It is evaluated on standard datasets where it proves to significantly outperform other similar state of the art implementations, without sacrificing generality or accuracy in any way. The proposed solution is integrated in SLAM++, a nonlinear least squares solver focused on robotics and computer vision. Highly efficient algorithms for both Central Processing Units (CPUs) and Graphics Processing Units (GPUs) are provided. These operations can be used to construct both direct and iterative solvers as well as to compute eigenvalues. Berkeley UPC and GCC UPC are two active open source UPC implemen-tations. The solution proposed in this thesis covers a broad range of functions: it includes efficient sparse block matrix assembly, matrix-vector and matrix-matrix products as well as triangular solving and Cholesky factorization. Most of the existing sparse block matrix implementations focus only on a single operation, such as the matrix-vector product. Some of the more specialized solvers in robotics and computer vision use sparse block matrices internally to reduce sparse matrix assembly costs, but finally end up converting such representation to an elementwise sparse matrix for the linear solver. This is perhaps due to the complexity of sparse block formats which reduces computational efficiency, unless the blocks are very large. The majority of the existing state of the art sparse linear algebra implementations use elementwise sparse matrices and only a small fraction of them support sparse block matrices. Parallelism mainly from Fortran 90 array syntax, FORALL and. Sparse block matrices also occur when solving Finite Element Methods (FEMs) or Partial Differential Equations (PDEs) in physics simulations. Shared-memory directives and OpenMP memory threads. A block size is specified in the declaration as follows: shared block-size type arrayN e.g. Blocking of Shared Arrays Default block size is 1 Shared arrays can be distributed on a block per thread basis, round robin, with arbitrary block sizes. Hargrove: 'Re: Installing runtime on IBV network' Next in thread: Paul H. Block-Cyclic Distributions for Shared Arrays Default block size is 1 Shared arrays can be distributed on a block per thread basis, round robin with arbitrary block sizes. Simultaneous Localization and Mapping (SLAM) in robotics, Bundle Adjustment (BA) or Structure from Motion (SfM) in computer vision. Hargrove: 'Re: Odd problem with shared array' Previous message: Paul H. ![]() ![]() Within this document, static user data means not dynamically allocated (i.e., not allocated on the stack, nor with malloc (), upcallalloc (), or any other memory allocation function). Sparse block matrices occur naturally in many key problems, such as Nonlinear LEast Squares (NLS) on graphical models. This document describes the interface between the UPC compiler and the UPC runtime for handling static user data (both shared and unshared) in UPC programs. This thesis focuses on data structures for sparse block matrices and the associated algorithms for performing linear algebra operations that I have developed.
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