369805
Flexible and efficient parallel I/O for large-scale multi-component simulations
In this paper, we discuss our experience of providing high performance parallel I/O for a large-scale, on-going, multi-disciplinary simulation project for solid propellant rockets. We describe the performance and data management issues observed in this project and present our solutions, including (1) support for relatively fine-grained distribution of irregular datasets in parallel I/O, (2) a flexible data management facility for inter-module communication, and (3) two schemes to overlap computation with I/O. Performance results obtained from both development and production platforms show that our I/O optimizations can dramatically reduce the simulation's visible I/O cost, as well as the number of disk files, and significantly improve the overall performance. Meanwhile, our data management facility helps to provide simulation developers with simple user interfaces for parallel I/O.
10862904
Improving MPI-IO output performance with active buffering plus threads
Efficient collective output of intermediate results to secondary storage becomes more and more important for scientific simulations as the gap between processing power/interconnection bandwidth and the I/O system bandwidth enlarges. Dedicated servers can offload I/O from compute processors and shorten the execution time, but it is not always possible or easy for an application to use them. We propose the use of active buffering with threads (ABT) for overlapping I/O with computation efficiently and flexibly without dedicated I/O servers. We show that the implementation of ABT in ROMIO, a popular implementation of MPI-IO, greatly reduces the application-visible cost of ROMIO's collective write calls, and improves an application's overall performance by hiding I/O cost and saving implicit synchronization overhead from collective write operations. Further, ABT is high-level, platform-independent, and transparent to users, giving users the benefit of overlapping I/O with other processing tasks even when the file system or parallel I/O library does not support asynchronous I/O.
514517
Efficient data access for parallel BLAST
Searching biological sequence databases is one of the most routine tasks in computational biology. This task is significantly hampered by the exponential growth in sequence database sizes. Recent advances in parallelization of biological sequence search applications have enabled bioinformatics researchers to utilize high-performance computing platforms and, as a result, greatly reduce the execution time of their sequence database searches. However, existing parallel sequence search tools have been focusing mostly on parallelizing the sequence alignment engine. While the computation-intensive alignment tasks become cheaper with larger machines, data-intensive initial preparation and result merging tasks become more expensive. Inefficient handling of input and output data can easily create performance bottlenecks even on supercomputers. It also causes a considerable data management overhead. In this paper, we present a set of techniques for efficient and flexible data handling in parallel sequence search applications. We demonstrate our optimizations through improving mpiBLAST, an open-source parallel BLAST tool rapidly gaining popularity. These optimization techniques aim at enabling flexible database partitioning, reducing I/O by caching small auxiliary files and results, enabling parallel I/O on shared files, and performing scalable result processing protocols. As a result, we reduce mpiBLAST users' operational overhead by removing the requirement of pre-partitioning databases. Meanwhile, our experiments show that these techniques can bring by an order of magnitude improvement to both the overall performance and scalability of mpiBLAST.
7609568
Mining Maximal Correlated Member Clusters in High Dimensional Database
Mining high dimensional data is an urgent problem of great practical importance. Although some data mining models such as frequent patterns and clusters have been proven to be very successful for analyzing very large data sets, they have some limitations. Frequent patterns are inadequate to describe the quantitative correlations among nominal members. Traditional cluster models ignore distances of some pairs of members, so a pair of members in one big cluster may be far away. As a combination and complementary of both techniques, we propose the Maximal-Correlated-Member-Cluster (MCMC) model in this paper. The MCMC model is based on a statistical measure reflecting the relationship of nominal variables, and every pair of members in one cluster satisfy unified constraints. Moreover, in order to improve algorithm's efficiency, we introduce pruning techniques to reduce the search space. In the first phase, a Tri-correlation inequation is used to eliminate unrelated member pairs, and in the second phase, an Inverse-Order-Enumeration-Tree (IOET) method is designed to share common computations. Experiments over both synthetic datasets and real life datasets are performed to examine our algorithm's performance. The results show that our algorithm has much higher efficiency than the naive algorithm, and this model can discover meaningful correlated patterns in high dimensional database.
2357877
Edge-based rich representation for vehicle classification
In this paper, we propose an approach to vehicle classification under a mid-field surveillance framework. We develop a repeatable and discriminative feature based on edge points and modified SIFT descriptors, and introduce a rich representation for object classes. Experimental results show the proposed approach is promising for vehicle classification in surveillance videos despite great challenges such as limited image size and quality and large intra-class variations. Comparisons demonstrate the proposed approach outperforms other methods