Statistical and machine learning models for optimizing energy in parallel applications

Rising power costs and constraints are driving a growing focus on the energy efficiency of high performance computing systems. The unique characteristics of a particular system and workload and their effect on performance and energy efficiency are typically difficult for application users to assess and to control. Settings for optimum performance and energy efficiency can also diverge, so we need to identify trade-off options that guide a suitable balance between energy use and performance. We present statistical and machine learning models that only require a small number of runs to make accurate Pareto-optimal trade-off predictions using parameters that users can control. We study model training and validation using several parallel kernels and more complex workloads, including Algebraic Multigrid (AMG), Large-scale Atomic Molecular Massively Parallel Simulator, and Livermore Unstructured Lagrangian Explicit Shock Hydrodynamics. We demonstrate that we can train the models using as few as 12 runs, with prediction error of less than 10%. Our AMG results identify trade-off options that provide up to 45% improvement in energy efficiency for around 10% performance loss. We reduce the sample measurement time required for AMG by 90%, from 13 h to 74 min

Energy efficiency modeling of parallel applications

Energy efficiency has become increasingly important in high performance computing (HPC), as power constraints and costs escalate. Workload and system characteristics form a complex optimization search space in which optimal settings for energy efficiency and performance often diverge. Thus, we must identify trade-off options for performance and energy efficiency to find the desired balance between them. We present an innovative statistical model that accurately predicts the Pareto optimal performance and energy efficiency trade-off options using only user-controllable parameters. Our approach can also tolerate both measurement and model errors. We study model training and validation using several HPC kernels, then explore the feasibility of applying the model to more complex workloads, including AMG and LAMMPS. We can calibrate an accurate model from as few as 12 runs, with prediction error of less than 10%. Our results identify trade-off options allowing up to 40% improvement in energy efficiency at the cost of under 20% performance loss. For AMG, we reduce the required sample measurement time from 13 hours to 74 minutes (about 90%)

A survey on software methods to improve the energy efficiency of parallel computing

Energy consumption is one of the top challenges for achieving the next generation of supercomputing. Codesign of hardware and software is critical for improving energy efficiency (EE) for future large-scale systems. Many architectural power-saving techniques have been developed, and most hardware components are approaching physical limits. Accordingly, parallel computing software, including both applications and systems, should exploit power-saving hardware innovations and manage efficient energy use. In addition, new power-aware parallel computing methods are essential to decrease energy usage further. This article surveys software-based methods that aim to improve EE for parallel computing. It reviews the methods that exploit the characteristics of parallel scientific applications, including load imbalance and mixed precision of floating-point (FP) calculations, to improve EE. In addition, this article summarizes widely used methods to improve power usage at different granularities, such as the whole system and per application. In particular, it describes the most important techniques to measure and to achieve energy-efficient usage of various parallel computing facilities, including processors, memories, and networks. Overall, this article reviews the state-of-the-art of energy-efficient methods for parallel computing to motivate researchers to achieve optimal parallel computing under a power budget constraint

SARS-CoV-2 seroprevalence and asymptomatic viral carriage in healthcare workers: a cross-sectional study

OBJECTIVE: To determine the rates of asymptomatic viral carriage and seroprevalence of SARS-CoV-2 antibodies in healthcare workers.DESIGN: A cross-sectional study of asymptomatic healthcare workers undertaken on 24/25 April 2020.SETTING: University Hospitals Birmingham NHS Foundation Trust (UHBFT), UK.PARTICIPANTS: 545 asymptomatic healthcare workers were recruited while at work. Participants were invited to participate via the UHBFT social media. Exclusion criteria included current symptoms consistent with COVID-19. No potential participants were excluded.INTERVENTION: Participants volunteered a nasopharyngeal swab and a venous blood sample that were tested for SARS-CoV-2 RNA and anti-SARS-CoV-2 spike glycoprotein antibodies, respectively. Results were interpreted in the context of prior illnesses and the hospital departments in which participants worked.MAIN OUTCOME MEASURE: Proportion of participants demonstrating infection and positive SARS-CoV-2 serology.RESULTS: The point prevalence of SARS-CoV-2 viral carriage was 2.4% (n=13/545). The overall seroprevalence of SARS-CoV-2 antibodies was 24.4% (n=126/516). Participants who reported prior symptomatic illness had higher seroprevalence (37.5% vs 17.1%, χ2=21.1034, p&lt;0.0001) and quantitatively greater antibody responses than those who had remained asymptomatic. Seroprevalence was greatest among those working in housekeeping (34.5%), acute medicine (33.3%) and general internal medicine (30.3%), with lower rates observed in participants working in intensive care (14.8%). BAME (Black, Asian and minority ethnic) ethnicity was associated with a significantly increased risk of seropositivity (OR: 1.92, 95% CI 1.14 to 3.23, p=0.01). Working on the intensive care unit was associated with a significantly lower risk of seropositivity compared with working in other areas of the hospital (OR: 0.28, 95% CI 0.09 to 0.78, p=0.02).CONCLUSIONS AND RELEVANCE: We identify differences in the occupational risk of exposure to SARS-CoV-2 between hospital departments and confirm asymptomatic seroconversion occurs in healthcare workers. Further investigation of these observations is required to inform future infection control and occupational health practices.</p