123Volume 2, Issue 3Copyright © 2001 John Wiley & Sons, Ltd.focussoftware

Autumn 2001

Parallel andDistributed Computing

Roland WismüllerTU MünchenInstitut für InformatikSAB, Lehrstuhl für Rechnertechnik und RechnerorganisationD-80290 München, Germanywismuell@informatik.tu-muenchen.de

After a decade of research, parallel

processing is just becoming a

standard instrument of high

performance computing, which is

used as naturally as any other

specialised programming technique.

ROLAND WISMÜLLER highlights

some interesting trends in

Concurrency and Computation:

Practice and Experience.

With the ever-growing accuracy ofsimulation models, combined with newparallel architectures and more advancedprogramming methods and tools, parallelcomputing looks set to continue todevelop exciting applications and evenmore exciting results.

In a recent issue of Concurrency andComputation: Practice and Experience onparallel programming, two of the papersaddress specific aspects of new parallelarchitectures. Rauch et al. are concernedwith clusters of workstations or PCs. Theirresearch is motivated by an importantproblem of clusters and otherdecentralised systems: how can weefficiently support the systemadministrators’ task of keeping thesoftware configuration of all machinesconsistent? The work of Theobald et al. highlights a very different, butequally important architecture trend:

multi-threaded architectures. Theresearch group follows a stepwiseapproach, where off-the-shelfmicroprocessors are extended withvarious levels of special hardware forsupporting multi-threaded execution.Thus, they can find a trade-off betweenhardware cost and performance.

While these papers suggest that in therealm of parallel architectures there is amovement towards new approaches, theremaining two articles indicate that theprogramming support for parallelmachines rather is characterised by asteady evolution. Existing techniques andtools are noticeably enhanced in order tomake them easier to use, more flexible,and more and more generally applicable.Cain et al. present an example from thearea of performance analysis. ThePerformance Consultant automaticallylocates different classes of bottlenecks in aparallel program. By using a moresuitable search strategy, both the searchtime and the accuracy could be improved.Schloegel et al. developed enhancedmethods of graph partitioning, which arevital for mesh-based parallel applications.Their improved algorithms account formultiple constraints to be minimised atthe same time, thus resulting in a goodload balancing even in applications thatconsist of multiple phases with differentload characteristics.

Existing techniques and tools arenoticeably enhanced in order tomake them more easy to use

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