An experimental validation of the PRO model for parallel and distributed computation

National audienceThe Parallel Resource-Optimal (PRO) computation model was introduced by Gebremedhin et al. [2002] as a framework for the design and analysis of efficient parallel algorithms. The key features of the PRO model that distinguish it from previous parallel computation models are the full integration of resource-optimality into the design process and the use of a {granularity function as a parameter for measuring quality. In this paper we present experimental results on parallel algorithms, designed using the PRO model, for two representative problems: list ranking and sorting. The algorithms are implemented using SSCRAP, our environment for developing coarse-grained algorithms. The experimental performance results observed agree well with analytical predictions using the PRO model. Moreover, by using different platforms to run our experiments, we have been able to provide an integrated view of the modeling of an underlying architecture and the design and implementation of scalable parallel algorithms

An Application-Oriented Synthetic Network Traffic Generator

Abstract—Design space exploration and detailed anal-ysis in the field of hardware design applies simulation in many variants. A frequently used method is stochastic simulation where systems are stimulated by randomised input. Synthetic traffic traces mainly form the load for stochastic simulation of network computing devices. The generator presented here utilises two well-known models to meet the features of a majority of applications and traffic sources. Based on application-specific pa-rameter sets, the traffic models stochastically generate packet flows which are merged to an aggregated stream. Nevertheless, all packets can always be identified and are not resolved to a data mass representing the load of a link

An optimized workflow enactor for data-intensive grid applications

I3S laboratory Research Report (I3S/RR-2005-32-FR), Sophia Antipolis, FranceData-intensive applications benefit from an intrinsic data parallelism that should be exploited on parallel systems to lower execution time. In the last years, data grids have been developed to handle, process, and analyze the tremendous amount of data produced in many scientific areas. Although very large, these grid infrastructures are under heavy use and efficiency is of utmost importance. This paper deals with the optimization of workflow managers used for deploying complex data-driven applications on grids. In that kind of application, we show how to better exploit data parallelism than currently done in most existing workflow managers. We present the design of a prototype implementing our solution and we show that it provides a significant speed-up w.r.t existing solutions by exemplifying results on a realistic medical imaging application

Algorithmic skeletons for exact combinatorial search at scale

Exact combinatorial search is essential to a wide range of application areas including constraint optimisation, graph matching, and computer algebra. Solutions to combinatorial problems are found by systematically exploring a search space, either to enumerate solutions, determine if a specific solution exists, or to find an optimal solution. Combinatorial searches are computationally hard both in theory and practice, and efficiently exploring the huge number of combinations is a real challenge, often addressed using approximate search algorithms. Alternatively, exact search can be parallelised to reduce execution time. However, parallel search is challenging due to both highly irregular search trees and sensitivity to search order, leading to anomalies that can cause unexpected speedups and slowdowns. As core counts continue to grow, parallel search becomes increasingly useful for improving the performance of existing searches, and allowing larger instances to be solved. A high-level approach to parallel search allows non-expert users to benefit from increasing core counts. Algorithmic Skeletons provide reusable implementations of common parallelism patterns that are parameterised with user code which determines the specific computation, e.g. a particular search. We define a set of skeletons for exact search, requiring the user to provide in the minimal case a single class that specifies how the search tree is generated and a parameter that specifies the type of search required. The five are: Sequential search; three general-purpose parallel search methods: Depth-Bounded, Stack-Stealing, and Budget; and a specific parallel search method, Ordered, that guarantees replicable performance. We implement and evaluate the skeletons in a new C++ parallel search framework, YewPar. YewPar provides both high-level skeletons and low-level search specific schedulers and utilities to deal with the irregularity of search and knowledge exchange between workers. YewPar is based on the HPX library for distributed task-parallelism potentially allowing search to execute on multi-cores, clusters, cloud, and high performance computing systems. Underpinning the skeleton design is a novel formal model, MT^3 , a parallel operational semantics that describes multi-threaded tree traversals, allowing reasoning about parallel search, e.g. describing common parallel search phenomena such as performance anomalies. YewPar is evaluated using seven different search applications (and over 25 specific instances): Maximum Clique, k-Clique, Subgraph Isomorphism, Travelling Salesperson, Binary Knapsack, Enumerating Numerical Semigroups, and the Unbalanced Tree Search Benchmark. The search instances are evaluated at multiple scales from 1 to 255 workers, on a 17 host, 272 core Beowulf cluster. The overheads of the skeletons are low, with a mean 6.1% slowdown compared to hand-coded sequential implementation. Crucially, for all search applications YewPar reduces search times by an order of magnitude, i.e hours/minutes to minutes/seconds, and we commonly see greater than 60% (average) parallel efficiency speedups for up to 255 workers. Comparing skeleton performance reveals that no one skeleton is best for all searches, highlighting a benefit of a skeleton approach that allows multiple parallelisations to be explored with minimal refactoring. The Ordered skeleton avoids slowdown anomalies where, due to search knowledge being order dependent, a parallel search takes longer than a sequential search. Analysis of Ordered shows that, while being 41% slower on average (73% worse-case) than Depth-Bounded, in nearly all cases it maintains the following replicable performance properties: 1) parallel executions are no slower than one worker sequential executions 2) runtimes do not increase as workers are added, and 3) variance between repeated runs is low. In particular, where Ordered maintains a relative standard deviation (RSD) of less than 15%, Depth-Bounded suffers from an RSD greater than 50%, showing the importance of carefully controlling search orders for repeatability

Situationsbewusste Informationsdienste für das arbeitsbegleitende Lernen

Zunehmend werden Lernen und Arbeiten als miteinander verwobene Aktivitäten verstanden, was von existierenden Ansätzen nur unzureichend unterstützt wird, da sie kaum die Arbeitssituation berücksichtigen, in der sie benutzt werden. In dieser Arbeit geht es darum, eine Methodik für die Lernunterstützung zu erarbeiten und auf technischer Ebene situationsbewusste Informationsdienste mittels Kompetenzontologien und Kontextmanagement zu konzipieren und in realen Unternehmensumgebungen zu evaluieren