PPCproject.bib

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@InProceedings{Fu2010,
  Title                    = {Scalable parallel {I/O} alternatives for massively parallel partitioned solver systems},
  Author                   = {Jing Fu and Ning Liu and Sahni, O. and Jansen, K.E. and Shephard, M.S. and Carothers, C.D.},
  Booktitle                = {Parallel Distributed Processing, Workshops and Phd Forum (IPDPSW), 2010 IEEE International Symposium on},
  Year                     = {2010},
  Pages                    = {1-8},

  Abstract                 = {With the development of high-performance computing, I/O issues have become the bottleneck for many massively parallel applications. This paper investigates scalable parallel I/O alternatives for massively parallel partitioned solver systems. Typically such systems have synchronized ¿loops¿ and will write data in a well defined block I/O format consisting of a header and data portion. Our target use for such an parallel I/O subsystem is checkpoint-restart where writing is by far the most common operation and reading typically only happens during either initialization or during a restart operation because of a system failure. We compare four parallel I/O strategies: 1 POSIX File Per Processor (1PFPP), a synchronized parallel IO library (syncIO), ¿Poor-Man's¿ Parallel I/O (PMPIO) and a new ¿reduced blocking¿ strategy (rbIO). Performance tests using real CFD solver data from PHASTA (an unstructured grid finite element Navier-Stokes solver) show that the syncIO strategy can achieve a read bandwidth of 6.6GB/Sec on Blue Gene/L using 16K processors which is significantly faster than 1PFPP or PMPIO approaches. The serial ¿token-passing¿ approach of PMPIO yields a 900 MB/sec write bandwidth on 16K processors using 1024 files and 1PFPP achieves 600 MB/sec on 8K processors while the ¿reduced-blocked¿ rbIO strategy achieves an actual writing performance of 2.3GB/sec and perceived/latency hiding writing performance of more than 21,000 GB/sec (i.e., 21TB/sec) on a 32,768 processor Blue Gene/L.},
  Doi                      = {10.1109/IPDPSW.2010.5470887}
}

@InProceedings{Fu2011,
  Title                    = {Parallel {I/O} Performance for Application-Level Checkpointing on the {B}lue {G}ene/{P} System.},
  Author                   = {Fu, Jing and Min, Misun and Latham, Robert and Carothers, Christopher D.},
  Booktitle                = {{CLUSTER}},
  Year                     = {2011},
  Pages                    = {465-473},

  Doi                      = {10.1109/CLUSTER.2011.81}
}

@Article{Hedges2005,
  Title                    = {Parallel File System Testing for the Lunatic Fringe: The Care and Feeding of Restless {I/O} Power Users},
  Author                   = {Richard Hedges and Bill Loewe and Tyce McLarty and Chris Morrone},
  Journal                  = {Mass Storage Systems and Technologies, IEEE / NASA Goddard Conference on},
  Year                     = {2005},
  Pages                    = {3-17},
  Volume                   = {0},

  __markedentry            = {[odin:]},
  Doi                      = {10.1109/MSST.2005.22}
}

@InProceedings{Shan2008,
  Title                    = {Characterizing and predicting the {I/O} performance of {HPC} applications using a parameterized synthetic benchmark},
  Author                   = {Hongzhang Shan and Antypas, K. and Shalf, J.},
  Booktitle                = {High Performance Computing, Networking, Storage and Analysis, 2008. SC 2008. International Conference for},
  Year                     = {2008},
  Month                    = {Nov},
  Pages                    = {1-12},

  Abstract                 = {The unprecedented parallelism of new supercomputing platforms poses tremendous challenges to achieving scalable performance for I/O intensive applications. Performance assessments using traditional I/O system and component benchmarks are difficult to relate back to application I/O requirements. However, the complexity of full applications motivates development of simpler synthetic I/O benchmarks as proxies to the full application. In this paper we examine the I/O requirements of a range of HPC applications and describe how the LLNL IOR synthetic benchmark was chosen as suitable proxy for the diverse workload. We show a procedure for selecting IOR parameters to match the I/O patterns of the selected applications and show it can accurately predict the I/O performance of the full applications. We conclude that IOR is an effective replacement for full-application I/O benchmarks and can bridge the gap of understanding that typically exists between stand-alone benchmarks and the full applications they intend to model.},
  Doi                      = {10.1109/SC.2008.5222721}
}

@InProceedings{Shan2007,
  Title                    = {Using {IOR} to Analyze the {I/O} Performance for {HPC} Platforms},
  Author                   = {Hongzhang Shan and John Shalf},
  Booktitle                = {In: {C}ray User Group Conference ({CUG}’07},
  Year                     = {2007}
}