Papers by Clifford Shaffer
Proceedings of the 46th ACM Technical Symposium on Computer Science Education - SIGCSE '15, 2015
Sensor Fusion Spatial Reasoning and Scene Interpretation, 1988
ABSTRACT
Computer Science Education, 2016
Proceedings of the 16th International Parallel and Distributed Processing Symposium, 2002
ABSTRACT We describe a binding schema markup language (BSML) for describing data interchange betw... more ABSTRACT We describe a binding schema markup language (BSML) for describing data interchange between scientific codes.
Proceedings of the 46th Acm Technical Symposium, Feb 24, 2015
This paper explores the application of a global optimiza-tion technique to solve the optimal tran... more This paper explores the application of a global optimiza-tion technique to solve the optimal transmitter place-ment problem in wireless system design. An efficient pattern search algorithm—DIRECT (DIviding RECT-angles) of Jones, Perttunen, and Stuckman (1993)—has been ...
We describe a binding schema markup language (BSML) for describing data interchange between scien... more We describe a binding schema markup language (BSML) for describing data interchange between scientific codes. Such a facility is an important constituent of scientific problem solving environments (PSEs). BSML is designed to integrate with a PSE or application composition system that views model specification and execution as a problem of managing semistructured data. The data interchange problem is addressed by three techniques for processing semistructured data: validation, binding, and conversion. We present BSML and describe its application to a PSE for wireless communications system design.
Ensembles of simulations are employed to estimate the statistics of possible future states of a s... more Ensembles of simulations are employed to estimate the statistics of possible future states of a system, and are widely used in important applications such as climate change and biological modeling. Ensembles of runs can naturally be executed in parallel. However, when the CPU times of individual simulations vary considerably, a simple strategy of assigning an equal number of tasks per processor can lead to serious work imbalances and low parallel efficiency. This paper presents a new probabilistic framework to analyze the performance of dynamic load balancing algorithms for ensembles of simulations where many tasks are mapped onto each processor, and where the individual compute times vary considerably among tasks. Four load balancing strategies are discussed: most-dividing, all-redistribution, random-polling, and neighbor-redistribution. Simulation results with a stochastic budding yeast cell cycle model is consistent with the theoretical analysis. It is especially significant that there is a provable global decrease in load imbalance for the local rebalancing algorithms due to scalability concerns for the global rebalancing algorithms. The overall simulation time is reduced by up to 25%, and the total processor idle time by 85%.
Simulation, 2007
The budding yeast cell cycle can be modeled by a set of ordinary differential equations with 143 ... more The budding yeast cell cycle can be modeled by a set of ordinary differential equations with 143 rate constant parameters. The quality of the model (and an associated vector of parameter settings) is measured by comparing simulation results to the experimental data derived from observing the cell cycles of over 100 selected mutated forms. Unfortunately, determining whether the simulated phenotype matches experimental data is difficult since the experimental data tend to be qualitative in nature (i.e., whether the mutation is viable, or which development phase it died in). Because of this, previous methods for automatically comparing simulation results to experimental data used a discontinuous penalty function, which limits the range of techniques available for automated estimation of the differential equation parameters. This paper presents a system of smooth inequality constraints that will be satisfied if and only if the model matches the experimental data. Results are presented for evaluating the mutants with the two most frequent phenotypes. This nonlinear inequality formulation is the first step toward solving a large-scale feasibility problem to determine the ordinary differential equation model parameters.
Proceedings of the 11th Annual Acm Symposium on User Interface Software and Technology, 1998
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Papers by Clifford Shaffer