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.

/pdf/characterizing-and-predicting-the-i-o-performance-of-hpc-3xz1dyunac.pdf

Characterizing and predicting the I/O performance of HPC applications using a parameterized synthetic benchmark

PREFACE MODELS Evolving Computational Systems S.G. Akl Decomposable BSP: A Bandwidth-Latency Model for Parallel and Hierarchical Computation G. Bilardi, A. Pietracaprina, and G. Pucci Membrane Systems: A "Natural" Way of Computing with Cells O.H. Ibarra and A. Paun Optical Transpose Systems: Models and Algorithms C.-F. Wang and S. Sahni Models for Advancing PRAM and Other Algorithms into Parallel Programs for a PRAM-On-Chip Platform U. Vishkin, G. Caragea, and B. Lee Deterministic and Randomized Sorting Algorithms for the Parallel Disks Model S. Rajasekaran A Programming Model and Architectural Extensions for Fine-Grain Parallelism A. Gontmakher, A. Mendelson, A. Schuster, and G. Shklover Computing with Mobile Agents in Distributed Networks E. Kranakis, D. Krizanc, and S. Rajsbaum Transitional Issues: Fine-Grain to Coarse-Grain Multicomputers S. Olariu Distributed Computing in the Presence of Mobile Faults N. Santoro and P. Widmayer A Hierarchical Performance Model for Reconfigurable Computers R. Scorfano and V.K. Prasanna Hierarchical Performance Modeling and Analysis of Distributed Software Systems R.A. Ammar Randomized Packet Routing, Selection, and Sorting on the POPS Network J. Davila and S. Rajasekaran Dynamic Reconfiguration on the R-Mesh R. Vaidyanathan and J.L. Trahan Fundamental Algorithms on the Reconfigurable Mesh K. Nakano Reconfigurable Computing with Optical Buses A.G. Bourgeois ALGORITHMS Distributed Peer-to-Peer Data Structures M.T. Goodrich and M.J. Nelson Parallel Algorithms via the Probabilistic Method L. Kliemann and A. Srivastav Broadcasting on Networks of Workstations S. Khuller, Y.-A. Kim, and Y.-C. Wan Atomic Selfish Routing in Networks: A Survey S. Kontogiannis and P. Spirakis Scheduling in Grid Environments Y-C. Lee and A.Y. Zomaya QoS Scheduling in Network and Storage Systems P.J. Varman and A. Gulati Optimal Parallel Scheduling Algorithms in WDM Packet Interconnects Y. Yang Online Real-Time Scheduling Algorithms for Multiprocessor Systems M.A. Palis Parallel Algorithms for Maximal Independent Set and Maximal Matching Y. Han Efficient Parallel Graph Algorithms for Shared-Memory Multiprocessors D.A. Bader and G. Cong Parallel Algorithms for Volumetric Surface Construction J. JaJa, Q. Shi, and A. Varshney Mesh-Based Parallel Algorithms for Ultra-Fast Computer Vision S. Olariu Prospectus for a Dense Linear Algebra Software Library J. Demmel and J. Dongarra Parallel Algorithms on Strings W. Rytter Design of Multithreaded Algorithms for Combinatorial Problems D.A. Bader, K. Madduri, G. Cong, and J. Feo Parallel Data Mining Algorithms for Association Rules and Clustering J. Li, Y. Liu, W.-K. Liao, and A. Choudhary An Overview of Mobile Computing Algorithmics S. Olariu and A.Y. Zomaya APPLICATIONS Using FG to Reduce the Effect of Latency in Parallel Programs Running on Clusters T.H. Cormen and E.R. Davidson High-Performance Techniques for Parallel I/O A. Ching, K. Coloma, A. Choudhary, and W.-K. Liao Message Dissemination Using Modern Communication Primitives T. Gonzalez Online Computation in Large Networks S. Albers Online Call Admission Control in Wireless Cellular Networks I. Caragiannis, C. Kaklamanis, and E. Papaioannou Minimum Energy Communication in Ad Hoc Wireless Networks I. Caragiannis, C. Kaklamanis, and P. Kanellopoulos Power Aware Mapping of Real-Time Tasks to Multiprocessors D. Zhu, B.R. Childers, D. Mosse, and R. Melhem Perspectives on Robust Resource Allocation for Heterogeneous Parallel and Distributed Systems S. Ali, H.J. Siegel, and A.A. Maciejewski A Transparent Distributed Runtime for Java M. Factor, A. Schuster, and K. Shagin Scalability of Parallel Programs A. Grama and V. Kumar Spatial Domain Decomposition Methods in Parallel Scientific Computing Sudip Seal and Srinivas Aluru Game Theoretical Solutions for Data Replication in Distributed Computing Systems S.U. Khan and I. Ahmad Effectively Managing Data on a Grid C.L. Ruby and R. Miller Fast and Scalable Parallel Matrix Multiplication and Its Applications on Distributed Memory Systems K. Li INDEX

/pdf/handbook-of-parallel-computing-models-algorithms-and-5e8x1o9xtn.pdf

Handbook of Parallel Computing: Models, Algorithms, and Applications

A cluster of data servers and a parallel file system are often used to provide high-throughput I/O service to parallel programs running on a compute cluster. To exploit I/O parallelism parallel file systems stripe file data across the data servers. While this practice is effective in serving asynchronous requests, it may break individual program's spatial locality, which can seriously degrade I/O performance when the data servers concurrently serve synchronous requests from multiple I/O-intensive programs. In this paper we propose a scheme, IOrchestrator, to improve I/O performance of multi-node storage systems by orchestrating I/O services among programs when such inter-data-server coordination is dynamically determined to be cost effective. We have implemented IOrchestrator in the PVFS2 parallel file system. Our experiments with representative parallel benchmarks show that IOrchestrator can significantly improve I/O performance-- by up to a factor of 2.5--delivered by a cluster of data servers servicing concurrently-running parallel programs. Notably, we have not observed any scenarios in which the use of IOrchestrator causes substantial performance degradation.

/pdf/iorchestrator-improving-the-performance-of-multi-node-i-o-3xb2vyrdvv.pdf

IOrchestrator: Improving the Performance of Multi-node I/O Systems via Inter-Server Coordination

Methods for describing structured data are a key aid in application development. The MPI standard defines a system for creating “MPI types” at run time and using these types when passing messages, performing RMA operations, and accessing data in files. Similar capabilities are available in other middleware. Unfortunately many implementations perform poorly when processing these structured data types. This situation leads application developers to avoid these components entirely, instead performing any necessary data processing by hand.

/pdf/implementing-fast-and-reusable-datatype-processing-46n7xqv06w.pdf

Implementing Fast and Reusable Datatype Processing

Summary form only given. In this paper, a systematic study of two main types of approach for MPI datatype communication (pack/unpack-based approaches and copy-reduced approaches) is carried out on the InfiniBand network. We focus on overlapping packing, network communication, and unpacking in the pack/unpack-based approaches. We use RDMA operations to avoid packing and/or unpacking in the copy-reduced approaches. Four schemes (buffer-centric segment pack/unpack, RDMA write gather with unpack, pack with RDMA read scatter, and multiple RDMA writes have been proposed. Three of them have been implemented and evaluated based on one MPI implementation over InfiniBand. Performance results of a vector microbenchmark demonstrate that latency is improved by a factor of up to 3.4 and bandwidth by a factor of up to 3.6 compared to the current datatype communication implementation. Collective operations like MPI Alltoall are demonstrated to benefit. A factor of up to 2.0 improvement has been seen in our measurements of those collective operations on an 8-node system.

High performance implementation of MPI derived datatype communication over InfiniBand

Parallel scientific applications store and retrieve very large, structured datasets. Directly supporting these structured accesses is an important step in providing high-performance I/O solutions for these applications. High-level interfaces such as HDF5 and Parallel netCDF provide convenient APIs for accessing structured datasets, and the MPI-IO interface also supports efficient access to structured data. However, parallel file systems do not traditionally support such access. In this work we present an implementation of structured data access support in the context of the parallel virtual file system (PVFS). We call this support "datatype I/O" because of its similarity to MPI datatypes. This support is built by using a reusable datatype-processing component from the MPICH2 MPI implementation. We describe how this component is leveraged to efficiently process structured data representations resulting from MPI-IO operations. We quantitatively assess the solution using three test applications. We also point to further optimizations in the processing path that could be leveraged for even more efficient operation.

/pdf/efficient-structured-data-access-in-parallel-file-systems-2hln3vfo54.pdf

Efficient structured data access in parallel file systems

The MPI Standard supports derived datatypes, which allow users to describe noncontiguous memory layout and communicate noncontiguous data with a single communication function. This feature enables an MPI implementation to optimize the transfer of noncontiguous data. In practice, however, few MPI implementations implement derived datatypes in a way that performs better than what the user can achieve by manually packing data into a contiguous buffer and then calling an MPI function. In this paper, we present a technique for improving the performance of derived datatypes by automatically using packing algorithms that are optimized for memory-access cost. The packing algorithms use memory-optimization techniques that the user cannot apply easily without advanced knowledge of the memory architecture. We present performance results for a matrix-transpose example that demonstrate that our implementation of derived datatypes significantly outperforms both manual packing by the user and the existing derived-datatype code in the MPI implementation (MPICH).

/pdf/improving-the-performance-of-mpi-derived-datatypes-by-4a5z830gl5.pdf

Improving the performance of MPI derived datatypes by optimizing memory-access cost

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Efficient structured data access in parallel file systems

Improving the performance of MPI derived datatypes by optimizing memory-access cost