This study developed a parallel algorithm to efficiently solve linear programming models. The proposed algorithm utilizes the Dantzig-Wolfe Decomposition Principle and can be easily implemented in a general distributed computing environment. The analytical performance of the new algorithm, including the speedup upper bound and lower bound limits, was derived. Numerical experiments are also prov...

Modern multi-core computers provide an opportunity to parallelize plan recognition algorithms to decrease runtime. Viewing the problem as one of parsing and performing a complete breadth first search, makes ELEXIR (Engine for LEXicalized Intent Recognition)(Geib 2009; Geib & Goldman 2011) particularly suitable for such parallelism. This paper documents the extension of ELEXIR to utilize such mo...

Our combined task and instruction static scheduling algorithm implemented in the COINS compiler uses an Integer Linear Programming model to find a schedule for a program on a symmetric multiprocessor system-on-chip. We compare our work to state of the art approaches and on average we find a speedup as high as 1.49 compared to a static task scheduling approach without instruction scheduling. Dep...

A case study is presented demonstrating the application of the Mondriaan package for sparse matrix partitioning to the field of cryptology. An important step in an integer factorisation attack on the RSA public-key cryptosystem is the solution of a large sparse linear system with 0/1 coefficients, which can be done by the block Lanczos algorithm proposed by Montgomery. We parallelise this algor...

MapReduce, the de facto standard for large scale data-intensive applications, is a remarkable parallel programming model, allowing for easy parallelization of data intensive computations over many machines in a cloud. As huge tree data such as XML has achieved the status of the de facto standard for representing structured information, the situation calls for efficient MapReduce programs treati...

We present a massively parallel implementation to perform quantum dynamical wave packet calculations of molecules on surfaces. The employed algorithm propagates the wavefunction via the time-dependent Schrödinger equation within a finite basis representation by Split and Chebyshev schemes, respectively. For the parallelization, a problem adapted data decomposition in all dimensions is introduce...

This paper presents a new branch-and-bound mixed-integer linear programming-based algorithm for designing discrete-coefficient finite-impulse response (FIR) filters using a cluster of workstations as the computation platform. The discrete coefficient space considered in this paper is the sum of signed power-of-two space, but the technique is also applicable to other discrete coefficient spaces....

Applying linear templates is an integral part of many object detection systems and accounts for a significant portion of computation time. We describe a method that achieves a substantial end-to-end speedup over the best current methods, without loss of accuracy. Our method is a combination of approximating scores by vector quantizing feature windows and a number of speedup techniques including...