1. Parallel Programming on Computational Grids

2. Outline Grids Application-level tools for grids Parallel programming on grids Case study: Ibis

3. Grids Seamless integration of geographically distributed computers, databases, instruments –The name is an analogy with power grids Highly active research area –Open Grid Forum –Globus middleware –Many European projects, e.g.: Gridlab: Grid Application Toolkit and Testbed DEISA: Distributed European Infrastructure for Supercomputing Applications XtreemOS: Linux-based OS for grids –VL-e (Virtual laboratory for e-Science) project –….

4. Why Grids? New distributed applications that use data or instruments across multiple administrative domains and that need much CPU power –Computer-enhanced instruments –Collaborative engineering –Browsing of remote datasets –Use of remote software –Data-intensive computing –Very large-scale simulation –Large-scale parameter studies

5. Web, Grids and e-Science Web is about exchanging information Grid is about sharing resources –Computers, data bases, instruments e-Science supports experimental science by providing a virtual laboratory on top of Grids –Support for visualization, workflows, data management, security, authentication, high-performance computing

6. The big picture Management of comm. & computing Management of comm. & computing Management of comm. & computing Potential Generic part Potential Generic part Potential Generic part Application Virtual Laboratory Application oriented services Grids Harness distributed resources

7. The data explosion e-Science experiments generate much data, that often is distributed and that need much (parallel) processing –high-resolution imaging: ~ 1 GByte per measurement –Bio-informatics queries: 500 GByte per database –Satellite world imagery: ~ 5 TByte/year –Current particle physics: 1 PByte per year –LHC physics (2010?): 10-30 PByte per year

8. Grid programming The goal of a grid is to make resource sharing very easy (transparent) Practice: grid programming is very difficult –Finding resources, running applications, dealing with heterogeneity and security, etc. Grid middleware (Globus) makes this somewhat easier, but is still low-level and changes frequently Need application-level tools

9. Application-level tools Paper (on Blackboard): –Blueprint for a New Computing Infrastructure (2nd Edition, editors I. Foster and C. Kesselman); Chapter 24: Application- Level tools (Bal, Casanova, Dongarra, Matsuoka) Builds on grid software infrastructure Isolates users from dynamics of the grid hardware infrastructure Generic (broad classes of applications) Easy-to-use

10. Taxonomy of application-level tools Grid programming models –RPC –Task parallelism –Message passing –Java programming Grid application execution environments –Parameter sweeps –Workflow –Portals

11. Remote Procedure Call (RPC) GridRPC: specialize RPC-style (client/server) programming for grids –Allows coarse-grain task parallelism & remote access –Extended with resource discovery, scheduling, etc. Example: NetSolve –Solves numerical problems on a grid Current development: use web technology (WSDL, SOAP) for grid services Web and grid technology are merging

12. Task parallelism Many systems for task parallelism (master-worker, replicated workers) exist for the grid Examples –MW (master-worker) –Satin: divide&conquer (hierarchical master-worker)

13. Message passing Several MPI implementations exist for the grid PACX MPI (Stutgart): –Runs on heterogeneous systems MagPIe (Thilo Kielmann): –Optimizes MPI’s collective communication for hierarchical wide-area systems MPICH-G2: – Similar to PACX and MagPIe, implemented on Globus

14. Java programming Java uses bytecode and is very portable –``Write once, run anywhere’’ Can be used to handle heterogeneity Many systems now have Java interfaces: –Globus (Globus Java Commodity Grid) –MPI (MPIJava, MPJ, …) –Gridlab Application Toolkit (Java GAT) Ibis and ProActive are Java-centric grid programming systems

15. Parameter sweep applications Computations what are mostly independent –E.g. run same simulation many times with different parameters Can tolerate high network latencies, can easily be made fault-tolerant Many systems use this type of trivial parallelism to harness idle desktops –APST, SETI@home, Entropia, XtremWeb

16. Workflow applications Link and compose diverse software tools and data formats –Connect modules and data-filters Results in coarse-grain, dataflow-like parallelism that can be run efficiently on a grid Several workflow management systems exist –E.g. Virtual Lab Amsterdam (predecessor VL-e)

17. Portals Graphical interfaces to the grid Often application-specific Also portals for resource brokering, file transfers, etc.

18. Outline Grids Application-level tools for grids Parallel programming on grids Case study: Ibis

19. Distributed supercomputing Parallel processing on geographically distributed computing systems (grids) Examples: –SETI@home ( ), RSA-155, Entropia, Cactus Mostly limited to trivially parallel applications Questions: –Can we generalize this to more HPC applications? –What high-level programming support is needed?

20. Grids usually are hierarchical –Collections of clusters, supercomputers –Fast local links, slow wide-area links Can optimize algorithms to exploit this hierarchy –Minimize wide-area communication Wide-area bandwidth is increasing –DAS-3 has 10 Gb/s dedicated optical links between sites –Wide-area latency remains high (limited by speed-of-light) Speedups on a grid?

21. Example: N-body simulation Much wide-area communication –Each node needs info about remote bodies CPU 1 CPU 2 CPU 1 CPU 2 AmsterdamDelft

22. Trivial optimization AmsterdamDelft CPU 1 CPU 2 CPU 1 CPU 2

23. Wide-area optimizations Message combining on wide-area links Latency hiding on wide-area links Collective operations for wide-area systems –Broadcast, reduction, all-to-all exchange Load balancing Conclusions: –Many applications can be optimized to run efficiently on a hierarchical wide-area system –Need better programming support

24. Outline Grids Application-level tools for grids Parallel programming on grids Case study: Ibis al., AGRIDM’03 (Workshop on Adaptive Grid Middleware, New Paper: –Real-World Distributed Computing with Ibis, Sept. 2003

25. The Ibis system High-level & efficient programming support for distributed supercomputing Use Java-centric approach + JVM technology –Inherently more portable than native compilation Goal: drastically simplify programming and deployment of high performance distributed applications Target: –Large-scale distributed systems, including clusters, grids, desktop grids, clouds, mobile devices …. –Possibly all at the same time for 1 application

26. Real-world distributed systems

27. World wide testbed

28. Problem How to write (high-performance) applications for real-world distributed systems? How to deal with: –Performance: efficiency on wide-area system –Heterogeneity: different systems & APIs –Malleability:resources come and go –Fault tolerance: crashes –Connectivity:firewalls, NAT, etc.

29. Our approach Study fundamental underlying problems … hand-in-hand with realistic applications … integrate solutions in one system: Ibis Distributed SystemsUser !

30. Applications Scientific applications –Imaging (VU Medical Center, AMOLF) –Bioinformatics (sequence analysis) –Astronomy (data analysis challenge) Multimedia content analysis Games and model checking Semantic web (distributed reasoning)

31. Multimedia content analysis Automatically extract information from images & video –E.g., video archive, surveillance cameras Extract feature vectors from images –Describe properties (color, shape) –Data-parallel task on a cluster Compute on consecutive images –Task-parallelism on a grid

32. Example: object recognition ● Analyze video stream from camera to learn and recognize every-day objects ● Representative for more serious applications ● Same algorithms used for surveillance cameras ● London Underground  >120.000 years of processing for >> 10.000’s CCTV cameras

33. Games and Model Checking Can solve entire Awari game on wide-area DAS-3 (889 B positions) –Needs 10G private optical network Distributed model checking has very similar communication pattern –Search huge state spaces, random work distribution, bulk asynchronous transfers Can efficiently run DeVinE model checker on wide- area DAS-3, use up to 1 TB memory

34. Distributed reasoning MaRVIN (Frank van Harmelen et al, VU): –A distributed platform for massive RDF inferencing (deductive closure) –``a brain the size of a planet’’ Uses Ibis to run on heterogeneous systems (clusters, desktop grids) Used for Billion Triple track of Semantic Web Challenge 2008 –Inputs 800M RDF triples, derives 29B triples

35. Awards SCALE 2008 (CCGrid’08) DACH 2008 – BS DACH 2008 - FT AAAI-VC 2007 ISWC 2008 Multimedia Computing Astronomy Semantic Web (van Harmelen et al.) (Cluster/Grid’08)

36. Ibis Philosophy Real-world distributed applications should be developed and compiled on a local workstation, and simply be launched from there

37. Ibis Approach Virtual Machines (Java) deal with heterogeneity Provide range of programming abstractions Designed for dynamic/faulty environments Easy deployment through middleware-independent programming interfaces Modular and flexible: can replace Ibis components by external ones

38. Ibis Design Applications need functionality for –Programming (as in programming languages) –Deployment (as in operating systems) Programming Logical Likes math Deployment Practical Visual (GUI)

39. Ibis System

40. Ibis brains

41. Programming system

42. Programming models Message passing (IPL, RMI, MPJ) Satin: Fault-tolerant, malleable divide-and-conquer system Jorus: Transparent library with multimedia operations Maestro: Self-optimizing fault-tolerant dataflow framework

43. Satin: a parallel divide-and-conquer system on top of Ibis Divide-and-conquer is inherently hierarchical More general than master/worker Satin: Cilk-like primitives (spawn/sync) in Java

44. Example interface FibInter { public int fib(long n); } class Fib implements FibInter { int fib (int n) { if (n < 2) return n; return fib(n-1) + fib(n-2); } Single-threaded Java

45. Example Java + divide&conquer interface FibInter extends ibis.satin.Spawnable { public int fib(long n); } class Fib extends ibis.satin.SatinObject implements FibInter { public int fib (int n) { if (n < 2) return n; int x = fib (n - 1); int y = fib (n - 2); sync(); return x + y; }

46. IPL (Ibis Portability Layer) Java-centric “run-anywhere” library Point-to-point, multicast, streaming Simple model for tracking resources –Join-Elect-Leave –Supports malleability & fault-tolerance

47. SmartSockets library Detects connectivity problems Tries to solve them automatically With as little help from the user as possible Integrates existing and several new solutions Reverse connection setup, STUN, TCP splicing, SSH tunneling, smart addressing, etc. Uses network of hubs as a side channel

48. Ibis Deployment system

49. IbisDeploy GUI

50. JavaGAT GAT: Grid Application Toolkit –Makes grid applications independent of the underlying grid infrastructure Used by applications to access grid services –File copying, resource discovery, job submission & monitoring, user authentication Successor API is currently being standardized

51. Grid Applications with GAT GAT Engine Remote Files Monitoring Info service Resource Management GridLabGlobusUnicoreSSHP2PLocal GAT Grid Application File.copy(...)‏ submitJob(...)‏ gridftp globus Intelligent dispatching Koala

52. Zorilla: Java P2P supercomputing middleware

53. Ibis demo (movie)

54. Object recognition Client Broker Servers Ibis (Java) Runs simultaneously on clusters (DAS-3, Japan, Australia), Desktop Grid, Amazon EC2 Cloud Connectivity problems solved automatically by Ibis SmartSockets

55. Ibis movie (part 1)

56. Performance on 1 DAS-3 cluster Relative speedups of Java/Ibis and C++/MPI –Using TCP or Myricom’s MX protocol Sequential performance Java: 88% of C++

57. DAS-3DAS-3

58. Speedup (wide-area) Homogeneous wide-area systems (DAS-3): –Frame rate increases linearly with #clusters World-wide experiment : –24 frames per second (@ 640 x 480 resolution) –Speed limited by camera, not computing infrastructure

59. Smart Phones GSM + PC + GPS + camera + networks + …. Will become ubiquitous (like GSMs) Our goal: study distributed applications running on (multiple) smart phones & other resources

60. Example: eyeDentify Implemented Ibis on Android –Google’s open-source Java-based platform Object recognition (eyeDentify) on a G1 smartphone Deploys computation server on DAS-3 cluster Launched from IbisDeploy/eyeDentify client on phone + +

61. Summary Parallel computing on Grids (distributed supercomputing) is a challenging and promising research area Many grid programming environments exist Ibis: a Java-centric Grid programming environment Extends to the mobile world