15627466
Testing parallel random number generators
Monte Carlo computations are considered easy to parallelize. However, the results can be adversely affected by defects in the parallel pseudorandom number generator used. A parallel pseudorandom number generator must be tested for two types of correlations--(i) intra-stream correlation, as for any sequential generator, and (ii) inter-stream correlation for correlations between random number streams on different processes. Since bounds on these correlations are difficult to prove mathematically, large and thorough empirical tests are necessary. Many of the popular pseudorandom number generators in use today were tested when computational power was much lower, and hence they were evaluated with much smaller test sizes.This paper describes several tests of pseudorandom number generators, both statistical and application-based. We show defects in several popular generators. We describe the implementation of these tests in the SPRING [ACM Trans. Math. Software 26 (2000) 436; SPRNG--scalable parallel random number generators. SPRNG 1.0--http://www.ncsa.uiuc.edu/ Apps/SPRNG; SPRNG 2.0--http://sprng.cs.fsu.edu] test suite and also present results for the tests conducted on the SPRNG generators. These generators have passed some of the largest empirical random number tests.
17105049
Data-Driven Time-Parallelization in the AFM Simulation of Proteins
Molecular dynamics is a popular technique to simulate the behavior of physical systems, with resolution at the atomic scale. One of its limitations is that an enormous computational effort is required to simulate to realistic time spans. Conventional parallelization strategies have limited effectiveness in dealing with this difficulty. We recently introduced a more scalable approach to parallelization, where data from prior, related, simulations are used to parallelize a simulation in the time domain. We demonstrated its effectiveness in nano-mechanics simulations. In this paper, we develop our approach so that it can be used in a soft-matter application involving the atomic force microscopy simulation of proteins. We obtain an order of magnitude improvement in performance when we combine time parallelization with conventional parallelization. The significance of this work lies in demonstrating the promise of data-driven time parallelization in soft-matter applications, which are more challenging than the hard-matter applications considered earlier.
14860992
Scalable Time-Parallelization of Molecular Dynamics Simulations in Nano Mechanics
Molecular dynamics (MD) is an important atomistic simulation technique, with widespread use in computational chemistry, biology, and materials. An important limitation of MD is that the time step size is small, requiring a large number of iterations to simulate realistic time spans. Conventional parallelization is not very effective for this. We recently introduced a new approach to parallelization, where data from related prior simulations are used to parallelize a new computation along the time domain. In our prior work, the size of the physical system in the current simulation needed to be identical to that of the prior simulations. The significance of this paper lies in demonstrating a strategy that enables this approach to be used even when the physical systems differ in size. Furthermore, this method scaled up to almost 1000 processors with close to ideal speedup in one case, where conventional methods scale to only 2-3 processors
36878064
A Compact Storage Scheme for Fast Wavelet-Based Subregion Retrieval
In an image browsing environment there is need for progressively viewing image subregions at various resolutions. We describe a storage scheme that accomplishes good image compression, while supporting fast image subregion retrieval. We evaluate analytically and experimentally the compression performance of our algorithm. We also provide results on the speed of the algorithm to demonstrate its effectiveness, and present an extension to a client/server environment.
17897427
Experimental Studies on a Compact Storage Scheme for Wavelet-Based Multiresolution Subregion Retrieval
Wavelet transforms, when combined with quantization and a suitable encoding, can be used to compress images eeectively. In order to use them for image library systems, a compact storage scheme for quantized coeecient wavelet data must be developed with a support for fast subregion retrieval. We have designed such a scheme and in this paper we provide experimental studies to demonstrate that it achieves good image compression ratios, while providing a natural indexing mechanism that facilitates fast retrieval of portions of the image at various resolutions.
16971481
Image compression for fast wavelet-based subregion retrieval
In an image browsing environment there is need for progressively viewing image subregions at various resolutions. We describe a storage scheme that accomplishes good image compression, while supporting fast image subregion retrieval. We evaluate analytically and experimentally the compression performance of our algorithm. We also provide results on the speed of the algorithm to demonstrate its effectiveness, and present an extension to a client/server environment.
4983613
Are Grammatical Representations Useful for Learning from Biological Sequence Data? - A Case Study
This paper investigates whether Chomsky-like grammar representations are useful for learning cost-effective, comprehensible predictors of members of biological sequence families. The Inductive Logic Programming (ILP) Bayesian approach to learning from positive examples is used to generate a grammar for recognising a class of proteins known as human neuropeptide precursors (NPPs). Collectively, five of the co-authors of this paper, have extensive expertise on NPPs and general bioinformatics methods. Their motivation for generating a NPP grammar was that none of the existing bioinformatics methods could provide sufficient cost-savings during the search for new NPPs. Prior to this project experienced specialists at SmithKline Beecham had tried for many months to hand-code such a grammar but without success. Our best predictor makes the search for novel NPPs more than 100 times more efficient than randomly selecting proteins for synthesis and testing them for biological activity. As far as these authors are aware, this is both the first biological grammar learnt using ILP and the first real-world scientific application of the ILP Bayesian approach to learning from positive examples. A group of features is derived from this grammar. Other groups of features of NPPs are derived using other learning strategies. Amalgams of these groups are formed. A recognition model is generated for each amalgam using C4.5 and C4.5rules and its performance is measured using both predictive accuracy and a new cost function, Relative Advantage (RA). The highest RA was achieved by a model which includes grammar-derived features. This RA is significantly higher than the best RA achieved without the use of the grammar-derived features. Predictive accuracy is not a good measure of performance for this domain because it does not discriminate well between NPP recognition models: despite covering varying numbers of (the rare) positives, all the models are awarded a similar (high) score by predictive accuracy because they all exclude most of the abundant negatives.
14812989
Experimental Analysis of Algorithms for Bilateral-Contract Clearing Mechanisms Arising in Deregulated Power Industry
We consider the bilateral contract satisfaction problem arising from electrical power networks due to the proposed deregulation of the electric utility industry in the USA. Given a network and a (multi)set of pairs of vertices (contracts) with associated demands, the goal is to find the maximum number of simultaneously satisfiable contracts. We study how four different algorithms perform in fairly realistic settings; we use an approximate electrical power network from Colorado. Our experiments show that three heuristics outperform a theoretically better algorithm. We also test the algorithms on four types of scenarios that are likely to occur in a deregulated marketplace. Our results show that the networks that are adequate in a regulated marketplace might be inadequate for satisfying all the bilateral contracts in a deregulated industry.
11619174
Provable algorithms for parallel sweep scheduling on unstructured meshes
We present provably efficient parallel algorithms for sweep scheduling on unstructured meshes. Sweep scheduling is a commonly used technique in radiation transport problems, and involves inverting an operator by iteratively sweeping across a mesh. Each sweep involves solving the operator locally at each cell. However, each direction induces a partial order in which this computation can proceed. On a distributed computing system, the goal is to schedule the computation, so that the length of the schedule is minimized. Several heuristics have been proposed for this problem; but none of the heuristics have worst case performance guarantees. We present a simple, almost linear time randomized algorithm which (provably) gives a schedule of length at most O(log/sup 2/n) times the optimal schedule for instances with n cells, when the communication cost is not considered, and a slight variant, which coupled with a much more careful analysis, gives a schedule of (expected) length O(logmlogloglogm) times the optimal schedule for m processors. These are the first such provable guarantees for this problem. We also design a priority based list schedule using these ideas, with the same theoretical guarantee, but much better performance in practice. We complement our theoretical results with extensive empirical analysis. The results show that (i) our algorithm performs very well and has significantly better performance guarantee in practice and (ii) the algorithm compares favorably with other natural and efficient parallel algorithms proposed in the literature [S. Pautz, (2002), S. Plimpton et al., (2001)].
8615000
Fair scheduling of dynamic task systems on multiprocessors
In dynamic real-time task systems, tasks that are subject to deadlines are allowed to join and leave the system. In previous work, Stoica et al. and Baruah et al. presented conditions under which such joins and leaves may occur in fair-scheduled uniprocessor systems without causing missed deadlines. In this paper, we extend their work by considering fair-scheduled multiprocessors. We show that their conditions are sufficient on M processors, under any deadline-based Pfair scheduling algorithm, if the utilization of every subset of M-1 tasks is at most one. Further, for the general case in which task utilizations are not restricted in this way, we derive sufficient join/leave conditions for the PD^2 Pfair algorithm. We also show that, in general, these conditions cannot be improved upon without causing missed deadlines.
44926291
Low Discrepancy Sets Yield Approximate Min-Wise Independent Permutation Families
Motivated by a problem of filtering near-duplicate Web documents, Broder, Charikar, Frieze & Mitzenmacher defined the following notion of e-approximate min-wise independent permutation families [2]. A multiset \(\mathcal{F}\) of permutations of {0,1, ... , n–1} is such a family if for all K ⊆ {0,1, ..., n–1} and any x ∈ K, a permutation π chosen uniformly at random form \(\mathcal{F}\) statisfies