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

作者： Song, Huaiming Jin, Hui He, Jun Sun, Xian-He Thakur, Rajeev Dawning Informat Ind Ctr Res & Dev Beijing 100193 Peoples R China IIT Dept Comp Sci Chicago IL 60616 USA Argonne Natl Lab Math & Comp Sci Div 9700 S Cass Ave Argonne IL 60439 USA

ISBN: (纸本)9780769546766

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： Server-level adaptive data layout variable stripe size equal-depth histogram data layout optimization parallel file system

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Towards High-Level Programming of Multi-GPU Systems Using the SkelCL Library

Towards High-Level Programming of Multi-GPU Systems Using th...

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26th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Steuwer, Michel Kegel, Philipp Gorlatch, Sergei Univ Munster Dept Math & Comp Sci Munster Germany

ISBN: (纸本)9780769546766

Application programming for GPUs (Graphics Processing Units) is complex and error-prone, because the popular approaches - CUDA and OpenCL - are intrinsically low-level and offer no special support for systems consisting of multiple GPUs. The SkelCL library presented in this paper is built on top of the OpenCL standard and offers pre-implemented recurring computation and communication patterns (skeletons) which greatly simplify programming for multi-GPU systems. The library also provides an abstract vector data type and a high-level data (re) distribution mechanism to shield the programmer from the low-level data transfers between the system's main memory and multiple GPUs. In this paper, we focus on the specific support in SkelCL for systems with multiple GPUs and use a real-world application study from the area of medical imaging to demonstrate the reduced programming effort and competitive performance of SkelCL as compared to OpenCL and CUDA. Besides, we illustrate how SkelCL adapts to large-scale, distributed heterogeneous systems in order to simplify their programming.

关键词： GPU computing GPU Programming Multi-GPU Systems SkelCL OpenCL Algorithmic Skeletons

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Improved Bounds for Discrete Diffusive Load Balancing

Improved Bounds for Discrete Diffusive Load Balancing

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

作者： Adolphs, Clemens P. J. Berenbrink, Petra Rhein Westfal TH Aachen Lehrstuhl Informat 1 Aachen Germany Simon Fraser Univ Dept Comp Sci Burnaby BC V5A 1S6 Canada

ISBN: (纸本)9780769546759

In this paper we consider load balancing in a static and discrete setting where a fixed number of indivisible tasks have to be allocated to processors. We assume uniform tasks but the processors may have different speeds. The load of a processor is the number of tasks assigned to it divided by its speed. We consider diffusion load balancing which works in rounds. In every round the processors are allowed to compare their own load with the load of their neighbors and to balance the load with the neighbors, using their local information only. The question is how many rounds does it take until the whole processor network is balanced, meaning the load discrepancy (difference between maximum load and m/n) is minimized. Our balancing algorithm is deterministic and extends the algorithm studied in [1] from the case of uniform speeds to non-uniform speeds. We use a potential function argument to show that a better load balance can be obtained when the algorithm is allowed to run longer compared to the algorithm of [1].

关键词： load balancing distributed algorithms reallocation

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Hierarchical QR factorization algorithms for multi-core cluster systems

Hierarchical QR factorization algorithms for multi-core clus...

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

作者： Dongarra, Jack Faverge, Mathieu Herault, Thomas Langou, Julien Robert, Yves Univ Tennessee Knoxville Knoxville TN 37996 USA Oak Ridge Natl Lab Oak Ridge TN USA Univ Manchester Manchester M13 9PL Lancs England Univ Colorado Denver CO USA Ecole Normale Superieure Lyon Lyon France

ISBN: (纸本)9780769546759

This paper describes a new QR factorization algorithm which is especially designed for massively parallel platforms combining parallel distributed multi-core nodes. These platforms make the present and the foreseeable future of high-performance computing. Our new QR factorization algorithm falls in the category of the tile algorithms which naturally enables good data locality for the sequential kernels executed by the cores (high sequential performance), low number of messages in a parallel distributed setting (small latency term), and fine granularity (high parallelism). Each tile algorithm is uniquely characterized by its sequence of reduction trees. In the context of a cluster of multicores, in order to minimize the number of inter-processor communications (aka, "communication-avoiding" algorithm), it is natural to consider two-level hierarchical trees composed of an "inter-node" tree which acts on top of "intra-node" trees. At the intra-node level, we propose a hierarchical tree made of three levels: (0) "TS level" for cache-friendliness, (1) "low level" for decoupled highly parallel inter-node reductions, (2) "coupling level" to efficiently resolve interactions between local reductions and global reductions. Our hierarchical algorithm and its implementation are flexible and modular, and can accommodate several kernel types, different distribution layouts, and a variety of reduction trees at all levels, both inter-cluster and intra-cluster. Numerical experiments on a cluster of multicore nodes (1) confirm that each of the four levels of our hierarchical tree contributes to build up performance and (2) build insights on how these levels influence performance and interact within each other. Our implementation of the new algorithm with the DAGUE scheduling tool significantly outperforms currently available QR factorization softwares for all matrix shapes, thereby bringing a new advance in numerical linear algebra for petascale and exascale platforms.

关键词： QR factorization numerical linear algebra hierarchical architecture distributed memory cluster multicore

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Tight approximation for scheduling parallel jobs on identical clusters

Tight approximation for scheduling parallel jobs on identica...

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

作者： Bougeret, Marin Dutot, Pierre-Francois Jansen, Klaus Robenek, Christina Trystram, Denis LIRMM Montpellier France Univ Kiel Dept Comp Sci Kiel Germany Grenoble Univ Grenoble France Inst Univ France Paris France

ISBN: (纸本)9780769546766

We consider the Multiple Cluster Scheduling Problem (MCSP), where the objective is to schedule n parallel rigid jobs on N identical clusters, minimizing the maximum completion time (makespan). MCSP is 2-inapproximable (unless P = NP), and several approximation algorithms have already been proposed. However, ratio 2 has only been reached by algorithms that use extremely costly and complex subroutines as "black boxes" which are polynomial and yet impractical due to prohibitive constants. Our objective within this work is to determine a reasonable restriction of MCSP where the inapproximability lower bound could be tightened in almost linear time. Thus, we consider a restriction of MCSP where jobs do not require strictly more than half of the processors of a cluster, and we provide a 2-approximation running in O(log(nh(max))n(N + log(n))), where h(max) is the processing time of the longest job. This approximation is the best possible, as this restriction (and even simpler ones) remains 2-inapproximable.

关键词： jobs Completion time Approximation algorithms Restriction processing time PROCESSOR approximation Identical Polynomial parallel Lines Clusters

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A parallel BP algorithm for the Discretizable Distance Geometry Problem

A parallel BP algorithm for the Discretizable Distance Geome...

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

作者： Gramacho, W. Mucherino, A. Lavor, C. Maculan, N. Fed Univ Tocantins Palmas TO Brazil Univ Rennes 1 IRISA Rennes France Univ Estadual Campinas IMECC Campinas Brazil Univ Fed Rio de Janeiro COPPE Rio De Janeiro Brazil

ISBN: (纸本)9780769546766

We propose a parallel version of the Branch & Prune (BP) algorithm for the Discretizable Distance Geometry Problem (DDGP), which consists in a subclass of Distance Geometry Problems (DGPs) that can be discretized. The main idea is to split a DDGP instance in as many subinstances as the number of processors involved in the computation, and to invoke the sequential version of BP on each processor. Due to the flexibility of the discretizing orderings that can be defined on the vertex sets of graphs.. representing DDGP instances, the subdivision of the original instance can be performed so that all solutions generated by locally solving the several subinstances are represented in a common coordinate system. This way, the communication phase of the parallel algorithm, where the local solutions are combined in order to generate the final set of solutions, is very efficient. We present some preliminary computational experiments and we study the behavior of the algorithm in relation to the number of considered processors. We also give some directions for transforming DDGP instances in parallelizable instances, and to modify them in order to improve the efficiency of the proposed parallel algorithm.

关键词： parallel algorithms PROCESSOR final sets geometry Coordinate system parallel Lines algorithms Vertex sets

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The Development of parallel Adaptive Sampling Algorithms for Analyzing Biological Networks

The Development of Parallel Adaptive Sampling Algorithms for...

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

作者： Dempsey, Kathryn Duraisamy, Kanimathi Bhowmick, Sanjukta Ali, Hesham Univ Nebraska Ctr Med Dept Pathol & Microbiol Omaha NE 68182 USA Univ Nebraska Coll Informat Sci & Technol Omaha NE 68182 USA

ISBN: (纸本)9780769546766

The availability of biological data in massive scales continues to represent unlimited opportunities as well as great challenges in bioinformatics research. Developing innovative data mining techniques and efficient parallel computational methods to implement them will be crucial in extracting useful knowledge from this raw unprocessed data, such as in discovering significant cellular subsystems from gene correlation networks. In this paper, we present a scalable combinatorial sampling technique, based on identifying maximum chordal subgraphs, that reduces noise from biological correlation networks, thereby making it possible to find biologically relevant clusters from the filtered network. We show how selecting the appropriate filter is crucial in maintaining the key structures from the original networks and uncovering new ones after removing noisy relationships. We also conduct one of the first comparisons in two important sensitivity criteria-the perturbation due to the vertex numbers of the network and perturbations due to data distribution. We demonstrate that our chordal-graph based filter is effective across many different vertex permutations, as is our parallel implementation of the sampling algorithm.

关键词： chordal graphs ordering correlation networks edge enrichment cluster overlap

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Optimizing Busy Time on parallel Machines

Optimizing Busy Time on Parallel Machines

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

作者： Mertzios, George B. Shalom, Mordechai Voloshin, Ariella Wong, Prudence W. H. Zaks, Shmuel Univ Durham Sch Engn & Comp Sci Durham England TelHai Coll Upper Galilee 12210 Israel Dept Comp Sci Haifa Israel Univ Liverpool Dept Comp Sci Liverpool Merseyside England

ISBN: (纸本)9780769546759

We consider the following fundamental scheduling problem in which the input consists of n jobs to be scheduled on a set of identical machines of bounded capacity g (which is the maximal number of jobs that can be processed simultaneously by a single machine). Each job is associated with a start time and a completion time;it is supposed to be processed from the start time to the completion time (and in one of our extensions it has to be scheduled also in a continuous number of days;this corresponds to a two-dimensional version of the problem). We consider two versions of the problem. In the scheduling minimization version the goal is to minimize the total busy time of machines used to schedule all jobs. In the resource allocation maximization version the goal is to maximize the number of jobs that are scheduled for processing under a budget constraint given in terms of busy time. This is the first study of the maximization version of the problem. The minimization problem is known to be NP-Hard, thus the maximization problem is also NP-Hard. We consider various special cases, identify cases where an optimal solution can be computed in polynomial time, and mainly provide constant factor approximation algorithms for both minimization and maximization problems. Some of our results improve upon the best known results for this job scheduling problem. Our study has applications in power consumption, cloud computing and optimizing switching cost of optical networks.

关键词： Interval scheduling busy time resource allocation approximation algorithms

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ExPERT: Pareto-Efficient Task Replication on Grids and a Cloud

ExPERT: Pareto-Efficient Task Replication on Grids and a Clo...

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

作者： Ben-Yehuda, Orna Agmon Schuster, Assaf Sharov, Artyom Silberstein, Mark Iosup, Alexandru Technion Israel Inst Technol IL-32000 Haifa Israel Delft Univ Technol Parallel & Distributed Syst Grp Delft Netherlands

ISBN: (纸本)9780769546759

Many scientists perform extensive computations by executing large bags of similar tasks (BoTs) in mixtures of computational environments, such as grids and clouds. Although the reliability and cost may vary considerably across these environments, no tool exists to assist scientists in the selection of environments that can both fulfill deadlines and fit budgets. To address this situation, we introduce the ExPERT BoT scheduling framework. Our framework systematically selects from a large search space the Pareto-efficient scheduling strategies, that is, the strategies that deliver the best results for both makespan and cost. ExPERT chooses from them the best strategy according to a general, user-specified utility function. Through simulations and experiments in real production environments, we demonstrate that ExPERT can substantially reduce both makespan and cost in comparison to common scheduling strategies. For bioinformatics BoTs executed in a real mixed grid+cloud environment, we show how the scheduling strategy selected by ExPERT reduces both makespan and cost by 30%-70%, in comparison to commonly-used scheduling strategies.

关键词： bags-of-tasks cloud grid Pareto-frontier

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LDPLFS: Improving I/O Performance Without Application Modification

LDPLFS: Improving I/O Performance Without Application Modifi...

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

作者： Wright, S. A. Hammond, S. D. Pennycook, S. J. Miller, I. Herdman, J. A. Jarvis, S. A. Univ Warwick Dept Comp Sci Coventry CV4 7AL W Midlands England Sandia Natl Labs CSRI Scalable Comp Architectures Albuquerque NM 87185 USA UK Atom Weapons Estab Supercomp Solut Ctr Aldermaston England

ISBN: (纸本)9780769546766

Input/Output (I/O) operations can represent a significant proportion of run-time when large scientific applications are run in parallel and at scale. In order to address the growing divergence between processing speeds and I/O performance, the parallel Log-structured File System (PLFS) has been developed by EMC Corporation and the Los Alamos National Laboratory (LANL) to improve the performance of parallel file activities. Currently, PLFS requires the use of either (i) the FUSE Linux Kernel module;(ii) a modified MPI library with a customised ROMIO MPI-IO library;or (iii) an application rewrite to utilise the PLFS API directly. In this paper we present an alternative method of utilising PLFS in applications. This method employs a dynamic library to intercept the low-level POSIX operations and retarget them to use the equivalents offered by PLFS. We demonstrate our implementation of this approach, named LDPLFS, on a set of standard UNIX tools, as well on as a set of standard parallel I/O intensive mini-applications. The results demonstrate almost equivalent performance to a modified build of ROMIO and improvements over the FUSE-based approach. Furthermore, through our experiments we demonstrate decreased performance in PLFS when ran at scale on the Lustre file system.

关键词： Data Storage Systems File Systems High Performance computing I/O

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