (ISPDC)

作者： Boulmier, Anthony Banicescu, Ioana Ciorba, Florina M. Abdennadher, Nabil Univ Geneva Dept Comp Sci CH-1227 Carouge Switzerland Mississippi State Univ Dept Comp Sci & Engn Mississippi State MS 39762 USA Univ Basel Dept Math & Comp Sci CH-4051 Basel Switzerland Univ Appl Sci Western Switzerland Dept Comp Sci & Engn CH-1202 Geneva Switzerland

ISBN: (纸本)9781538608623

Parallel applications are highly irregular and high performance computing (HPC) infrastructures are very complex. The HPC applications of interest herein are time-stepping scientific applications (TSSA). Often, TSSA involve the repeated execution of multiple parallel loops with thousands of iterations and irregular behavior. dynamic loop scheduling (DLS) techniques were developed over time and have proven to be effective in scheduling parallel loops for achieving load balancing of TSSA. Using a single particular DLS technique throughout the entire execution of a time-step, or even over the entire application, does not guarantee optimal performance due to the unpredictable variations in problem and algorithmic characteristics as well as those of the infrastructure capabilities. For that reason, an autonomic selection of DLS techniques as function of the parallel loop execution time has shown to improve application performance. Recently, a robustness metric of DLS techniques, named "flexibility", has been proposed to estimate the capability of a DLS technique to resist to variations in the loop iterations execution time. To improve the performance of TSSA, we propose in this work an approach that involves the autonomic selection of DLS techniques as function of the flexibility of DLS techniques. The first major novelty of our approach lies in the use of state-of-the-art reinforcement learning (RL) algorithms as smart agents. The second novelty lies in the design of a modified flexibility metric. The third major novelty resides in using the new modified flexibility metric as a reward for the smart agents. The fourth novelty is the evaluation of the proposed approach within a simulated environment, in particular using the SimGrid-SMPI interface to execute DLS algorithms. We discuss the advantages and the limitations of the new proposed flexibility metric as a reward.

关键词： High Performance Computing Autonomic Computing Reinforcement Learning Performance Optimization Robustness dynamic loop scheduling

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