1. Parallel Computing on Wide-Area Clusters: the Albatross Project Aske Plaat Thilo Kielmann Jason Maassen Rob van Nieuwpoort Ronald Veldema Vrije Universiteit Amsterdam Faculty of Sciences vrije Universiteit Henri Bal

2. 2 Introduction Cluster computing becomes popular -Excellent price/performance ratio -Fast commodity networks Next step: wide-area cluster computing -Use multiple clusters for single application -Form of metacomputing Challenges -Software infrastructure (e.g., Legion, Globus) -Parallel applications that can tolerate WAN-latencies

3. 3 Albatross project Study applications and programming environments for wide-area parallel systems Basic assumption: wide-area system is hierarchical -Connect clusters, not individual workstations General approach -Optimize applications to exploit hierarchical structure  most communication is local

4. 4 Outline Experimental system and programming environments Application-level optimizations Performance analysis Wide-area optimized programming environments

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

6. 6 Programming environments Existing library/language + expose hierarchical structure -Number of clusters -Mapping of CPUs to clusters Panda library -Point-to-point communication -Group communication -Multithreading Panda JavaOrcaMPI LFCTCP/IP ATMMyrinet

7. 7 Example: Java Remote Method Invocation (RMI) -Simple, transparent, object-oriented, RPC-like communication primitive Problem: RMI performance -JDK RMI on Myrinet is factor 40 slower than C-RPC (1228 vs. 30 µsec) Manta: high-performance Java system [PPoPP’99] -Native (static) compilation: source  executable -Fast RMI protocol between Manta nodes -JDK-style protocol to interoperate with JVMs

8. 8 JDK versus Manta 200 MHz Pentium Pro, Myrinet, JDK 1.1.4 interpreter, 1 object as parameter

9. 9 2 orders of magnitude between intra-cluster (LAN) and inter-cluster (WAN) communication performance Application-level optimizations [JavaGrande’99] -Minimize WAN-overhead Manta on wide-area DAS

10. 10 Example: SOR Red/black Successive Overrelaxation -Neighbor communication, using RMI Problem: nodes at cluster-boundaries -Overlap wide-area communication with computation -RMI is synchronous  use multithreading Cluster 1Cluster 2 CPU 3CPU 2CPU 1CPU 6CPU 5CPU 4 50 5600 µsec µs

11. 11 Wide-area optimizations

12. 12 Performance Java applications Wide-area DAS system: 4 clusters of 10 CPUs Sensitivity to wide-area latency and bandwidth: -See HPCA’99

13. 13 Optimized applications obtain good speedups -Reduce wide-area communication, or hide its latency Java RMI is easy to use, but some optimizations are awkward to express -Lack of asynchronous communication and broadcast RMI model does not help exploiting hierarchical structure of wide-area systems Need wide-area optimized programming environment Discussion

14. 14 MagPIe: wide-area collective communication Collective communication among many processors -e.g., multicast, all-to-all, scatter, gather, reduction MagPIe: MPI’s collective operations optimized for hierarchical wide-area systems [PPoPP’99] Transparent to application programmer

15. 15 Spanning-tree broadcast Cluster 1Cluster 2Cluster 3Cluster 4 MPICH (WAN-unaware) -Wide-area latency is chained -Data is sent multiple times over same WAN-link MapPIe (WAN-optimized) -Each sender-receiver path contains at most 1 WAN-link -No data item travels multiple times to same cluster

16. 16 MagPIe results MagPIe collective operations are wide-area optimal, except non-associative reduction Operations up to 10 times faster than MPICH Factor 2-3 speedup improvement over MPICH for some (unmodified) MPI applications

17. 17 Conclusions Wide-area parallel programming is feasible for many applications Exploit hierarchical structure of wide-area systems to minimize WAN overhead Programming systems should take hierarchical structure of wide-area systems into account