Average household income, crime, and smoking behaviour in a local area: The Finnish 10-Town study

Social environments, like neighbourhoods, are increasingly recognised as determinants of health. While several studies have reported an association of low neighbourhood socio-economic status with morbidity, mortality and health risk behaviour, little is known of the health effects of neighbourhood crime rates. Using the ongoing 10-Town study in Finland, we examined the relations of average household income and crime rate measured at the local area level, with smoking status and intensity by linking census data of local area characteristics from 181 postal zip codes to survey responses to smoking behaviour in a cohort of 23,008 municipal employees. Gender-stratified multilevel analyses adjusted for age and individual occupational status revealed an association between low local area income rate and current smoking. High local area crime rate was also associated with current smoking. Both local area characteristics were strongly associated with smoking intensity. Among ever-smokers, being an ex-smoker was less likely among residents in areas with low average household income and a high crime rate. In the fully adjusted model, the association between local area income and smoking behaviour among women was substantially explained by the area-level crime rate. This study extends our knowledge of potential pathways through which social environmental factors may affect health. (c) 2007 Elsevier Ltd. All rights reserved.

Numerical ‘health check’ for scientific codes: the CADNA approach

Scientific computation has unavoidable approximations built into its very fabric. One important source of error that is difficult to detect and control is round-off error propagation which originates from the use of finite precision arithmetic. We propose that there is a need to perform regular numerical `health checks' on scientific codes in order to detect the cancerous effect of round-off error propagation. This is particularly important in scientific codes that are built on legacy software. We advocate the use of the CADNA library as a suitable numerical screening tool. We present a case study to illustrate the practical use of CADNA in scientific codes that are of interest to the Computer Physics Communications readership. In doing so we hope to stimulate a greater awareness of round-off error propagation and present a practical means by which it can be analyzed and managed.

2DRMP: A suite of two-dimensional R-matrix propagation codes

The R-matrix method has proved to be a remarkably stable, robust and efficient technique for solving the close-coupling equations that arise in electron and photon collisions with atoms, ions and molecules. During the last thirty-four years a series of related R-matrix program packages have been published periodically in CPC. These packages are primarily concerned with low-energy scattering where the incident energy is insufficient to ionize the target. In this paper we describe previous term2DRMP,next term a suite of two-dimensional R-matrix propagation programs aimed at creating virtual experiments on high performance and grid architectures to enable the study of electron scattering from H-like atoms and ions at intermediate energies.

Prediction-Based Power-Performance Adaptation of Multithreaded Scientific Codes

Computing has recently reached an inflection point with the introduction of multicore processors. On-chip thread-level parallelism is doubling approximately every other year. Concurrency lends itself naturally to allowing a program to trade performance for power savings by regulating the number of active cores; however, in several domains, users are unwilling to sacrifice performance to save power. We present a prediction model for identifying energy-efficient operating points of concurrency in well-tuned multithreaded scientific applications and a runtime system that uses live program analysis to optimize applications dynamically. We describe a dynamic phase-aware performance prediction model that combines multivariate regression techniques with runtime analysis of data collected from hardware event counters to locate optimal operating points of concurrency. Using our model, we develop a prediction-driven phase-aware runtime optimization scheme that throttles concurrency so that power consumption can be reduced and performance can be set at the knee of the scalability curve of each program phase. The use of prediction reduces the overhead of searching the optimization space while achieving near-optimal performance and power savings. A thorough evaluation of our approach shows a reduction in power consumption of 10.8 percent, simultaneous with an improvement in performance of 17.9 percent, resulting in energy savings of 26.7 percent.