Training of Artificial Neural Networks for large data sets is a time consuming task. Various approaches have been proposed to reduce the efforts, many of them by applying parallelization techniques. In this paper we develop and analyze two novel parallel training approaches for Backpropagation neural networks for face recognition. We focus on two specific parallelization environments, using on ...

We combine parallelization and cluster Monte Carlo for hard sphere systems and present a parallelized event chain algorithm for the hard disk system in two dimensions. For parallelization we use a spatial partitioning approach into simulation cells. We find that it is crucial for correctness to ensure detailed balance on the level of Monte Carlo sweeps by drawing the starting sphere of event ch...

We present a hybrid OpenMP/MPI parallelization of the finite element method that is suitable to make use of modern high performance computers. These are usually built from a large bulk of multi-core systems connected by a fast network. Our parallelization method is based firstly on domain decomposition to divide the large problem into small chunks. Each of them is then solved on a multi-core sy...

With the fast development of multi-core processors, automatic parallelization becomes increasingly important. In this work, we focus on the parallelization of utility programs, a class of commonly used applications including compilers, transcoding utilities, file compressions, and databases. They take a series of requests as inputs and serve them one by one. Their high input dependence poses a ...

The goal of this dissertation is to give programmers the ability to achieve high performance by focusing on developing parallel algorithms, rather than on architecture-speciic details. The advantages of this approach also include program portability and legibility. To achieve high performance, we provide automatic compilation techniques that tailor parallel algorithms to shared-memory multiproc...

Since their impressive successes in various areas of large-scale parallelization, recent techniques like UCT and other Monte-Carlo planning variants (Kocsis and Szepesvari, 2006a) have been extensively studied (Coquelin and Munos, 2007; Wang and Gelly, 2007). We here propose and compare various forms of parallelization of bandit-based tree-search, in particular for our computer-go algorithm XYZ.