Massively parallel GPU computing for fast stereo correspondence algorithms

Current accurate stereo matching algorithms employ some key techniques that are not suitable for parallel GPU architecture. It will be tricky and cumbersome to directly take these techniques into GPU applications. Trying to tackle this difficulty, we design two GPU-based stereo matching algorithms, one using a local fixed aggregation window whose size is configurable, and the other using an adaptive aggregation window which only includes necessary pixels. We use the winner-takes-all (WTA) principle for optimization and a plain voting refinement for post-processing; both do not need complex data structures. We aim to implement on GPU platforms fast stereo matching algorithms that produce results with same-level quality as other WTA local dense methods that use window-based cost aggregation. In our GPU-based implementation of the fixed window partially demosaiced CFA stereo matching application, accelerations up to 20 times are obtained for large size images. In our GPU-based implementation of the adaptive window color stereo matching application, experiment results show that it can handle four pairs of standard images from Middlebury database within roughly 100 ms.     

Optimal parallel algorithms for computing convex hulls and for sorting

Nonlinear equations are considered, where some input parameters are subjected to errors. By a class of monotone enclosing methods sequences of intervals are constructed, containing for each value of the perturbation parameter at least one zero of the problem. In finite dimensional spaces concrete realizations are given, e. g. of Newton-, Regula falsi- and Jacobi-Newton-type.     

基于GPU的复杂网络社区挖掘算法并行计算

由于复杂网络的规模越来越大, 在大规模的复杂网络中快速、准确地挖掘出隐藏的社区结构是当前该领域研究的热点问题。目前社区结构挖掘常用的基于快速Newman算法的社区结构挖掘算法之一是一般概率框架方法。以规模日益增大的复杂网络为研究对象, 提出了基于GPGPU的一般概率框架并行算法, 有效地解决了在大规模的复杂网络中快速、准确地挖掘出隐藏的社区结构问题。实验证明, 随着节点数的增加, 该并行算法在不损失准确性的前提下运行效率有所提高, 为复杂网络社区结构挖掘的研究提供了一种高效的解决方案。     

Multisite co-allocation scheduling algorithms for parallel jobs in computing grid environments

Cooperation of multi-domain massively parallel processor systems in com- puting grid environment provides new opportunities for multisite job scheduling. At the same time, in the area of co-allocation, heterogeneity, network adaptability and scalability raise the challenge for the international design of multisite job scheduling models and algorithms. It presents multisite job scheduling schema through the introduction of mul- tisite job scheduling model and the performance model under the grid environment. It introduces two job multisite and cooperative scheduling models and algorithms with the core of the optimal and greedy-heuristic resource selection strategies. Meanwhile, com- pared with single and multisite cooperative scheduling models and algorithms introduced by Sabin, Yahyapour and other persons, the validity and advance of the scheduling model and the performance model herein are proved.     

Ring, torus and hypercube architectures/algorithms for parallel computing

This paper provides a survey of both architectural and algorithmic aspects of solving problems using parallel processors with ring, torus and hypercube interconnection.     

Efficient parallel algorithms for computing all pair shortest paths in directed graphs

We present parallel algorithms for computing all pair shortest paths in directed graphs. Our algorithm has time complexityO(f(n)/p+I(n)logn) on the PRAM usingp processors, whereI(n) is logn on the EREW PRAM, log logn on the CCRW PRAM,f(n) iso(n ³). On the randomized CRCW PRAM we are able to achieve time complexityO(n ³/p+logn) usingp processors. A preliminary version of this paper was presented at the 4th Annual ACM Symposium on Parallel Algorithms and Architectures, June 1992. Support by NSF Grant CCR 90-20690 and PSC CUNY Awards #661340 and #662478.     

Parallel bioinspired algorithms for NP complete graph problems

It is no longer believed that DNA computing will outperform digital computers when it comes to the computation of intractable problems. In this paper, we emphasise the in silico implementation of DNA-inspired algorithms as the only way to compete with other algorithms for solving NP-complete problems. For this, we provide sticker algorithms for some of the most representative NP-complete graph problems. The simple data structures and bit-vertical operations make them suitable for some parallel architectures. The parallel algorithms might solve either moderate-size problems in an exact manner or, when combined with a heuristic, large problems in polynomial time.     

网格计算

<正> 网格计算远远超越了纯粹的计算。从技术上讲,网格计算能够构建系统映像来虚拟化分布式计算和数据资源.例如处理能力、网络带宽和存储容量,为用户和应用系统提供对大量IT功能的无缝访问。一位网格用户实际上只看到一个单一的大型虚拟计算机。     

Designing for parallel fuzzy computing

As the number of fuzzy logic applications increases, demand for faster architectures will grow. Our design for a VLSI fuzzy processor uses fuzzy inference techniques that optimize processing time. Preprocessing that reduces the number of rules to be processed, parallel computation of active rule degrees of activation, and scalability are major features of this architecture. The journal issue contains a concise summary of this article. The complete article is linked to Micro's home page on the World Wide Web (http://www.computer.org/pubs/micro/micro.htm)     

Synchronization transformations for parallel computing

This article describes a framework for synchronization optimizations and a set of transformations for programs that implement critical sections using mutual exclusion locks. The basic synchronization transformations take constructs that acquire and release locks and move these constructs both within and between procedures. They also eliminate, acquire and release constructs that use the same lock and are adjacent in the program. The article also presents a synchronization optimization algorithm, lock elimination, that uses these transformations to reduce the synchronization overhead. This algorithm locates computations that repeatedly acquire and release the same lock, then transforms the computations so that they acquire and release the lock only once. The goal of this algorithm is to reduce the lock overhead by reducing the number of times that computations acquire and release locks. But because the algorithm also increases the sizes of the critical sections, it may decrease the amount of available concurrency. The algorithm addresses this trade-off by providing several different optimization policies. The policies differ in the amount by which they increase the sizes of the critical sections. Experimental results from a parallelizing compiler for object-based programs illustrate the practical utility of the lock elimination algorithm. For three benchmark applications, the algorithm can dramatically reduce the number of times the applications acquire and release locks, which significantly reduces the amount of time processors spend acquiring and releasing locks. The resulting overall performance improvements for these benchmarks range from no observable improvement to up to 30% performance improvement. Copyright © 1999 John Wiley & Sons, Ltd.     

Line-drawing algorithms for parallel machines

The fact that conventional line-drawing algorithms, when applied directly on parallel machines, can lead to very inefficient codes is addressed. It is suggested that instead of modifying an existing algorithm for a parallel machine, a more efficient implementation can be produced by going back to the invariants in the definition. Popular line-drawing algorithms are compared with two alternatives; distance to a line (a point is on the line if sufficiently close to it) and intersection with a line (a point on the line if an intersection point). For massively parallel single-instruction-multiple-data (SIMD) machines (with thousands of processors and up), the alternatives provide viable line-drawing algorithms. Because of the pixel-per-processor mapping, their performance is independent of the line length orientation     

Environments for prototyping parallel algorithms

The current ferment in parallel computer architecture has exacerbated the already large problems in developing and testing parallel algorithms. Although only experimentation will identify effective designs, the variety of computing paradigms has precluded development of the (necessary) general environment for parallel programming. We propose an environment design system where design specification is divorced from implementation. Because the design system, Polylith, provides the communication structure of a parallel computation, the computation's individual tasks can be written in one of several relatively portable sequential languages.     

计算最大积实例的新算法

最大积实例包括最大可能解释(MPE)和最大后验估计(MAP),它们是贝叶斯网络的基本问题.针对经典算法求最大积实例的时间复杂度高,提出新算法来求解该问题.该算法将求贝叶斯网络的最大积实例问题转变成一组一元一次方程,而一元一次方程很容易求解;通过临时表来缓存计算最大积概率时的中间结果,而这些临时表可以用来优化计算最大积实例而不需要过多的额外空间开销,并能够在贝叶斯查询之间共享.通过实验证实该算法计算贝叶斯网络实例时的高效性,在计算最大积实例时的有效性.     

Cost-effective parallel computing

We consider how large memories can make parallel computing cost-effective even with modest speedups. As a concrete example, we use 1994 Silicon Graphics (SGI) prices to show that actual costups can be far less than linear for systems with hundreds of Mbytes of main memory. We consider hardware costs but not software costs, since we do not know how to noncontroversially measure the latter     

Occam algorithms for computing visual motion

The standard approach to computing motion relies on pixel correspondence. Computational schemes impose additional constraints, such as smoothness and continuity of the motion vector even though these are not directly related to pixel correspondence. This paper proposes an alternative to the multiple constraints approach. By drawing analogy with machine learning, motion is computed as a function that accurately predicts frames. The Occam-Razor principle suggests that among all functions that accurately predict the second frame from the first frame, the best predictor is the “simplest,” and simplicity can be rigorously defined in terms of encoding length. An implementation of a practical algorithm is described. Experiments with real video sequences verify the algorithm assumptions by showing that motion in typical sequences can be accurately described in terms of a few parameters. Our particular choice of predictors produces results that compare very favorably with other image flow algorithms in terms of accuracy and compactness. It may, however, be too constrained to enable accurate recovery of 3D motion and structure     

Decomposable algorithms for nearest neighbor computing

Privacy and security concerns can prevent sharing of data, derailing many data projects. Distributed knowledge computing, if done correctly, can alleviate this problem. The key is to obtain valid results, while providing guarantees on the (non)disclosure of data. In the present study, two novel algorithms have been developed to find the nearest neighbor from known points, and a pair of nearest neighbors in a given set data points in d$d$-dimensional space in distributed databases. These algorithms preserve the privacy and security of the data at individual sites by requiring transmission of only minimal information to other sites.