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Wide-Area Parallel Computing in Java

vrije Universiteit. Wide-Area Parallel Computing in Java. Henri Bal. Vrije Universiteit Amsterdam Faculty of Sciences. Introduction. Distributed supercomputing Parallel applications on geographically distributed computing system (computational grid) Examples: SETI@home, RSA-155

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Wide-Area Parallel Computing in Java

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  1. vrije Universiteit Wide-Area Parallel Computing in Java Henri Bal Vrije Universiteit Amsterdam Faculty of Sciences

  2. Introduction • Distributed supercomputing • Parallel applications on geographically distributed computing system (computational grid) • Examples: SETI@home, RSA-155 • Programming support • Language-neutral systems: Legion, Globus • Language-centric: Java • Goal: study wide-area parallel computing in Java • Programming model: Remote Method Invocation

  3. Outline • Wide-area parallel computing • Java Remote Method Invocation (RMI) • Performance of JDK RMI • The Manta high-performance Java system • Wide-area parallel Java applications using RMI • Application performance

  4. Wide-area parallel computing • Challenge • Tolerating poor latency and bandwidth of WANs • Basic assumption: wide-area system is hierarchical • Connect clusters, not individual workstations • Most links are fast • General approach • Optimize applications to exploit hierarchical structure most communication is local

  5. Distributed ASCI Supercomputer VU (128) UvA (24) Node configuration 200 MHz Pentium Pro 64-128 MB memory 2.5 GB local disks Myrinet LAN Redhat Linux 2.0.36 6 Mb/s ATM Leiden (24) Delft (24)

  6. Java • Growing interest in Java for parallel applications • Java Grande forum • Parallel programming support in Java • Shared memory : multithreading • Distributed memory : Remote Method Invocation • Study suitability of Java RMI for (wide-area) parallel programming • Optimizing performance of local RMI [PPoPP’99] • Wide-area parallel programming using RMI [JavaGrande’99]

  7. Animal Orca Panda Manta RMI (1) • Flexible object-oriented RPC-like primitive • Easy interoperability between Java Virtual Machines • Polymorphism  dynamic bytecode loading void species(Animal x) throws … { System.out.println(“Species “ + x.name()); } o.species(new Orca());  “Species orca” o.species(new Panda());  “Species panda” o.species(new Manta());  “Species manta”

  8. RMI (2) • Designed for client-server applications • Automatic serialization (marshalling) • Normally used in a high latency environment • E.g. Internet • Is RMI fast enough for parallel programming ?

  9. JDK RMI Performance ( 200 MHz Pentium Pro, JDK 1.1.4 )

  10. Why is JDK RMI slow ? • Serialization uses run-time type inspection • Protocol overhead (class information) • Thread creation for incoming calls • TCP/IP • Most code is written in Java

  11. The Manta system • Designed for high-performance computing • Native (static) compilation • Source  executable • New fast RMI protocol between Manta nodes • Support (polymorphic) RMIs with JVMs • Implemented on wide-area DAS system

  12. JDK versus Manta 200 MHz Pentium Pro, Myrinet, JDK 1.1.4 interpreter,1 object as parameter

  13. Manta serialization class Test implements Serializable { int i; double d; Object o; } Java Source void PackageClass__Test(…) { WRITE_INT( type_id ); WRITE_INT( i ); WRITE_DOUBLE( d ); WRITE_OBJECT( o ); } Manta JDK

  14. RMI protocol • Light-weight RMI protocol • Send minimal type information • Avoid thread creation • Simple nonblocking methods executed directly • Avoid interrupts • Poll network when processor is idle • Everything is written in C

  15. Communication software Manta RMI • Panda user space RPC protocol • LFC Myrinet control program • Similar to active messages • Implemented partly on Myrinet network interfaces • Myrinet network interfaces mapped in user space Panda RPC LFC UDP TCP Myrinet Ethernet ATM

  16. Interoperability with JVMs • Manta RMI protocol incompatible with JDK • Use fast RMI between Manta nodes • Use JDK-compliant protocol with JVMs • Polymorphic RMI requires exchanging bytecodes • Also generate bytecodes when compiling a program • Dynamically compile and link bytecodes into running program

  17. Null-RMI latency

  18. RMI Throughput

  19. Outline • Wide-area parallel computing • Java Remote Method Invocation (RMI) • Performance of JDK RMI • The Manta high-performance Java system • Wide-area parallel Java applications using RMI • Application performance

  20. 2 orders of magnitude between intra-cluster (LAN) and inter-cluster (WAN) communication performance Manta exposes hierarchical structure to application Applications are optimized to reduce WAN-overhead Manta on wide-area DAS

  21. Wide-area programming • Problem: how to tolerate difference between LAN and WAN performance • Wide-area system is structured hierarchically • Most links are fast • Approach: application-level optimizations that exploit the hierarchical structure • Reduce wide-area communication

  22. Application experience • Parallel applications • Successive overrelaxation (SOR) • All-pairs shortest paths problem (ASP) • Traveling salesperson problem (TSP) • Iterative Deepening A* (IDA*) • Measurements on wide-area DAS • 1-4 clusters with 16 nodes • Comparison with single 64-node cluster

  23. Successive Overrelaxation • Red/black SOR • Neighbor communication, using RMI • Problem: nodes at cluster-boundaries • Overlap wide-area communication with computation • RMI is synchronous  use multithreading 5600 µsec µs 40 CPU 1 CPU 2 CPU 3 CPU 4 CPU 5 CPU 6 Cluster 1 Cluster 2

  24. All-pairs shortest paths • Broadcast at beginning of each iteration • Problem: broadcasting over wide-area links • Lack of broadcast in Java -> use spanning tree • Use coordinator node per cluster • Do asynchronous send to all remote coordinators • Implemented using threads Cluster 1 2 3

  25. 1 2 3 Traveling salesperson problem • Replicated-worker style parallel search algorithm • Problem: work distribution • Central job-queue has high overhead • Statically distribute jobs over clusters • Use centralized job-queue per cluster • Easy to express using RMI

  26. Iterative Deepening A* • Parallel search algorithm using work stealing • Problem: inter-cluster work stealing • Optimization: first look for work in local cluster • Easy to express using RMI Cluster 1 2

  27. Performance • Wide-area DAS system: 4 clusters of 16 CPUs • Comparison with single 16-node and 64-node cluster

  28. Conclusions • Fast RMI possible through • Compiler-generated serialization, light-weight communication & RMI protocols • Optimized wide-area applications are efficient • Reduce wide-area communication, or hide its latency • Java RMI is easy to use, but some optimizations are awkward to express • No asynchronous communication, collective comm. • Programming systems should take hierarchical structure of wide-area systems into account http://www.cs.vu.nl/manta

  29. Performance breakdown Manta ( Fast Ethernet )

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