MSA’2000

Metacomputing Systems and Applications

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Organizing Committee

• F. Desprez, INRIA Rhône-Alpes

• E. Fleury, INRIA Lorraine

• J.-F. Méhaut, INRIA Rhône-Alpes

• Y. Robert, ENS Lyon

• www.ens-lyon.fr/LIP/

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Program Committee

• H. Bal, Vrije University, Amsterdam

• F. Berman, UCSD San Diego • J. Dongarra, UT Knoxville & ONRL

• G. von Laszewski, Argonne• T. Ludwig, TUM München • T. Priol, INRIA Rennes• M. Resch, Stuttgart

OC +

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• ISBN 1-55860-475-8 • 22 chapters by expert

authors including Andrew Chien, Jack Dongarra, Tom DeFanti, Andrew Grimshaw, Roch Guerin, Ken Kennedy, Paul Messina, Cliff Neuman, Jon Postel, Larry Smarr, Rick Stevens, and many others

The Grid: Blueprint for a New Computing Infrastructure

I. Foster, C. Kesselman (Eds), Morgan Kaufmann, 1999

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Bibliography

• Web– NPCACI (National Partnership for Advanced Computational

Infrastructure) www.npaci.edu

– GrADS (Grid Application Development Software Project) hipersoft.cs.rice.edu/grads

– “An Overview of Computational Grids and Survey of a Few Research Projects”, Jack Dongarrawww.netlib.org/utk/people/JackDongarra/talks.html

• LIP Report 99-36– “Algorithms and Tools for (Distributed) Heterogeneous

Computing: A Prospective Report” www.ens-lyon.fr/~yrobert

Framework

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Metacomputing

• Future of parallel computing distributed and heterogeneous

• Metacomputing = Making use of distributed collections of heterogeneous platforms

• Target = Tightly-coupled high-performance distributed applications(rather than loosely-coupled cooperative applications)

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Metacomputing Platforms (1)

• Low end of the fieldCluster computing with heterogeneousnetworks of workstations or PCs

– Ubiquitous in university departments and companies

– Typical poor man’s parallel computer

– Running large PVM or MPI experiments

– Make use of all available resources: slower machinesin addition to more recent ones

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Metacomputing Platforms (2)• High end of the field

Computational grid linking the most powerful supercomputers of the largest supercomputing centers through dedicated high-speed networks.

• Middle of the field Connecting medium size parallel servers

(equipped with application-specific databases andapplication-oriented software) through fast butnon-dedicated, thus creating a “meta-system”

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High end: Gusto

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Low end (1)

• Distributed ASCI Supercomputer (DAS)– Common platform for research

– (Wide-area) parallel computing and distributed applications

– November 1998, 4 universities, 200 nodes

– Node• 200 MHz Pentium Pro

• 128 MB memory, 2.5 GB disk

• Myrinet 1.28 Gbit/s (full duplex)

• Operating System: BSD/OS

– ATM Network

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Low end (2)

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Administrative Issues

• Intensive computations on a set of processors across several countries and institutions– Strict rules to define the (good) usage of shared

resources• A major difficulty is to avoid a large increase in the

administrative overhead– Challenge = find a tradeoff that does not increase the

administrative load while preserving the users’ security se rules must be guaranteed by the runtime, together with methods to migrate computations to other sites whenever some local request is raised

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Tomorrow’s Virtual Super-Computer

• Metacomputing applications will execute on a hierarchical grid– Interconnection of clusters scattered all around the world

• A fundamental characteristic of the virtual super-computer:– A set of strongly heterogeneous and geographically

scattered resources

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Algorithmic and Software Issues (1)

Whereas the architectural vision is clear,the software developments are not so well understood

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Algorithmic and Software Issues (2)

• Low end of the field:– Cope with heterogeneity– Major algorithmic effort to be undertaken

• High end of the field– Logically assemble the distributed computers: extensions to

PVM and MPI to handle distributed collection of clusters– Configuration and performance optimization

• Inherent complexity of networked and heterogeneous systems

• Resources often identified at runtime

• Dynamic nature of resource characteristics

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Algorithmic and Software Issues (3) • High-performance computing applications must:

– Configure themselves to fit the execution environment

– Adapt their behavior to subsequent changes in resource characteristics

• Parallel environments focused on strongly homogeneous architectures (processor, memory, network)– Array and loop distribution, parallelizing compilers,

HPF constructs, gang scheduling, MPI

However… Metacomputing platforms are strongly heterogeneous!

Programming environments

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Programing models (1)

• Extensions of MPI:– MPI_Connect, Nexus, PACX-MPI, MPI-Plus,

Data-Exchange, VCM, MagPIe, …

• Globus: a layered approach– Fundamental layer = a set of core services,

including resource management, security, and communications that enable the linking and interoperation of distributed computer systems

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Programing models (2)

• Object-oriented technologies to cope with heterogeneity:– Encapsulate technical ``details'' such as protocols,

data representations, migration policies– Legion is building on Mentat, an object-oriented

parallel processing system– Albatross relies on a high-performance Java system,

with a very efficient implementation of Java Remote Method Invocation.

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Programing models (3)

• Far from achieving the holy goal:

– Using the computing resources remotely and transparently,just as we do with electricity,without knowing where it comes from

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References

• Globus www.globus.org

• Legion www.cs.virginia.org/~legion

• Albatross www.cs.vu.nl/~bal/albatross

• AppLeSwww-cse.ucsd.edu/groups/hpcl/apples/apples.html

• NetSolve www.cs.utk.edu/netsolve

Algorithmic issues

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Data Decomposition Techniques for Cluster Computing

• Block-cyclic distribution paradigm = preferred layout for data-parallel programs (HPF, ScaLAPACK)

• Evenly balances total workload only if all processors have same speed

• Extending ScaLAPACK to heterogeneous clusters turns out to be surprisingly difficult

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Algorithmic challenge• Bad news: designing a matrix-matrix product or a dense

linear solver proves a hard task on a heterogeneous cluster!

• Next problems:– Simple linear algebra kernels on a collection of clusters

(extending the platform)– More ambitious routines, composed of a variety of elementary

kernels, on a heterogeneous cluster (extending the application)– Implementing more ambitious routines on more ambitious

platforms (extending both)

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Collections of clusters (1)

Slower link

Fastlink

Conclusion

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(A) Algorithmic issues

• Difficulties seem largely underestimated

• Data decomposition, scheduling heuristics, load balancing become extremely difficult in the context of metacomputing platforms

• Research community focuses on low-level communication protocols and distributed system issues (light-weight process invocation, migration, ...)

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(B) Programming level• Which is the good level ?

– Data-parallelism unrealistic, due to heterogeneity– Explicit message passing too low-level– Object-oriented approaches still request the user to have a

deep knowledge of both its application behavior and the underlying resources

– Remote computing systems (NetSolve) face severe limitations to efficiently load-balance the work

– Relying on specialized but highly-tuned libraries of all kinds may prove a good trade-off

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(C) Applications

• Key applications (from scientific computing to data-bases) have dictated the way classical parallel machines are used, programmed, and even updated into more efficient platforms

• Key applications will strongly influence, or even guide, the development of metacomputing environments

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(C) Applications (cont’d)

• Which applications will be worth the abundant but hard-to-access resources of the grid ?– tightly-coupled grand challenges ?– mobile computing applications ?– micro-transactions on the Web ?

• All these applications require new programming paradigms to enable inexperienced users to access the magic grid!

Today’s program

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Session 1: Communication and Metacomputing Infrastructures

• 9h00:10h00, Metacomputing in a High Performance Computing Center (invited talk), M. Resh.

• 10:30-11:00, Scheduling Algorithms for Efficient Gather Operation in Distributed Heterogeneous Systems,Juin-ichi Hatta & Susumu Shibusawa

• 11:00-11:30, Applying and Monitoring Latency Based Metacomputing Infrastructures, Philipp Drum & Günther Rackl.

• 11:30-12:00, MPC: A New Message Passing Library in CorbaT. Es-sqally, J. Guyard & E. Fleury.

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Session 2: Scientific Applications and Distributed Computing

• 14:00-15:00, The Netsolve Environment: Processing Towards a Seamless Grid (invited talk), D. Arnold & J. Dongarra

• 15:30-16:00, Specification of a Scilab Meta-Computing Extension,S. Contassot-Vivier, F. Lombard, J-M. Nicod & L. Philippe

• 16:00-16:30, Extending WebCom: A Proposed Framework for Web based Distributed Computing, J. P. Morrison, J. J. Kennedy & D. A. Power

• 16:30-17:30, Panel discussion