ASSIST e Programmazione Grid

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

2. Gruppo Assist, gennaio 2003 Segretaria del Gruppo: Simona Moi moi@di.unipi.it 050 - 2212718 • 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

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, • On demand HPC, • Data Intensive Computing, • Collaborative Computing • Grid 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. Data intensive applications Abstraction of Memory Hierarchy Abstraction of Shared Objects Component-structured application 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. Global variables • Shared memory • Files and I/O • Web • CORBA • ASSIST modules • . . . • External Objects  Output stream M1 s13 Input streams s34 M3 M4 M2 s23 s45 s54 M5 s25 Some abstractions are present in ASSIST 1.0, others will be realized in Grid projects Component: Parallel (or sequential) module Application structuring through Parallel Components and Objects

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. s13 s34 M3 M4 M1 s23 s25 M2 M5 Formalisms for ASSIST programs as a graph • 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 • . . . • 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); • 

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

14. C1 C2 Ci Cn Parallel components + objects S1 S2 . . . Sj . . . Sm External 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 . . . Components and streams . . .

15. An example of complex application: data mining C4.5 expressed as a parallel Divide & Conquer  training set TS, decision tree G  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 … Divide Client P1 Conquer T G1 G G2 PN G Test An example of adaptive strategy expressed at the programming level.

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

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

18. External Objects s13 I Input Section O Output Section s34 s23 • For each output stream: • Output strategies of values computed by VPs • Possible post-elaborations and operations • Management of input streams according to the proper strategy (data-flow, nondeterministic) • Possible pre-elaborations (filter) and operations • Distribution of received values to VPs Input Section, Virtual Processors, Output Section of parmod VP Virtual Processors internal pipelining

19. Input Section: data distributions + nondeterminism and “on the fly” processing of stream data • 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

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.

21. ASSIST and CORBA

22. ASSISTeCORBA • Un programma ASSIST può importare ed esportare servizi tramite lo standard CORBA. • ASSIST vede CORBA come un oggetto esterno • L’importazione/esportazione avviene all’interno delle procedure (proc) di un qualsiasi modulo sequenziale o parallelo. • L’utilizzo di CORBA all’interno di un programma ASSIST non richiede particolari accorgimenti sintattici.

23. Server CORBA Client ASSIST 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.

24. GUI Server CORBA Cliente parallelo Programma ASSIST client loop N-body • 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. • L’interazione avviene quindi contemporaneamente con il calcolo

25. Esportazione di servizi • Ogni applicazione Fscritta in ASSIST, con un INPUT (A) ed un OUTPUT (B), • puo’ essere esportata come servizio attraverso CORBA, • aggiungendo all’applicazione un modulo che svolga le funzioni del Server. • Il Server esporta il metodo do_F con l’INPUT e l’OUTPUT dell’applicazione F. Programma ASSIST A server F 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. Client con interfaccia CORBA B = do_SPT(A) Programma ASSIST A server spt B parmod one parmod array

27. The end