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Scheduling tasks with dflerent weights in the imprecise computation model is rather dflcult. Each task in the imprecise computation model is logically decomposed into a mandatory subtask and an optional subtask. The mandatory subtask must be completely executed before the deadline to produce acceptable result; the optional subtask begins after the mandatory(More)
Preconditioned Conjugate Gradient (PCG) method has been demonstrated to be effective in solving large-scale linear systems for sparse and symmetric positive definite matrices. One critical problem in PCG is to design a good preconditioner, which can significantly reduce the runtime while keeping memory usage efficient. Universal preconditioners are simple(More)
As the high performance computing systems scale up, mapping the tasks of a parallel application onto physical processors to allow efficient communication becomes one of the critical performance issues. Existing algorithms were usually designed to map applications with regular communication patterns. Their mapping criterion usually overlooks the size of(More)
Collaborative filtering algorithms that extract desired information from records have been widely used in data mining and information retrieval, such as recommendation systems. However, the rapidly increased data size demands more efficient and scalable algorithms and implementations. In this paper, we present a novel algorithm that utilizes stochastic(More)
Box intersection checking is a common task used in many large-scale simulations. Traditional methods cannot provide fast box intersection checking with large-scale datasets. This article presents a parallel algorithm to perform Pairwise Box Intersection checking on Graphics processing units (PBIG). The PBIG algorithm consists of three phases: planning,(More)
In this paper, we investigate efficient algorithms and implementations using GPU plus CPU to solve the rectangle intersection problem on a plane. The problem is to report all intersecting pairs of iso-oriented rectangles, whose parallelization on GPUs poses two major computational challenges: data partition and the massive output. The algorithm we presented(More)
— Modern General Purpose Graphics Processing Units(GPGPUs) offer much more computational power than recent CPUs by providing a vast number of simple, data parallel, multithreaded cores. In this study, we focus on the use of a GPGPU to perform parallel discrete-event simulation. Our approach is to use a modified service time distribution function to allow(More)
This paper introduces a prototype of Taiwan UniCloud, a community-driven hybrid cloud platform for academics in Taiwan. The goal is to leverage resources in multiple clouds among different organizations. Each self-managing cloud can join the UniCloud platform to share its resources and simultaneously benefit from other clouds with scale-out capabilities.(More)
Determinant Quantum Monte Carlo (DQMC) simulation has been widely used to reveal macroscopic properties of strong correlated materials. However, parallelization of the DQMC simulation is extremely challenging duo to the serial nature of underlying Markov chain and numerical stability issues. We extend previous work with novelty by presenting a hybrid(More)
With the ability to provide on-demand service and to reduce the IT cost, cloud computing becomes more and more popular recently. Virtualization is one of the important technologies in cloud computing, whose main idea is to provide abstractions of the physical resources. However, such abstraction can cause performance degradation, especially for I/O(More)