The Numerical Aerodynamic Simulation (NAS) Parallel Benchmarks are a set of algorithmically specified benchmarks indicative of the computation and communication needs of typical large-scale aerodynamics problems. Although a great deal of work has been done with respect to implementing the NAS

A new set of benchmarks has been developed for the performance evaluation of highly parallel supercomputers. These benchmarks consist of ve \parallel kernel " benchmarks and three \simulated application" benchmarks. Together they mimic the computation and data movement

stage by gradually pruning the support value spectrum and optimizing the hyperparameters during the sparse approximation procedure. In this paper, twenty public domain benchmark datasets are used to evaluate the test set performance of LS-SVM classifiers with linear, polynomial and radial basis function

this document Although it is rather uncommon to have a section like this in a Thesis, I believe it to be an indispensable part. In a Thesis, like the one before you, some conventions for style and structure are used and some information was speci cally included or left out. I hope the reader will gain from the following remarks and thus will nd his way more quickly in this document

on histograms, and is even better than the nonlinear SPM kernels, leading to state-of-the-art performance on several benchmarks by using a single type of descriptors. 1.

Abstract The U-Net communication architecture provides processes with a virtual view of a network device to enable user-level access to high-speed communication devices. The architecture, implemented on standard workstations using off-the-shelf ATM communication hardware, removes the kernel from

functions than a comparable SVM while offering a number of additional advantages. These include the benefits of probabilistic predictions, automatic estimation of `nuisance’ parameters, and the facility to utilise arbitrary basis functions (e.g. non-`Mercer’ kernels). We detail the Bayesian framework

This paper evaluates several hardware platforms and operating systems using a set of benchmarks that stress kernel entry/exit, file systems, and other things related to operating systems. The overall conclusion is that operating system performance is not improving at the same rate as the base speed

of kernel, it is parameterless -- the selection of the number of training errors is inherent in the algorithm and not chosen by an extra free parameter as in SVMs. First experiments using the method on benchmark datasets from the UCI repository show results similar to SVMs which have been tuned to have

This paper presents and characterizes Rodinia, a benchmark suite for heterogeneous computing. To help architects study emerging platforms such as GPUs (Graphics Processing Units), Rodinia includes applications and kernels which target multi-core CPU and GPU platforms. The choice of applications