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ASSIST e Programmazione Grid Marco Vanneschi Dipartimento di Informatica Università di Pisa.

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Presentazione sul tema: "ASSIST e Programmazione Grid Marco Vanneschi Dipartimento di Informatica Università di Pisa."— Transcript della presentazione:

1 ASSIST e Programmazione Grid Marco Vanneschi Dipartimento di Informatica Università di Pisa

2 Gruppo Assist, gennaio 2003 Marco Aldinucci Sonia Campa Pierpaolo Ciullo Massimo Coppola Marco Danelutto Silvia Magini Paolo Pesciullesi Alessandro Petrocelli Edoardo Pistoletti Laura Potiti Roberto Ravazzolo Massimo Torquati Marco Vanneschi Corrado Zoccolo Segretaria del Gruppo: Simona Moi 050 - 2212718

3 Obiettivo Caratteristiche base di ASSIST e possibili evoluzioni Relazione con le esigenze della programmazione di griglie computazionali Componenti e oggetti Applicazioni ASSIST + CORBA

4 High-performance Grids: high-speed networks of … of clusters of … SMP/MPP …. Uniform approach to distribution + parallelism Distributed Supercomputing,Distributed Supercomputing, On demand HPC,On demand HPC, Data Intensive Computing,Data Intensive Computing, Collaborative ComputingCollaborative Computing Grid aware ComputingGrid aware Computing … Parallel Programming as the basic methodology to understand and to realize development tools to be characterized wrt the specific features of Grids to be rendered consistent with the programmability, compositionality and reuse standards

5 Uniform approach to distribution + parallelism: why ? Not so obvious that distinct parallel components / objects can be efficiently allocated to distinct nodes –Optimization of computational and data resources –Evolution of networking and communication technology Different versions of the same applications can be produced acting on parallelism degree, parallelism forms, multiplicity of components / objects Not necessarily parallelism inside a component / object is fine grain; e.g. coarse grain farms Adaptive applications Data intensive applications –Interfacing data management to computations

6 Component-structured application Abstraction of Memory Hierarchy Data intensive applications Abstraction of Shared Objects Scheduling and configuration of complex, high- volume data flows through multiple levels of hierarchy.

7 ASSIST Structured Parallel Programming + High-performance Components + Objects

8 Results of ASI-PQE project and Agenzia 2000 ASSIST version 1.0 for homogeneous and heterogeneous clusters (DI-UniPi, Synapsis) –A subset of ASSIST-CL –Support based on ACE + Distributed Shared Memory –Object-based design of compiler and support –Task-code and AssistLib: lower levels of programmability Integration of scientific libraries in ASSIST support (CPS-CNR, Na) AssistConf: a first tool for Grid configuration on top of Globus (ISTI-CNR, Pi) Demonstration on computational chemistry applications (UniPg)and benchmarking (PoliMi)

9 ASSIST Coordination Language ( ASSIST-CL ) Programs as general graphs whose nodes are parallel modules and/or sequential modules Compositionality through streams Data-flow behaviour + state + nondeterminism Programs reusable as nodes of other programs Programs could also be expressed as SkIE graphs (pipeline, farm, loop skeletons), where the composed modules are ASSIST -modules

10 External Objects Application structuring through Parallel Components and Objects M3M3 Component: Parallel (or sequential) module Input streams Output stream M5M5 s 34 M4M4 s 25 s 45 s 54 M1M1 s 13 M2M2 s 23 Global variables Shared memory Files and I/O Web CORBA ASSIST modules... Some abstractions are present in ASSIST 1.0, others will be realized in Grid projects

11 Graphs and streams ASSIST networks of components operate on streams DAG as particular case (stream length = 1) –co-scheduling is expressed by program or implemented at the support level –in ASSIST DAGs could also be executed in pipeline Importance of multiple streams –data intensive computations, –interactive computations, –calculation / communication overlapping, – …

12 Formalisms for ASSIST programs as a graph M1M1 M2M2 M3M3 M4M4 M5M5 s 13 s 23 s 34 s 25 generic main (...) stream int s13; stream int [N][N] s23; stream int [N][N] s34; stream int s25; M1 (ouput_stream s13); M2 (output_stream s23, s25); M3 (input_stream s13, s23; output_stream s34); M4 (input_stream s34); M5 (input_stream s25); Graphs with general structure Compositionality through streams (i.e. sequences of typed data) Current, experimental syntax/semantics; the FIRB GRID.IT project will consider IDL-compliant CCA-compliant scripting languages...

13 Parallel Module (parmod): parallel component in ASSIST Set of Virtual Processors with names VP External Objects Output streams... Input streams... Other Modules Other Modules Several distribution and collection strategies, nondeterminism Distributed internal state

14 Parallel components + objects Not only a classical approach to software reuse Often, external objects are simpler and/or more performant solutions to programming problems –e.g. data distribution, load balancing, memory capacity, … –dynamic data structures (shape and size not known until run-time) In turn, parallel ASSIST programs may be used as objects C1C1 C2C2 CiCi CnCn S1S1 S2S2 SjSj SmSm... Components and streams External Objects...

15 An example of complex application: data mining C4.5 expressed as a parallel Divide & Conquer T Client Test Divide Conquer training set TS, decision tree P1P1 PNPN For load balancing reasons, during some phases the same parmod (Divide) works in a data-parallel manner, in other phases in a farm-like manner, in other phases … An example of adaptive strategy expressed at the programming level.

16 Data intensive computations in ASSIST Object (possibly high-bandwidth) Abstraction of high- performance objects can be implemented by ASSIST parmod(s) with proper interface (expressed in ASSIST or another formalism) VP Input Section Output Section ASSIST module (parallel component) External Object Interface (possibly parallel)

17 Data intensive computations in ASSIST 1.High-performance abstaction of objects (e.g. complex memory hierarchies) can be implemented by parallelism (see previuos slide). 2.In some cases, it could be more efficient to interface external objects through the Input/Output sections of parallel components: an ASSIST extension to be studied. 3.The combination of techniques 1 and 2 might be very powerful.

18 Input Section, Virtual Processors, Output Section of parmod I Input Section s 13 s 23 O Output Section s 34 VP Virtual Processors Management of input streams according to the proper strategy (data-flow, nondeterministic) Possible pre-elaborations (filter) and operations Distribution of received values to VPs For each output stream: Output strategies of values computed by VPs Possible post-elaborations and operations External Objects internal pipelining

19 Input Section: data distributions On demand Broadcast Scatter Multicast Input/Output sections could be optimized for external objects interfacing data management, memory hierarchies minimizing bottlenecks (pre/post-processing) in addition to VP parallel access to objects security and fault tolerance + nondeterminism and on the fly processing of stream data

20 Optimizations, scheduling, mapping, … (FIRB GRID.IT project) New research issues [e.g.: GrADS] are to be dealt with: –challanges in static + dynamic compiler design –importance of a constraint-based programming model New impacts on middleware –new light-weight middleware –exploiting Knowledge Discovery in Grid databases More that one level of programmability? Expressive power for dynamic behaviours.


22 ASSIST e CORBA Un programma ASSIST può importare ed esportare servizi tramite lo standard CORBA. ASSIST vede CORBA come un oggetto esterno Limportazione/esportazione avviene allinterno delle procedure ( proc ) di un qualsiasi modulo sequenziale o parallelo. Lutilizzo di CORBA allinterno di un programma ASSIST non richiede particolari accorgimenti sintattici.

23 Cliente in ASSIST Il Server CORBA esporta un servizio che si può utilizzare in un programma ASSIST. Il Cliente è un modulo ASSIST (sequenziale o parallelo) che: inizializza un ORB si procura un object Reference invoca il metodo del server. Server CORBA Client ASSIST

24 Cliente parallelo Il programma ASSIST ottiene i dati di input dal server CORBA - GUI Il programma ASSIST esegue N-body e visualizza i dati tramite il server CORBA. Linterazione avviene quindi contemporaneamente con il calcolo GUI Server CORBA clientloop N-body Programma ASSIST

25 Esportazione di servizi Ogni applicazione F scritta in ASSIST, con un INPUT (A) ed un OUTPUT (B), puo essere esportata come servizio attraverso CORBA, aggiungendo allapplicazione un modulo che svolga le funzioni del Server. Il Server esporta il metodo do_F con lINPUT e lOUTPUT dellapplicazione F. server F Programma ASSIST A B Programma ASSIST disponibile come servizio CORBA

26 Esempio di esportazione di servizi SPT: parmod ASSIST Server: modulo ASSIST che si interfaccia con il mondo esterno mediante CORBA. Il server puo essere generato in modo automatico a partire dal codice ASSIST. Client: programma scritto in un qualsiasi linguaggio supportato da CORBA ; invoca il metodo do_spt esportato dal server. server spt Programma ASSIST A B Client con interfaccia CORBA parmod one parmod array B = do_SPT(A)

27 The e nd

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