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Adaptive Precision FloatingPoint Arithmetic and Fast Robust Geometric Predicates
 Discrete & Computational Geometry
, 1996
"... Exact computer arithmetic has a variety of uses including, but not limited to, the robust implementation of geometric algorithms. This report has three purposes. The first is to offer fast softwarelevel algorithms for exact addition and multiplication of arbitrary precision floatingpoint values. T ..."
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Cited by 172 (5 self)
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Exact computer arithmetic has a variety of uses including, but not limited to, the robust implementation of geometric algorithms. This report has three purposes. The first is to offer fast softwarelevel algorithms for exact addition and multiplication of arbitrary precision floatingpoint values
Robust Adaptive FloatingPoint Geometric Predicates
 in Proc. 12th Annu. ACM Sympos. Comput. Geom
, 1996
"... Fast C implementations of four geometric predicates, the 2D and 3D orientation and incircle tests, are publicly available. Their inputs are ordinary single or double precision floatingpoint numbers. They owe their speed to two features. First, they employ new fast algorithms for arbitrary precision ..."
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Cited by 57 (2 self)
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Fast C implementations of four geometric predicates, the 2D and 3D orientation and incircle tests, are publicly available. Their inputs are ordinary single or double precision floatingpoint numbers. They owe their speed to two features. First, they employ new fast algorithms for arbitrary
Highprecision floatingpoint arithmetic in scientific computation
 Computing in Science and Engineering, May–June
, 2005
"... At the present time, IEEE 64bit floatingpoint arithmetic is sufficiently accurate for most scientific applications. However, for a rapidly growing body of important scientific computing applications, a higher level of numeric precision is required: some of these applications require roughly twice ..."
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Cited by 19 (1 self)
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At the present time, IEEE 64bit floatingpoint arithmetic is sufficiently accurate for most scientific applications. However, for a rapidly growing body of important scientific computing applications, a higher level of numeric precision is required: some of these applications require roughly twice
Fast Parallel Algorithms for ShortRange Molecular Dynamics
 JOURNAL OF COMPUTATIONAL PHYSICS
, 1995
"... Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of interatomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dyn ..."
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Cited by 622 (6 self)
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Three parallel algorithms for classical molecular dynamics are presented. The first assigns each processor a fixed subset of atoms; the second assigns each a fixed subset of interatomic forces to compute; the third assigns each a fixed spatial region. The algorithms are suitable for molecular dynamics models which can be difficult to parallelize efficiently  those with shortrange forces where the neighbors of each atom change rapidly. They can be implemented on any distributedmemory parallel machine which allows for messagepassing of data between independently executing processors. The algorithms are tested on a standard LennardJones benchmark problem for system sizes ranging from 500 to 100,000,000 atoms on several parallel supercomputers  the nCUBE 2, Intel iPSC/860 and Paragon, and Cray T3D. Comparing the results to the fastest reported vectorized Cray YMP and C90 algorithm shows that the current generation of parallel machines is competitive with conventi...
FAST VOLUME RENDERING USING A SHEARWARP FACTORIZATION OF THE VIEWING TRANSFORMATION
, 1995
"... Volume rendering is a technique for visualizing 3D arrays of sampled data. It has applications in areas such as medical imaging and scientific visualization, but its use has been limited by its high computational expense. Early implementations of volume rendering used bruteforce techniques that req ..."
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Cited by 541 (2 self)
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that require on the order of 100 seconds to render typical data sets on a workstation. Algorithms with optimizations that exploit coherence in the data have reduced rendering times to the range of ten seconds but are still not fast enough for interactive visualization applications. In this thesis we present a
An experimental analysis of floatingpoint versus exact arithmetic
 In Proceedings of the 23rd Annual Canadian Conference on Computational Geometry
, 2011
"... In this paper we investigate how sophisticated floatingpoint codes that are in realworld use – VRONI for computing Voronoi diagrams, FIST for computing triangulations, and BONE for computing straight skeletons – can benefit from the use of the Core library (for exact geometric computing) or the MPF ..."
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Cited by 2 (1 self)
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validity of the outputs generated by all codes. As expected, the output precision of VRONI increases when MPFR is used, at a cost of an average slowdown by a multiplicative factor of 70. On the other hand, FIST demonstrates that a careful engineering can enable a code that uses floatingpoint arithmetic
A Tutorial on Visual Servo Control
 IEEE Transactions on Robotics and Automation
, 1996
"... This paper provides a tutorial introduction to visual servo control of robotic manipulators. Since the topic spans many disciplines our goal is limited to providing a basic conceptual framework. We begin by reviewing the prerequisite topics from robotics and computer vision, including a brief review ..."
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Cited by 822 (25 self)
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review of coordinate transformations, velocity representation, and a description of the geometric aspects of the image formation process. We then present a taxonomy of visual servo control systems. The two major classes of systems, positionbased and imagebased systems, are then discussed. Since any
Accurate floatingpoint summation
, 2005
"... Given a vector of floatingpoint numbers with exact sum s, we present an algorithm for calculating a faithful rounding of s into the set of floatingpoint numbers, i.e. one of the immediate floatingpoint neighbors of s. If the s is a floatingpoint number, we prove that this is the result of our a ..."
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Cited by 11 (1 self)
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, where in that case the result is stored in a vector of K floatingpoint numbers. We also present an algorithm for rounding the sum s to the nearest floatingpoint number. Our algorithms are fast in terms of measured computing time because they neither require special operations such as access
Results 1  10
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28,898