Charlotte: Metacomputing on the Web

Charlotte:Metacomputing on the Web • Arash Baratloo Mehmet KaraulZvi Kedem Peter WyckoffNew York University

Roadmap • Goals • Virtual machine model • Code sample • Execution environment • Distributed shared memory • Experiments • Summary

Goals • Programmer’s goals • High level language • Reliable and predictable virtual machine (fault tolerance, heterogeneity of machine types and speeds, transiently available machines) • Portability • User’s goals • Utilize any machine on the Web (no account or shared file system) • Reliable and predictable virtual machine • Authentication of results • CPU donator’s goals • Protection from malicious code • Full control of her resources • No administrative hassles

Leveraging Java • Predictable and reliable virtual machine on top of the Java virtual machine • Java-capable browser widely available • Emerging standard • Security • Heterogeneity • Portability • Compilers to appear in the near future

Virtual Machine Model • Separation of programming and execution environment • Programmer develops applications for a perfect virtual machine • Slow and fast machines are handled transparently by the runtime system • Transiently available machines handled transparently by the runtime system • Fault tolerance handled transparently by the runtime system • High level programming model • Unbounded number of parallel routines • Java plus three simple language constructs • Distributed shared memory • Simple memory semantics

public class MatrixMult extends Droutine { public static int Size = 500; public Dfloat a[][] = new Dfloat[Size][Size]; public Dfloat b[][] = new Dfloat[Size][Size]; public Dfloat c[][] = new Dfloat[Size][Size]; public void drun(int numTasks, int id) { int sum; for(int i=0; i<Size; i++) { sum = 0; for(int j=0; j<Size; j++) sum += a[id][j].get() * b[j][i].get(); c[id][i].set(sum); } } public void run() { InitMatrix(a); InitMatrix(b); parBegin(); addDroutine(this, Size); parEnd(); PrintMatrix(c); } } Example: Matrix Multiplication Sample Charlotte Program

Execution Environment • The same Charlotte program runs on: a single machine multiple machines (one user machine and a set of potential volunteer machines) • Interaction among machines solely through Java-capable browsers

Eager Scheduling • Difficulties in a distributed system • Detection of crashed-failed machines • Detection of slow machines • Solution: Eager scheduling • Volunteer machines contact user machine for work • Routines may be assigned to multiple machines • Difficulties with eager scheduling • Inconsistent memory views across routines and different executions of the same routine • Violation of exactly-once semantics • Solution: Two-phase Idempotent Execution Strategy (TIES)

DSM • Why DSM? • Easy to use • Programmer and user transparent • Design objectives • Heterogeneity • Operating system independence • Compiler independence • These require an object-based approach for implementing DSM

Realized at the object level All objects have a unique identifier Identifiers are identically mapped to objects across machines Data is transferred on demand Granularity can be controlled False sharing avoided DSM — Implementation

Experiments • 10 Sun SPARC 5 workstation • 10 MBit/s Ethernet • Application: Ising model • Measured time is wall-clock time • Three tests • Scalability • Load balancing • Transiently available machines

Experiment 1: Scalability

Experiment 2: Load balancing Time Equivalent Machines Speedup

Experiment 3:Transient Availability • Five machines used • After 100 seconds: 1 machine crashed and 1 added • After another 100 seconds: 2 machines crashed and 2 added • 90.18 % efficiency as opposed to 5 reliable machines • 86.25 % efficiency as opposed to sequential execution (95.64 % for 5 reliable machines)

Summary • Charlotte targets the Web • Leverages benefits of Java (security, heterogeneity, widely available, ...) • Seamlessly crosses administration boundaries • Distribution of program and data • DSM with no compiler or OS support

Charlotte: Metacomputing on the Web