Parallel i o optimizations
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Parallel I/O Optimizations. Sources/Credits: R. Thakur, W. Gropp, E. Lusk. A Case for Using MPI's Derived Datatypes to Improve I/O Performance. Supercomputing 98 http://www.cs.dartmouth.edu/pario/bib/short.html (bibliography)

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Parallel I/O Optimizations

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Parallel i o optimizations

Parallel I/O Optimizations

Sources/Credits:

R. Thakur, W. Gropp, E. Lusk. A Case for Using MPI's Derived Datatypes to Improve I/O Performance. Supercomputing 98

http://www.cs.dartmouth.edu/pario/bib/short.html (bibliography)

Xiaosong Ma, Marianne Winslett, Jonghyun Lee, and Shengke Yu. Improving MPI IO output performance with active buffering plus threads. In Proceedings of the International Parallel and Distributed Processing Symposium. IEEE Computer Society Press, April 2003.


High performance with derived data types thakur et al sc 98

High Performance with Derived Data Types (Thakur et. al: SC 98)

  • Potential of parallel file systems not fully utilized because of application’s I/O access patterns

  • Many small requests to non-contiguous blocks

  • Most parallel file systems access single large chunk

  • Thus motivation for making a single call using derived data types

  • ROMIO (MPICH’s I/O) performs 2 optimizations – data sieving and collective I/O


Datatype constructors in mpi

Datatype Constructors in MPI

  • contiguous

  • vector/hvector

  • indexed/hindexed/indexed_block

  • struct

  • subarray

  • darray

I

I

I

I

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I

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I

I

I

I

I

I

I

I

I

I

I

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I

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D

D

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C


Different levels of access

Different levels of access


Different levels of access1

Different levels of access


Different levels of access2

Different levels of access


Optimizations in romio for derived datatype noncontiguous access

Optimizations in ROMIO for derived-datatype noncontiguous access

  • Data sieving

    • Make a few, large contiguous requests to the file system even if the user’s requests consists of several, small, nocontiguous requests

    • Extract (pick out data) in memory that is really needed

    • This is ok for read? For write?

    • Use small buffer for writing with data sieving than for reading with data sieving. Why?

Read-modify-write along with locking

Greater the size of the write buffer, greater the contention among processes for locks


Optimizations in romio for derived datatype noncontiguous access1

Optimizations in ROMIO for derived-datatype noncontiguous access

  • Data sieving

  • Collective I/O

    • During collective-I/O functions, the implementation can analyze and merge the requests of different processes

    • The merged request can be large and continuous although the individual requests were noncontiguous.

    • Perform I/O in 2 phases:

      • I/O phase – processes perform I/O for the merged request. Some data may belong to other processes. If the merged request is not contiguous, use data sieving

      • Communication phase – processes redistribute data to obtain the desired distribution

    • Additional cost of communication phase can be offset by performance gain due to contiguous access.

  • Data sieving and collective-I/O also help improve caching and prefetching in underlying file system


Collective i o illustration

Collective I/O Illustration

P0

P0

P1

P1

P0

P0

P1

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P0

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P0

P1

P0

P1


Active buffering with threads xiaosong ma et al ipdps 2003

Active Buffering with Threads (Xiaosong Ma et al.: IPDPS 2003)

  • Above optimizations alone are not enough.

  • Active Buffering – use of separate I/O nodes

  • Overlapping I/O access with computation by threads

  • Buffer space automatically adjusted to available memory


Original scheme ma ipdps 2002

Original Scheme (Ma: IPDPS 2002)

  • Hierarchical buffering scheme

  • Dedicated I/O server nodes

  • During I/O:

    if(not overflow in compute nodes)

    compute nodes -> local buffers

    else

    if(not overflow in server nodes)

    compute nodes ->server buffers (using MPI)

    else

    server nodes -> I/O system

  • During computation:

    Server nodes clear local buffers and I/O write

    Fetch data from compute nodes (one-sided communication) and I/O write


Current scheme

Current Scheme

  • I/O threads collective I/O overlapped with main threads computation and communication

  • Uses pthreads with kernel-level scheduling

  • Interception of ROMIO’s I/O calls

  • Main threads and I/O threads coordinate by buffer queue

  • Producer-consumer and bounded-buffer problem


Execution timeline

Execution Timeline


Bibliography

Bibliography

  • Philip H. Carns, Walter B. Ligon III, Robert B. Ross, and Rajeev Thakur. PVFS: A parallel file system for linux clusters. In Proceedings of the 4th Annual Linux Showcase and Conference, pages 317-327, Atlanta, GA, October 2000. USENIX Association.

  • Jose Aguilar. A graph theoretical model for scheduling simultaneous I/O operations on parallel and distributed environments. Parallel Processing Letters, 12(1):113-126, March 2002.

  • Rajesh Bordawekar. Implementation of collective I/O in the Intel Paragon parallel file system: Initial experiences. In Proceedings of the 11th ACM International Conference on Supercomputing, pages 20-27. ACM Press, July 1997.

  • Peter Brezany, Marianne Winslett, Denis A. Nicole, and Toni Cortes. Parallel I/O and storage technology. In Proceedings of the Seventh International Euro-Par Conference, volume 2150 of Lecture Notes in Computer Science, pages 887-888, Manchester, UK, August 2001. Springer-Verlag.

  • Bradley Broom, Rob Fowler, and Ken Kennedy. KelpIO: A telescope-ready domain-specific I/O library for irregular block-structured applications. In Proceedings of the First IEEE/ACM International Symposium on Cluster Computing and the Grid, pages 148-155, Brisbane, Australia, May 2001. IEEE Computer Society Press


  • Bibliography1

    Bibliography

    • J. Carretero, F. Pérez, P. de Miguel, F. Garc\'\ia, and L. Alonso. I/O data mapping in \em ParFiSys: support for high-performance I/O in parallel and distributed systems. In Euro-Par '96, volume 1123 of Lecture Notes in Computer Science, pages 522-526. Springer-Verlag, August 1996

      • Ying Chen, Marianne Winslett, Y. Cho, and S. Kuo. Automatic parallel I/O performance optimization using genetic algorithms. In Proceedings of the Seventh IEEE International Symposium on High Performance Distributed Computing, pages 155-162. IEEE Computer Society Press, July 1998.

      • Ying Chen, Ian Foster, Jarek Nieplocha, and Marianne Winslett. Optimizing collective I/O performance on parallel computers: A multisystem study. In Proceedings of the 11th ACM International Conference on Supercomputing, pages 28-35. ACM Press, July 1997.

    • Avery Ching, Alok Choudhary, Kenin Coloma, Wei keng Liao, Robert Ross, and William Gropp. Noncontiguous I/O accesses through MPI-IO. In Proceedings of the Third IEEE/ACM International Symposium on Cluster Computing and the Grid, pages 104-111, Tokyo, Japan, May 2003. IEEE Computer Society Press.

    • Phillip M. Dickens and Rajeev Thakur. Evaluation of collective I/O implementations on parallel architectures. Journal of Parallel and Distributed Computing, 61(8):1052-1076, August 2001.


    Bibliography2

    Bibliography

    • Félix Garcia-Carballeira, Alejandro Calderon, Jesus Carretero, Javier Fernandez, and Jose M. Perez. The design of the Expand parallel file system. The International Journal of High Performance Computing Applications, 17(1):21-38, 2003

      • Sanjay Ghemawat, Howard Gobioff, and Shun-Tak Leung. The Google file system. In Proceedings of the Nineteenth ACM Symposium on Operating Systems Principles, pages 96-108, Bolton Landing, NY, October 2003. ACM Press.

    • James V. Huber, Jr., Christopher L. Elford, Daniel A. Reed, Andrew A. Chien, and David S. Blumenthal. PPFS: A high performance portable parallel file system. In Hai Jin, Toni Cortes, and Rajkumar Buyya, editors, High Performance Mass Storage and Parallel {I/O}: Technologies and Applications, chapter 22, pages 330-343. IEEE Computer Society Press and Wiley, New York, NY, 2001.

    • Meenakshi A. Kandaswamy, Mahmut Kandemir, Alok Choudhary, and David Bernholdt. An experimental evaluation of I/O optimizations on different applications. IEEE Transactions on Parallel and Distributed Systems, 13(7):728-744, July 2002.

    • Mahmut Kandemir. Compiler-directed collective I/O. IEEE Transactions on Parallel and Distributed Systems, 12(12):1318-1331, December 2001.


    Bibliography3

    Bibliography

    • Xiaosong Ma, Marianne Winslett, Jonghyun Lee, and Shengke Yu. Improving MPI IO output performance with active buffering plus threads. In Proceedings of the International Parallel and Distributed Processing Symposium. IEEE Computer Society Press, April 2003.

    • Tara M. Madhyastha and Daniel A. Reed. Learning to classify parallel input/output access patterns. IEEE Transactions on Parallel and Distributed Systems, 13(8):802-813, August 2002.

  • Ethan L. Miller and Randy H. Katz. RAMA: An easy-to-use, high-performance parallel file system. Parallel Computing, 23(4-5):419-446, June 1997.

  • Bill Nitzberg and Virginia Lo. Collective buffering: Improving parallel I/O performance. In Proceedings of the Sixth IEEE International Symposium on High Performance Distributed Computing, pages 148-157, Portland, OR, August 1997. IEEE Computer Society Press.See also later version nitzberg:bcollective.

    • Huseyin Simitci and Daniel Reed. A comparison of logical and physical parallel I/O patterns. The International Journal of High Performance Computing Applications, 12(3):364-380, Fall 1998.


  • Bibliography4

    Bibliography

    • Domenico Talia and Pradip K. Srimani. Parallel data-intensive algorithms and applications. Parallel Computing, 28(5):669-671, May 2002.

    • Len Wisniewski, Brad Smisloff, and Nils Nieuwejaar. Sun MPI I/O: Efficient I/O for parallel applications. In Proceedings of SC99: High Performance Networking and Computing, Portland, OR, November 1999. ACM Press and IEEE Computer Society Press

    • K. K. Lee, M. Kallahalla, B. S. Lee, and P. J. Varman. Performance comparison of prefetching and placement policies for parallel I/O. International Journal of Parallel and Distributed Systems and Networks, 5(2):76-84, 2002.

    • M. Kallahalla and P. J. Varman. PC-OPT: Optimal offline prefetching and caching for parallel I/O systems. IEEE Transactions on Computers, 51(11):1333-1344, November 2002.


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