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Programming Distributed Systems with High Level Abstractions

Programming Distributed Systems with High Level Abstractions. Douglas Thain University of Notre Dame 23 October 2008. Distributed Systems. Scale: 2 – 100s – 1000s – millions Domains: Single or Multi Users: 1 – 10 – 100 – 1000 – 10000 Naming: Direct, Virtual

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Programming Distributed Systems with High Level Abstractions

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  1. ProgrammingDistributed Systemswith High Level Abstractions Douglas Thain University of Notre Dame 23 October 2008

  2. Distributed Systems • Scale: 2 – 100s – 1000s – millions • Domains: Single or Multi • Users: 1 – 10 – 100 – 1000 – 10000 • Naming: Direct, Virtual • Scheduling: Timesharing / Space Sharing • Interface: Allocate CPU / Execute Job • Security: None / IP / PKI / KRB … • Storage: Embedded / External

  3. Cloud Computing? • Scale: 2 – 100s – 1000s – 10000s • Domains: Single or Multi • Users: 1 – 10 – 100 – 1000 – 10000 • Naming: Direct, Virtual • Scheduling: Timesharing / Spacesharing • Interface: Allocate CPU / Execute Job • Security: None / IP / PKI / KRB … • Storage: Embedded / External

  4. Grid Computing? • Scale: 2 – 100s – 1000s – 10000s • Domains: Single or Multi • Users: 1 – 10 – 100 – 1000 – 10000 • Naming: Direct, Virtual • Scheduling: Timesharing / Spacesharing • Interface: Allocate CPU / Execute Job • Security: None / IP / PKI / KRB … • Storage: Embedded / External

  5. An Assembly Languageof Distributed Computing • Fundamental Operations • TransferFile( source, destination ) • ExecuteJob( host, exe, input, output ) • AllocateVM( cpu, mem, disk, opsys ) • Semantics of Assembly are Subtle: • When do instructions commit? • Delay slots before control transfers? • What exceptions are valid for each opcode? • Precise or imprecise exceptions? • What is the cost of each instruction?

  6. Programming in Assembly Stinks • You know the problems: • Stack management. • Garbage collection. • Type checking. • Co-location of data and computation. • Query optimizations. • Function shipping or data shipping? • How many nodes should I harness?

  7. Abstractionsfor Distributed Computing • Abstraction: a declarative specification of the computation and data of a workload. • A restricted pattern, not meant to be a general purpose programming language. • Avoid the really terrible cases. • Provide users with a bright path. • Data structures instead of file systems.

  8. All-Pairs Abstraction AllPairs( set A, set B, function F ) returns matrix M where M[i][j] = F( A[i], B[j] ) for all i,j A1 A2 A3 A1 A1 An AllPairs(A,B,F) B1 F F F B1 B1 Bn B2 F F F F B3 F F F Moretti, Bulosan, Flynn, Thain, AllPairs: An Abstraction… IPDPS 2008

  9. F F 0.97 0.05 Example Application • Goal: Design robust face comparison function.

  10. F Similarity Matrix Construction Current Workload: 4000 images 256 KB each 10s per F (five days) Future Workload: 60000 images 1MB each 1s per F (three months)

  11. http://www.cse.nd.edu/~ccl/viz

  12. Try 1: Each F is a batch job. Failure: Dispatch latency >> F runtime. Try 2: Each row is a batch job. Failure: Too many small ops on FS. F F F F F CPU CPU CPU CPU CPU F F F F F F F F F F CPU F CPU F CPU F CPU F CPU F F F F F F HN HN Try 3: Bundle all files into one package. Failure: Everyone loads 1GB at once. Try 4: User gives up and attempts to solve an easier or smaller problem. F F F F F F F F F F CPU F CPU F CPU F CPU F CPU F F F F F F HN Non-Expert User Using 500 CPUs

  13. All-Pairs Abstraction AllPairs( set A, set B, function F ) returns matrix M where M[i][j] = F( A[i], B[j] ) for all i,j A1 A2 A3 A1 A1 An AllPairs(A,B,F) B1 F F F B1 B1 Bn B2 F F F F B3 F F F

  14. What is the right metric? • Speedup? • Seq Runtime / Parallel Runtime • Parallel Efficiency? • Speedup / N CPUs? • Neither works, because the number of CPUs varies over time and between runs. • Cost Efficiency • Work Completed / Resources Consumed • Person-Miles / Gallon • Results / CPU-hours • Results / $$$

  15. All-Pairs Abstraction

  16. Classify Abstraction Classify( T, R, N, P, F ) T = testing set R = training set N = # of partitions F = classifier T1 F V1 T P T2 F V2 C V T3 F V3 R Moretti, Steinhauser, Thain, Chawla, Scaling up Classifiers to Cloud Computers, ICDM 2008.

  17. A1 A2 A3 B1 F F F B2 F F F B3 F F F BXGrid Abstractions S = Select( color=“brown” ) B = Transform( S,F ) M = AllPairs( A, B, F ) eye color S1 L brown F L blue ROC Curve S2 F R brown S3 F R brown Bui, Thomas, Kelly, Lyon, Flynn, Thain BXGrid: A Repository and Experimental Abstraction… in review 2008.

  18. Implementing Abstractions Relational Database (2x) Relational Database S = Select( color=“brown” ) DBMS Active Storage Cluster (16x) B = Transform( S,F ) Condor Pool (500x) CPU CPU CPU CPU M = AllPairs( A, B, F ) CPU CPU CPU CPU

  19. Compatibility of Abstractions? Map-Reduce Classify All-Pairs Assembly Language

  20. Compatibility of Abstractions? ??? All-Pairs Classify Mismatch:Classify partitions logically. MR partitions physically. Mismatch:MR relies on data partition. AP relies on data re-use. Map-Reduce Assembly Language

  21. Dryad Swift More General, Less Optimized? Compatibility of Abstractions? Map-Reduce Classify All-Pairs Assembly Language

  22. Swift Swift Dryad Dryad Map-Reduce Map-Reduce Classify Classify All-Pairs All-Pairs Assembly Language Assembly Language From Clouds to Multicore • Next Step: AP Implementation that runs well on Single CPU, Multicore, Cloud, or Cloud of Multicores. CPU CPU CPU CPU CPU CPU CPU CPU $$$ $$$ $$$ $$$ RAM

  23. Acknowledgments • Cooperative Computing Lab • http://www.cse.nd.edu/~ccl • Grad Students: • Chris Moretti • Hoang Bui • Michael Albrecht • Li Yu • NSF Grants CCF-0621434, CNS-0643229 • Undergraduate Students • Mike Kelly • Rory Carmichael • Mark Pasquier • Christopher Lyon • Jared Bulosan

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