Results

**11 - 13**of**13**### ARCHITECTURE COMPUTERS

"... Monte Carlo applications are widely perceived as embarrassingly parallel. (Monte Carlo enthusiasts prefer the term “naturally parallel ” to the somewhat derogatory “embarrassingly parallel ” coined by computer scientists.) The truth of this notion depends, to a large extent, on the quality of the pa ..."

Abstract
- Add to MetaCart

(Show Context)
Monte Carlo applications are widely perceived as embarrassingly parallel. (Monte Carlo enthusiasts prefer the term “naturally parallel ” to the somewhat derogatory “embarrassingly parallel ” coined by computer scientists.) The truth of this notion depends, to a large extent, on the quality of the parallel random number generators used. It is widely assumed that with N processors executing N copies of a Monte Carlo calculation, the pooled result will achieve a variance N times smaller than a

### A Parallel Traceforward-Traceback Simulation at Large Scale

, 2007

"... In April 2006 the USDA-APHIS released a voluntary animal identification and traceability framework collectively known as the National Animal Identification System (NAIS). The basic goal of the NAIS program is to associate a unique identifier (animal ID) to every element of commercial livestock in th ..."

Abstract
- Add to MetaCart

(Show Context)
In April 2006 the USDA-APHIS released a voluntary animal identification and traceability framework collectively known as the National Animal Identification System (NAIS). The basic goal of the NAIS program is to associate a unique identifier (animal ID) to every element of commercial livestock in the United

### Performance and Quality of Random Number Generators

"... Abstract — Random number generation continues to be a critical component in much of computational science and the tradeoff between quality and computational performance is a key issue for many numerical simulations. We review the performance and statistical quality of some well known algorithms for ..."

Abstract
- Add to MetaCart

(Show Context)
Abstract — Random number generation continues to be a critical component in much of computational science and the tradeoff between quality and computational performance is a key issue for many numerical simulations. We review the performance and statistical quality of some well known algorithms for generating pseudo random numbers. Graphical Processing Units (GPUs) are a powerful platform for accelerating computational performance of simulations and random numbers can be generated directly within GPU code or from hosting CPU code. We consider an alternative approach using high quality and genuinely “random ” deviates generated using a Quantum device and we report on how such a PCI bus device can be linked to a CPU program. We discuss computational performance and statistical quality tradeoffs of this architectural model for Monte Carlo simulations such as the Ising system. Keywords: quantum random number generation; GPU; CUDA. 1.