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Time-Step Optimal Broadcasting in 3-D Meshes with Minimum Total Communication Distance

by Songluan Cang, Jie Wu - Dept. Computer Science, Univ. Texas at Austin , 2000
"... In this paper we propose a new minimum total communication distance #TCD# algorithm and an optimal TCD algorithm for broadcast in a 3-dimensional mesh #3-D mesh#. The former generates a minimum TCD from a given source node, and the latter guarantees a minimum TCD among all the possible source nodes. ..."
Abstract - Cited by 3 (0 self) - Add to MetaCart
In this paper we propose a new minimum total communication distance #TCD# algorithm and an optimal TCD algorithm for broadcast in a 3-dimensional mesh #3-D mesh#. The former generates a minimum TCD from a given source node, and the latter guarantees a minimum TCD among all the possible source nodes

Minimizing Total Communication Distance of a Time-Step Optimal Broadcast in Mesh Networks

by unknown authors
"... In this paper, we propose a new minimum total communication distance (TCD) algorithm and an optimal TCD algorithm for broadcast in a 2-dimension mesh. The former generates a minimum TCD from a given source node, and the latter guarantees a minimum TCD among all the possible source nodes. These algor ..."
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In this paper, we propose a new minimum total communication distance (TCD) algorithm and an optimal TCD algorithm for broadcast in a 2-dimension mesh. The former generates a minimum TCD from a given source node, and the latter guarantees a minimum TCD among all the possible source nodes

Minimizing Total Communication Distance of a Time-Step Optimal Broadcast in Mesh Networks

by Songluan Cang And , 1998
"... In this paper, we propose a new minimum total communication distance (TCD) algorithm and an optimal TCD algorithm for broadcast in a 2-dimension mesh. The former generates a minimum TCD from a given source node, and the latter guarantees a minimum TCDamong all the possible source nodes. These algor ..."
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In this paper, we propose a new minimum total communication distance (TCD) algorithm and an optimal TCD algorithm for broadcast in a 2-dimension mesh. The former generates a minimum TCD from a given source node, and the latter guarantees a minimum TCDamong all the possible source nodes

Nonlinear total variation based noise removal algorithms

by Leonid I. Rudin, Stanley Osher, Emad Fatemi , 1992
"... A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using the g ..."
Abstract - Cited by 2271 (51 self) - Add to MetaCart
A constrained optimization type of numerical algorithm for removing noise from images is presented. The total variation of the image is minimized subject to constraints involving the statistics of the noise. The constraints are imposed using Lagrange multipliers. The solution is obtained using

Automatically characterizing large scale program behavior

by Timothy Sherwood, Erez Perelman, Greg Hamerly , 2002
"... Understanding program behavior is at the foundation of computer architecture and program optimization. Many pro-grams have wildly different behavior on even the very largest of scales (over the complete execution of the program). This realization has ramifications for many architectural and com-pile ..."
Abstract - Cited by 778 (41 self) - Add to MetaCart
-piler techniques, from thread scheduling, to feedback directed optimizations, to the way programs are simulated. However, in order to take advantage of time-varying behavior, we.must first develop the analytical tools necessary to automatically and efficiently analyze program behavior over large sections

MediaBench: A Tool for Evaluating and Synthesizing Multimedia and Communications Systems

by Chunho Lee, Miodrag Potkonjak, William H. Mangione-smith
"... Over the last decade, significant advances have been made in compilation technology for capitalizing on instruction-level parallelism (ILP). The vast majority of ILP compilation research has been conducted in the context of generalpurpose computing, and more specifically the SPEC benchmark suite. At ..."
Abstract - Cited by 966 (22 self) - Add to MetaCart
. At the same time, a number of microprocessor architectures have emerged which have VLIW and SIMD structures that are well matched to the needs of the ILP compilers. Most of these processors are targeted at embedded applications such as multimedia and communications, rather than general-purpose systems

TIME-STEPPING AND PRESERVING ORTHONORMALITY

by Desmond J. Higham , 1997
"... Certain applications produce initial value ODEs whose solutions, regarded as time-dependent matrices, preserve orthonormality. Such systems arise in the computation of Lyapunov exponents and the construction of smooth singular value decompositions of parametrized matrices. For some special problem c ..."
Abstract - Cited by 15 (0 self) - Add to MetaCart
classes, there exist time-stepping methods that automatically inherit the orthonormality preservation. However, a more widely applicable approach is to apply a standard integrator and regularly replace the approximate solution by an orthonormal matrix. Typically, the approximate solution is replaced

A scaled conjugate gradient algorithm for fast supervised learning

by Martin F. Møller - NEURAL NETWORKS , 1993
"... A supervised learning algorithm (Scaled Conjugate Gradient, SCG) with superlinear convergence rate is introduced. The algorithm is based upon a class of optimization techniques well known in numerical analysis as the Conjugate Gradient Methods. SCG uses second order information from the neural netwo ..."
Abstract - Cited by 451 (0 self) - Add to MetaCart
and avoids a time consuming line-search, which CGB and BFGS uses in each iteration in order to determine an appropriate step size. Incorporating problem dependent structural information in the architecture of a neural network often lowers the overall complexity. The smaller the complexity of the neural

Policy gradient methods for reinforcement learning with function approximation.

by Richard S Sutton , David Mcallester , Satinder Singh , Yishay Mansour - In NIPS, , 1999
"... Abstract Function approximation is essential to reinforcement learning, but the standard approach of approximating a value function and determining a policy from it has so far proven theoretically intractable. In this paper we explore an alternative approach in which the policy is explicitly repres ..."
Abstract - Cited by 439 (20 self) - Add to MetaCart
that the gradient can be written in a form suitable for estimation from experience aided by an approximate action-value or advantage function. Using this result, we prove for the first time that a version of policy iteration with arbitrary differentiable function approximation is convergent to a locally optimal

A time-stepping library for simulation-driven optimization

by William W. Symes , 2004
"... The Timestepping Simulation for Optimization (”TSOpt”) library provides an interface for time-stepping simulation. It packages a simulator together with its derivatives (”sensitivities”) and adjoint derivatives with respect to simulation parameters, and uses the aggregate to define a Rice Vector Lib ..."
Abstract - Cited by 2 (1 self) - Add to MetaCart
The Timestepping Simulation for Optimization (”TSOpt”) library provides an interface for time-stepping simulation. It packages a simulator together with its derivatives (”sensitivities”) and adjoint derivatives with respect to simulation parameters, and uses the aggregate to define a Rice Vector
Results 1 - 10 of 6,325