252,421 research outputs found
Efficient Iterative Processing in the SciDB Parallel Array Engine
Many scientific data-intensive applications perform iterative computations on
array data. There exist multiple engines specialized for array processing.
These engines efficiently support various types of operations, but none
includes native support for iterative processing. In this paper, we develop a
model for iterative array computations and a series of optimizations. We
evaluate the benefits of an optimized, native support for iterative array
processing on the SciDB engine and real workloads from the astronomy domain
Format Abstraction for Sparse Tensor Algebra Compilers
This paper shows how to build a sparse tensor algebra compiler that is
agnostic to tensor formats (data layouts). We develop an interface that
describes formats in terms of their capabilities and properties, and show how
to build a modular code generator where new formats can be added as plugins. We
then describe six implementations of the interface that compose to form the
dense, CSR/CSF, COO, DIA, ELL, and HASH tensor formats and countless variants
thereof. With these implementations at hand, our code generator can generate
code to compute any tensor algebra expression on any combination of the
aforementioned formats.
To demonstrate our technique, we have implemented it in the taco tensor
algebra compiler. Our modular code generator design makes it simple to add
support for new tensor formats, and the performance of the generated code is
competitive with hand-optimized implementations. Furthermore, by extending taco
to support a wider range of formats specialized for different application and
data characteristics, we can improve end-user application performance. For
example, if input data is provided in the COO format, our technique allows
computing a single matrix-vector multiplication directly with the data in COO,
which is up to 3.6 faster than by first converting the data to CSR.Comment: Presented at OOPSLA 201
Well-Centered Triangulation
Meshes composed of well-centered simplices have nice orthogonal dual meshes
(the dual Voronoi diagram). This is useful for certain numerical algorithms
that prefer such primal-dual mesh pairs. We prove that well-centered meshes
also have optimality properties and relationships to Delaunay and minmax angle
triangulations. We present an iterative algorithm that seeks to transform a
given triangulation in two or three dimensions into a well-centered one by
minimizing a cost function and moving the interior vertices while keeping the
mesh connectivity and boundary vertices fixed. The cost function is a direct
result of a new characterization of well-centeredness in arbitrary dimensions
that we present. Ours is the first optimization-based heuristic for
well-centeredness, and the first one that applies in both two and three
dimensions. We show the results of applying our algorithm to small and large
two-dimensional meshes, some with a complex boundary, and obtain a
well-centered tetrahedralization of the cube. We also show numerical evidence
that our algorithm preserves gradation and that it improves the maximum and
minimum angles of acute triangulations created by the best known previous
method.Comment: Content has been added to experimental results section. Significant
edits in introduction and in summary of current and previous results. Minor
edits elsewher
History Matching Using Principal Component Analysis
Imperial Users onl
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