Scalable black-box prediction models for multi-dimensional adaptation on NUMA multi-cores

This paper presents a scalable, statistical ‘black-box’ model for predicting the performance of parallel programs on multi-core non-uniform memory access (NUMA) systems. We derive a model with low overhead, by reducing data collection and model training time. The model can accurately predict the behaviour of parallel applications in response to changes in their concurrency, thread layout on NUMA nodes, and core voltage and frequency. We present a framework that applies the model to achieve significant energy and energy-delay-square (ED2) savings (9% and 25%, respectively) along with performance improvement (10% mean) on an actual 16-core NUMA system running realistic application workloads. Our prediction model proves substantially more accurate than previous efforts.

Vulnerability in the perioperative patient: a concept analysis

This study aims to report the analysis of the concept of vulnerability. Literature searches were conducted as well as a manual library search from article reference lists. Retrieved literature was analysed using the Walker & Advant (2005) concept analysis framework. The study concludes that inclusion of the concept of vulnerability within both pre- and post-registration training programmes would facilitate awareness of the issues surrounding perioperative vulnerability and the need to plan individualised care accordingly. It is hoped that this analysis will inspire further research and theoretical underpinning of perioperative practice, facilitating the development of new ways to manage vulnerability.