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MSA’2000 Metacomputing Systems and Applications. 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/. Program Committee. OC +. - PowerPoint PPT Presentation
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MSA’2000
Metacomputing Systems and Applications
MSA Introduction 2
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/
MSA Introduction 3
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 +
MSA Introduction 4
• 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
MSA Introduction 5
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
MSA Introduction 7
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)
MSA Introduction 8
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
MSA Introduction 9
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”
MSA Introduction 10
High end: Gusto
MSA Introduction 11
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
MSA Introduction 12
Low end (2)
MSA Introduction 13
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
MSA Introduction 14
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
MSA Introduction 15
Algorithmic and Software Issues (1)
Whereas the architectural vision is clear,the software developments are not so well understood
MSA Introduction 16
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
MSA Introduction 17
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
MSA Introduction 19
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
MSA Introduction 20
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.
MSA Introduction 21
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
MSA Introduction 22
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
MSA Introduction 24
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
MSA Introduction 25
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)
MSA Introduction 26
Collections of clusters (1)
Slower link
Fastlink
Conclusion
MSA Introduction 28
(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, ...)
MSA Introduction 29
(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
MSA Introduction 30
(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
MSA Introduction 31
(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
MSA Introduction 33
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.
MSA Introduction 34
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