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Problem

Problem. Parallelize (serial) applications that use files. Examples: compression tools, logging utilities, databases. In general applications that use files depend on sequential output, serial append is the usual file I/O operation. Goal: perform file I/O operations in parallel,

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Problem

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  1. Problem • Parallelize (serial) applications that use files. • Examples: compression tools, logging utilities, databases. • In general • applications that use files depend on sequential output, • serial append is the usual file I/O operation. • Goal: • perform file I/O operations in parallel, • keep the sequential, serial append of the file.

  2. Results • Cilkruntime-support for serial append with good scalability. • Three serial append schemes and implementations for Cilk: • ported Cheerio, previous parallel file I/O API (M. Debergalis), • simple prototype (with concurrent Linked Lists), • extension, more efficient data structure (concurrent double-linked Skip Lists). • Parallel bz2 using PLIO.

  3. Single Processor Serial Append FILE (serial append) computation DAG 1 7 8 11 12 9 10 2 5 6 3 4

  4. Single Processor Serial Append FILE (serial append) 1 2 3 computation DAG 1 7 8 11 12 9 10 2 5 6 3 4

  5. Single Processor Serial Append FILE (serial append) 1 2 3 4 5 6 7 computation DAG 1 7 8 11 12 9 10 2 5 6 3 4

  6. Single Processor Serial Append FILE (serial append) 1 2 3 4 5 6 7 8 9 10 11 12 computation DAG 1 7 8 11 12 9 10 2 5 6 3 4

  7. Single Processor Serial Append FILE (serial append) 1 2 3 4 5 6 7 8 9 10 11 12 Why not in parallel?! computation DAG 1 7 8 11 12 9 10 2 5 6 3 4

  8. ParalleL file I/O (PLIO) support for Serial Append in Cilk Alexandru Caracaş Fast Serial Append

  9. Outline • Example • single processor & multiprocessor • Semantics • view of Cilk Programmer • Algorithm • modification of Cilk runtime system • Implementation • Previous work • Performance • Comparison

  10. Multiprocessor Serial Append FILE (serial append) computation DAG 1 7 8 11 12 9 10 2 5 6 3 4

  11. Multiprocessor Serial Append FILE (serial append) 1 2 7 computation DAG 1 7 8 11 12 9 10 2 5 6 3 4

  12. Multiprocessor Serial Append FILE (serial append) 1 2 3 5 7 8 9 computation DAG 1 7 8 11 12 9 10 2 5 6 3 4

  13. Multiprocessor Serial Append FILE (serial append) 6 1 2 3 4 5 7 8 9 10 computation DAG 1 7 8 11 12 9 10 2 5 6 3 4

  14. Multiprocessor Serial Append FILE (serial append) 1 2 3 4 5 6 7 8 9 10 11 12 computation DAG 1 7 8 11 12 9 10 2 5 6 3 4

  15. File Operations • open (FILE, mode) / close (FILE). • write (FILE, DATA, size) • processor writes to its PION. • read (FILE, BUFFER, size) • processor reads from PION. • Note: a seek operation may be required • seek (FILE, offset, whence) • processor searches for the right PION in the ordered data structure

  16. Semantics • View of Cilk programmer: • Write operations • preserve the sequential, serial append. • Read and Seek operations • can occur only after the file has been closed, • or on a newly opened file.

  17. Approach (for Cilk) • Bookkeeping (to reconstruct serial append) • Divide execution of the computation, • Meta-Data (PIONs) about the execution of the computation. • Observation • In Cilk,steals need to be accounted for during execution. • Theorem • expected # of steals = O ( PT∞ ). • Corollary (see algorithm) • expected # of PIONs = O ( PT∞ ).

  18. PION (Parallel I/ONode) • Definition: a PION represents all the write operations to a file performed by a processor in between 2 steals. • A PION contains: • # data bytes written, • victim processor ID, • pointer to written data. π1 π3 π2 π4 PION 1 2 3 4 5 6 7 8 9 10 11 12 FILE

  19. Algorithm • All PIONSs are kept in an ordered data structure. • very simple Example: Linked List. • On each steal operation performed by processor Pi from processor Pj: • create a new PION πi, • attach πi immediately after πj, the PION of Pj in the order data structure. PIONs πj π1 πk

  20. Algorithm • All PIONSs are kept in an ordered data structure. • very simple Example: Linked List. • On each steal operation performed by processor Pi from processor Pj: • create a new PION πi, • attach πi immediately after πj, the PION of Pj in the order data structure. πi PIONs πj π1 πk

  21. Algorithm • All PIONSs are kept in an ordered data structure. • very simple Example: Linked List. • On each steal operation performed by processor Pi from processor Pj: • create a new PION πi, • attach πi immediately after πj, the PION of Pj in the order data structure. PIONs π1 πj πk πi

  22. Implementation • Modified the Cilkruntime system to support desired operations. • implemented hooks on the steal operations. • Initial implementation: • concurrent Linked List (easier algorithms). • Final implementation: • concurrent double-linked Skip List. • Ported Cheerio to Cilk 5.4.

  23. Details of Implementation • Each processor has a buffer for the data in its own PIONs • implemented as a file. • Data structure to maintain the order of PIONs: • Linked List, Skip List. • Meta-Data (order maintenance structure of PIONs) • kept in memory, • saved to a file when serial append file is closed.

  24. Skip List • Similar performance with search trees: • O ( log (SIZE) ). NIL NIL NIL NIL

  25. Double-Linked Skip List • Based on Skip Lists (logarithmic performance). • Cilk runtime-support in advanced implementation of PLIO as rank order statistics. NIL NIL NIL NIL

  26. PLIO Performance • no I/O vs writing 100MB with PLIO (w/ linked list), • Tests were run on yggdrasil a 32 proc Origin machine. • Parallelism=32, • Legend: • black: no I/O, • red: PLIO.

  27. Improvements & Conclusion • Possible Improvements: • Optimization of algorithm: • delete PIONs with no data, • cache oblivious Skip List, • File system support, • Experiment with other order maintenance data structures: • B-Trees. • Conclusion: • Cilk runtime-support for parallel I/O • allows serial applications dependent on sequential output to be parallelized.

  28. References • Robert D. Blumofe and Charles E. Leiserson. Scheduling multithreaded computations by work stealing. In Proceedings of the 35th Annual Symposium on Foundations of Computer Science, pages 356-368, Santa Fe, New Mexico, November 1994. • Matthew S. DeBergalis. A parallel file I/O API for Cilk. Master's thesis, Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, June 2000. • William Pugh. Concurrent Maintenance of Skip Lists. Departments of Computer Science, University of Maryland, CS-TR-2222.1, June, 1990.

  29. References • Thomas H. Cormen, Charles E. Leiserson, Donald L. Rivest and Clifford Stein. Introduction to Algorithms (2nd Edition). MIT Press. Cambridge, Massachusetts, 2001. • Supercomputing Technology Group MIT Laboratory for Computer Science. Cilk 5.3.2 Reference Manual, November 2001. Available at http://supertech.lcs.mit.edu/cilk/manual-5.3.2.pdf. • bz2 source code. Available at http://sources.redhat.com/bzip2.

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