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GPS: Towards Simplified Communication on SGL Model 28

GPS: Towards Simplified Communication on SGL Model

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28th ieee International parallel & distributed processing symposium Workshops (IPDPSW)

作者： Li, Chong Hains, Gaetan Univ Paris Est Creteil LACL Creteil France Univ Paris 1 Sorbonne CNRS CES Paris France Univ Orleans LIFO INSA Val de Loire Orleans France

ISBN: (纸本)9781479941162

parallel programming and data-parallel algorithms have been the main techniques supporting high-performance computing for many decades. A major conceptual step was taken by L. Valiant who introduced the Bulk-Synchronous parallel (BSP) model. parallel algorithms on BSP can be designed and measured by taking into account not only the classical balance between time and parallel space but also communication and synchronization. Inspired by BSP, the SGL bridging model was proposed in order to improve the simplicity of parallel program development, the portability of parallel program code, and the precision of parallel algorithm performance prediction on both classical parallel machines and novel hierarchical machines. The programming model of SGL replaces the BSML (BSP-Ocaml) programming primitives with scatter, gather and pardo. However, SGL does not express "horizontal" communication patterns. In this paper we introduce the GPS theorem which can be implemented later in a compiler to optimize the SGL's "horizontal" all-to-all communication. We then propose a simplified version of BSML's put based on GPS and implement Tiskin-McColl parallel sample-sort with it. The comparison of BSML's put and SGL's GPS shows that GPS has a better code readability and lower execution time.

关键词： parallel programming simplified communication bridging model GPS theorem SGL BSML

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A Stream processing Framework for On-line Optimization of Performance and Energy Efficiency on Heterogeneous Systems 28

A Stream Processing Framework for On-line Optimization of Pe...

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28th ieee International parallel & distributed processing symposium Workshops (IPDPSW)

作者： Ranft, Benjamin Denninger, Oliver Pfaffe, Philip FZI Res Ctr Informat Technol D-76131 Karlsruhe Germany Karlsruhe Inst Technol D-76131 Karlsruhe Germany

ISBN: (纸本)9781479941162

Modern processors have the potential of executing compute-intensive programs quickly and efficiently, but require applications to be adapted to their ever increasing parallelism. Here, heterogeneous systems add complexity by combining processing units with different characteristics. Scheduling should thus consider the performance of each processor as well as competing workloads and varying inputs. To assist programmers of stream processing applications in facing this challenge we present libHawaii, an open source library for cooperatively using all processors of heterogeneous systems easily and efficiently. It supports exploiting data flow, data element and task parallelism via pipelining, partitioning and demand-based allocation of consecutive work items. Scheduling is automatically adapted on-line to continuously optimize performance and energy efficiency. Our C++ library does not depend on specific hardware architectures or parallel computing frameworks. However, it facilitates maximizing the throughput of compatible GPUs by overlapping computations and memory transfers while maintaining low latencies. This paper describes the algorithms and implementation of libHawaii and demonstrates its usage on existing applications. We experimentally evaluate our library using two examples: General matrix multiplication (GEMM) is a simple yet important building block of many high-performance computing applications. Complementarily, the detection, extraction and matching of sparse image features exhibits greater complexity, including indeterministic memory access and synchronization.

关键词： heterogeneous computing stream processing load balancing energy efficiency real-time parallel programming

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Adaptive N To P Portfolio for Solving Constraint Programming Problems on Top of the parallel Bobpp Framework 28

Adaptive <i>N</i> To <i>P</i> Portfolio for Solving Constrai...

引用

28th ieee International parallel & distributed processing symposium Workshops (IPDPSW)

作者： Menouer, Tarek Le Cun, Bertrand Univ Versailles St Quentin En Yvelines PRISM Lab Versailles France

ISBN: (纸本)9781479941162

This paper presents a parallelization of Constraint Programming (CP) solver, based on the portfolio principle, in order to quickly solve constraint satisfaction and optimisation problems. The portfolio principle is widely used in the parallelization of boolean SATisfiability (SAT) and CP solvers. It is based on the running of N search strategies for the same problem using N computing cores. Each core uses its own strategy in order to perform a search that is different form the other ones. The first strategy that responds to the needs of the user stops all other strategies. In the usual portfolio principle, the number of search strategies is limited compared to the current number of the computing cores used by parallel machines. The idea of this article is to run N search strategies for the same CP problem and schedule these strategies using P computing cores (P > N). The novelty is that the scheduling of these N strategies is dynamically performed between the different computing cores. The goal is to adapt the scheduling of the search strategies so as to favour the strategy that finds a solution quickly. The performances obtained with this adaptive portfolio solver are illustrated by solving the CP problems modeled using FlatZinc format and solved using the OR-Tools solver on top of the parallel Bobpp framework.

关键词： parallelism Dynamic load balancing Combinatorial Optimization scheduling

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ZooFence: Principled Service Partitioning and Application to the ZooKeeper Coordination Service 33

ZooFence: Principled Service Partitioning and Application to...

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ieee 33rd International symposium on Reliable distributed Systems

作者： Halalai, Raluca Sutra, Pierre Riviere, Etienne Felber, Pascal Univ Neuchatel CH-2000 Neuchatel Switzerland

ISBN: (纸本)9781479955848

Cloud computing infrastructures leverage fault-tolerant and geographically distributed services in order to meet the requirements of modern applications. Each service deals with a large number of clients that compete for the resources it offers. When the load increases, the service needs to scale. In this paper, we investigate a scalability solution which consists in partitioning the service state. We formulate specific conditions under which a service is partitionable. Then, we present a general algorithm to build a dependable and consistent partitioned service. To assess the practicability of our approach, we implement and evaluate the ZooFence coordination service. ZooFence orchestrates several instances of ZooKeeper and presents the exact same API and semantics to its clients. It automatically splits the coordination service state among ZooKeeper instances while being transparent to the application. By reducing the convoy effect on operations and leveraging the workload locality, our approach allows proposing a coordination service with a greater scalability than with a single ZooKeeper instance. The evaluation of ZooFence assesses this claim for two benchmarks, a synthetic service of concurrent queues and the BookKeeper distributed logging engine.

关键词： Banking History Nominations and elections parallel processing Partitioning algorithms Semantics Synchronization coordination service state partitioning zookeeper Partitioning algorithms Semantics Superelevation banking parallel processing (COMPUTERS) semiotics customers Legal Services

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A Hybrid parallel Tridiagonal Solver on Multi-core Architectures 28

A Hybrid Parallel Tridiagonal Solver on Multi-core Architect...

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28th ieee International parallel & distributed processing symposium Workshops (IPDPSW)

作者： Tang, Guangping Li, Kenli Li, Keqin Chen, Hang Du, Jiayi Hunan Univ Coll Informat Sci & Engn Changsha 410082 Hunan Peoples R China State Univ New York New Paltz Dept Comp Sci New Paltz NY 12561 USA

ISBN: (纸本)9781479941162

An optimized parallel algorithm is proposed to solve the problem occurred in the process of complicated backward substitution of cyclic reduction during solving tridiagonal linear systems. Adopting a hybrid parallel model, this algorithm combines the cyclic reduction method and the partition method. This hybrid algorithm has simple backward substitution on parallel computers comparing with the cyclic reduction method. In this paper, the operation count and execution time are obtained to evaluate and make comparison for these methods. On the basis of results of these measured parameters, the hybrid algorithm using the hybrid approach with a multi-threading implementation achieves better efficiency than the other parallel methods, i.e., the cyclic reduction and the partition methods. Among them, the cyclic reduction method is previously found to be the fastest algorithm in many ways for solutions. In particular, the approach involved in this paper has the least scalar operation count and the shortest execution time on multi-core computer when the size of an equation is large enough. The hybrid parallel algorithm improves the performance of the cyclic reduction and partition methods by 30% and 20% respectively.

关键词： parallel tridiagonal hybrid algorithm multi-threading

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YAFIM: A parallel Frequent Itemset Mining Algorithm with Spark 28

YAFIM: A Parallel Frequent Itemset Mining Algorithm with Spa...

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28th ieee International parallel & distributed processing symposium Workshops (IPDPSW)

作者： Qiu, Hongjian Gu, Rong Yuan, Chunfeng Huang, Yihua Nanjing Univ Dept Comp Sci & Technol Natl Key Lab Novel Software Technol Nanjing 210023 Jiangsu Peoples R China

ISBN: (纸本)9781479941162

The frequent itemset mining (FIM) is one of the most important techniques to extract knowledge from data in many real-world applications. The Apriori algorithm is the widely-used algorithm for mining frequent itemsets from a transactional dataset. However, the FIM process is both data-intensive and computing-intensive. On one side, large scale data sets are usually adopted in data mining nowadays;on the other side, in order to generate valid information, the algorithm needs to scan the datasets iteratively for many times. These make the FIM algorithm very time-consuming over big data. The parallel and distributed computing is effective and mostly-used strategy for speeding up large scale dataset algorithms. However, the existing parallel Apriori algorithms implemented with the MapReduce model are not efficient enough for iterative computation. In this paper, we proposed YAFIM (Yet Another Frequent Itemset Mining), a parallel Apriori algorithm based on the Spark RDD framework-a specially-designed in-memory parallel computing model to support iterative algorithms and interactive data mining. Experimental results show that, compared with the algorithms implemented with MapReduce, YAFIM achieved 18 x speedup in average for various benchmarks. Especially, we apply YAFIM in a real-world medical application to explore the relationships in medicine. It outperforms the MapReduce method around 25 times.

关键词： Frequent Itemset Mining Apriori Algorithm parallel Computing Spark Medical Application

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Maintaining Dependable Communication Service for Mobile Stations in Wireless Mesh Networks by Tracking Capacity Demands 28

Maintaining Dependable Communication Service for Mobile Stat...

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28th ieee International parallel & distributed processing symposium Workshops (IPDPSW)

作者： Lindhorst, Timo Weseloh, Burkhard Nett, Edgar Univ Magdeburg Inst Distributed Syst Univ Pl 2 D-39106 Magdeburg Germany

ISBN: (纸本)9781479941162

Wireless Mesh Networks (WMNs) form communication networks that combine high flexibility due to self-organization within a completely wireless infrastructure and reliability due to the meshed topology. A fundamental aspect to provide dependable communication in such networks is the avoidance of network overload, which might cause significant packet loss and unbounded time delays. For this purpose, admission control mechanisms have been elaborated to determine the available network capacity and the capacity demands for new communication flows. However, even if a precise assessment of the capacity is available, the presence of mobile stations in the network cause a variation of the network capacity. When a station moves, its capacity demands may increase due to a longer path which may incur the violation of QoS guarantees. We propose a scheme to track the capacity demands of mobile stations and consider these on admission decisions. We show that this approach allows to maintain a dependable communication service even in presence of mobile stations and thus improve the applicability of WMNs for dependable mobile applications.

关键词： Dependable communication Mobility Wireless mesh networks

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Twill: A Hybrid Microcontroller-FPGA Framework for parallelizing Single-Threaded C Programs 28

Twill: A Hybrid Microcontroller-FPGA Framework for Paralleli...

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28th ieee International parallel & distributed processing symposium Workshops (IPDPSW)

作者： Gallatin, Doug Keen, Aaron Lupo, Chris Oliver, John Calif Polytech State Univ San Luis Obispo Comp Engn Program San Luis Obispo CA 93407 USA

ISBN: (纸本)9781479941162

Increasingly System-On-A-Chip platforms which incorporate both microprocessors and re-programmable logic are being utilized across several fields ranging from the auto-motive industry to network infrastructure. Unfortunately, the development tools accompanying these products leave much to be desired, requiring knowledge of both traditional embedded systems languages like C and hardware description languages like Verilog. We propose to bridge this gap with Twill, a truly automatic hybrid compiler that can take advantage of the parallelism inherent in these platforms. Twill can extract long-running threads from single threaded C code and distribute these threads across the hardware and software domains to more fully utilize the asymmetric characteristics between processors and the embedded reconfigurable logic fabric. We show that Twill provides a significant performance increase on the CHStone benchmarks with an average 1.63 times increase over the pure hardware approach and an increase of 22.2 times on average over the pure software approach while in general decreasing the area required by the reconfigurable logic compared to the pure hardware approach.

关键词： Embedded Systems Microcontroller-FPGA Hybrid Systems parallel Architecture

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proceedings of the International parallel and distributed processing symposium, IPDPS

Proceedings of the International Parallel and Distributed Pr...

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28th ieee International parallel and distributed processing symposium Workshops, IPDPSW 2014

ISBN: (纸本)9780769552088

The proceedings contain 195 papers. The topics discussed include: autotuning tensor transposition;deterministic synchronization of multi-threaded programs with operational transformation;using physical stigmergy in decentralized optimization under multiple non-separable constraints: formal methods and an intelligent lighting example;hybrid metaheuristic for annual hydropower generation optimization;machine-learning-based identification of defect patterns in semiconductor wafer maps: an overview and proposal;data quality, consistency, and interpretation management for wind farms by using neural networks;graph-based cellular automata approach to maximum lifetime coverage problem in wireless sensor networks;and GPU accelerated nature inspired methods for modeling large scale bi-directional pedestrian movement.

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gpuRF and gpuERT: Efficient and Scalable GPU Algorithms for Decision Tree Ensembles 28

gpuRF and gpuERT: Efficient and Scalable GPU Algorithms for ...

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28th ieee International parallel & distributed processing symposium Workshops (IPDPSW)

作者： Jansson, Karl Sundell, Hakan Bostrom, Henrik Univ Boras Sch Business & IT S-50190 Boras Sweden Stockholm Univ Dept Comp & Syst Sci S-16440 Kista Sweden

ISBN: (纸本)9781479941162

We present two new parallel implementations of the ensemble learning methods Random Forests (RF) and Extremely Randomized Trees (ERT), called gpuRF and gpuERT, for emerging many-core platforms, e.g., contemporary graphics cards suitable for general-purpose computing (GPGPU). RF and ERT are two ensemble methods for generating predictive models that are of high importance within machine learning. They operate by constructing a multitude of decision trees at training time and outputting a prediction by comparing the outputs of the individual trees. Thanks to the inherent parallelism of the task, an obvious platform for its computation is to employ contemporary GPUs with a large number of processing cores. Previous parallel algorithms for RF in the literature are either designed for traditional multi-core CPU platforms or early history GPUs with simpler architecture and relatively few cores. For ERT, only briefly sketched parallelization attempts exist in the literature. The new parallel algorithms are designed for contemporary GPUs with a large number of cores and take into account aspects of the newer hardware architectures, such as memory hierarchy and thread scheduling. They are implemented using the C/C++ language and the CUDA interface to attain the best possible performance on NVidia-based GPUs. An experimental study comparing the most important previous solutions for CPU and GPU platforms to the novel implementations shows significant advantages in the aspect of efficiency for the latter, often with several orders of magnitude.

关键词： CUDA GPGPU Machine Learning parallel Algorithms Random Forest

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