![]() 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. UPC: uses shared attribute implicit locality various blocking strategies. Eachthread has local data on which it can operate with all the efciency of a traditional process on a sequential computer.At the same time, however, it has easy access to shared data that are local to other threads. Highly efficient algorithms for both Central Processing Units (CPUs) and Graphics Processing Units (GPUs) are provided. UPC program running with shared data on a parallel system will contain at least a single thread per processor. These operations can be used to construct both direct and iterative solvers as well as to compute eigenvalues. If you are trying to run jobs on a multi-node network, your best bet is to rebuild from source, following the install instructions. 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. If all you want is to run jobs on the local node, Id recommend compiling with upcc -networksmp to enable the smp loopback backend, which should not depend on the InfiniBand libraries. Most of the existing sparse block matrix implementations focus only on a single operation, such as the matrix-vector product. A block size indicates how many elements are consecutively allocated to one thread before proceeding to the next thread when allocating a shared array among. 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. Sparse block matrices also occur when solving Finite Element Methods (FEMs) or Partial Differential Equations (PDEs) in physics simulations. Simultaneous Localization and Mapping (SLAM) in robotics, Bundle Adjustment (BA) or Structure from Motion (SfM) in computer vision. Sparse block matrices occur naturally in many key problems, such as Nonlinear LEast Squares (NLS) on graphical models. This thesis focuses on data structures for sparse block matrices and the associated algorithms for performing linear algebra operations that I have developed. Hides the distinction between shared/distributed memory Partitioned: data is designated as local or global Does not hide this: critical for locality and scaling.
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