Core Count vs Cache Size for Manycore Architectures in the Cloud

The number of cores which fit on a single chip is growing at an exponential rate while off-chip main memory bandwidth is growing at a linear rate at best. This core count to off-chip bandwidth disparity causes per-core memory bandwidth to decrease as process technology advances. Continuing per-core off-chip bandwidth reduction will cause multicore and manycore chip architects to rethink the optimal grain size of a core and the on-chip cache configuration in order to save main memory bandwidth. This work introduces an analytic model to study the tradeoffs of utilizing increased chip area for larger caches versus more cores. We focus this study on constructing manycore architectures well suited for the emerging application space of cloud computing where many independent applications are consolidated onto a single chip. This cloud computing application mix favors small, power-efficient cores. The model is exhaustively evaluated across a large range of cache and core-count configurations utilizing SPEC Int 2000 miss rates and CACTI timing and area models to determine the optimal cache configurations and the number of cores across four process nodes. The model maximizes aggregate computational throughput and is applied to SRAM and logic process DRAM caches. As an example, our study demonstrates that the optimal manycore configuration in the 32nm node for a 200 mm^2 die uses on the order of 158 cores, with each core containing a 64KB L1I cache, a 16KB L1D cache, and a 1MB L2 embedded-DRAM cache. This study finds that the optimal cache size will continue to grow as process technology advances, but the tradeoff between more cores and larger caches is a complex tradeoff in the face of limited off-chip bandwidth and the non-linearities of cache miss rates and memory controller queuing delay

A retrospective analysis of Victorian and South Australian clinical registries for prostate cancer: trends in clinical presentation and management of the disease

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Abstract Background: Prostate cancer (PCa) is the most commonly diagnosed malignancy reported to Australian cancer registries with numerous studies from individual registries summarizing diagnostic and treatment characteristics. The aim of this study was to describe annual trends in clinical and treatment characteristics, and changes in surveillance practice within a large combined cohort of men with PCa in South Australia (SA) and Victoria, Australia in 2008–2013. Methods: Common data items from clinical registries in SA and Victoria were merged to develop a crossjurisdictional dataset consisting of 13,598 men with PCa. Frequencies were used to describe these variables using the National Comprehensive Cancer Network risk of disease progression categories in 10 year age groups. A logistic regression analysis was performed to assess the impact of a number of factors (both individually and together) on the likelihood of men receiving no active treatment within twelve months of the diagnosis (i.e. managed with active surveillance/watchful waiting). Results: Trend analysis showed that over time: (1) men in SA and Victoria are being diagnosed at older age in 2013, 66.1 (SD = 9.7) years compared to 2009 (64.5 (SD = 9.7)); (2) diagnostic methods and characteristics have changed with time; and (3) types of the treatments have changed, with more men having no active treatment. The majority of men were diagnosed with Prostate-Specific Antigen (PSA) <10 ng/mL (66 %) and Grade Group < 4 (65 %). Nearly seventy percent received radical treatment within 12 months of diagnosis, while ~20 % had no active treatment. In 14 % of cases treatment was not recorded or had not commenced. Having no active treatment was strongly associated older age, lower PSA and lower Grade Group at diagnosis, and in 2013 it was offered more frequently (more than 3 times) than in 2009 (OR = 2.63, 95 % CI: 2.16–3.22). Conclusions: Findings of this study provide the first cross-jurisdictional description of PCa characteristics and management in Australia. These findings will provide benchmarking for ongoing monitoring and feedback of disease management and outcomes of PCa through the Prostate Cancer Outcomes Registry–Australia New Zealand to improve evidence-based practice

A Unified Operating System for Clouds and Manycore: fos

Single chip processors with thousands of cores will be available in the next ten years and clouds of multicore processors afford the operating system designer thousands of cores today. Constructing operating systems for manycore and cloud systems face similar challenges. This work identifies these shared challenges and introduces our solution: a factored operating system (fos) designed to meet the scalability, faultiness, variability of demand, and programming challenges of OSâ s for single-chip thousand-core manycore systems as well as current day cloud computers. Current monolithic operating systems are not well suited for manycores and clouds as they have taken an evolutionary approach to scaling such as adding fine grain locks and redesigning subsystems, however these approaches do not increase scalability quickly enough. fos addresses the OS scalability challenge by using a message passing design and is composed out of a collection of Internet inspired servers. Each operating system service is factored into a set of communicating servers which in aggregate implement a system service. These servers are designed much in the way that distributed Internet services are designed, but provide traditional kernel services instead of Internet services. Also, fos embraces the elasticity of cloud and manycore platforms by adapting resource utilization to match demand. fos facilitates writing applications across the cloud by providing a single system image across both future 1000+ core manycores and current day Infrastructure as a Service cloud computers. In contrast, current cloud environments do not provide a single system image and introduce complexity for the user by requiring different programming models for intra- vs inter-machine communication, and by requiring the use of non-OS standard management tools

Development of South Australian-Victorian Prostate Cancer Health Outcomes Research Dataset

Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.BACKGROUND: Prostate cancer is the most commonly diagnosed and prevalent malignancy reported to Australian cancer registries, with numerous studies from single institutions summarizing patient outcomes at individual hospitals or States. In order to provide an overview of patterns of care of men with prostate cancer across multiple institutions in Australia, a specialized dataset was developed. This dataset, containing amalgamated data from South Australian and Victorian prostate cancer registries, is called the South Australian-Victorian Prostate Cancer Health Outcomes Research Dataset (SA-VIC PCHORD). RESULTS: A total of 13,598 de-identified records of men with prostate cancer diagnosed and consented between 2008 and 2013 in South Australia and Victoria were merged into the SA-VIC PCHORD. SA-VIC PCHORD contains detailed information about socio-demographic, diagnostic and treatment characteristics of patients with prostate cancer in South Australia and Victoria. Data from individual registries are available to researchers and can be accessed under individual data access policies in each State. CONCLUSIONS: The SA-VIC PCHORD will be used for numerous studies summarizing trends in diagnostic characteristics, survival and patterns of care in men with prostate cancer in Victoria and South Australia. It is expected that in the future the SA-VIC PCHORD will become a principal component of the recently developed bi-national Australian and New Zealand Prostate Cancer Outcomes Registry to collect and report patterns of care and standardised patient reported outcome measures of men nation-wide in Australia and New Zealand

Applications of sensitivity analysis for probit stochastic network equilibrium

Network equilibrium models are widely used by traffic practitioners to aid them in making decisions concerning the operation and management of traffic networks. The common practice is to test a prescribed range of hypothetical changes or policy measures through adjustments to the input data, namely the trip demands, the arc performance (travel time) functions, and policy variables such as tolls or signal timings. Relatively little use is, however, made of the full implicit relationship between model inputs and outputs inherent in these models. By exploiting the representation of such models as an equivalent optimisation problem, classical results on the sensitivity analysis of non-linear programs may be applied, to produce linear relationships between input data perturbations and model outputs. We specifically focus on recent results relating to the probit Stochastic User Equilibrium (PSUE) model, which has the advantage of greater behavioural realism and flexibility relative to the conventional Wardrop user equilibrium and logit SUE models. The paper goes on to explore four applications of these sensitivity expressions in gaining insight into the operation of road traffic networks. These applications are namely: identification of sensitive, ‘critical’ parameters; computation of approximate, re-equilibrated solutions following a change (post-optimisation); robustness analysis of model forecasts to input data errors, in the form of confidence interval estimation; and the solution of problems of the bi-level, optimal network design variety. Finally, numerical experiments applying these methods are reported

Sociodemographic disparities in survival from colorectal cancer in South Australia: a population-wide data linkage study

Copyright © Beckmann et al. 2016 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Background: Inequalities in survival from colorectal cancer (CRC) across socioeconomic groups and by area of residence have been described in various health care settings. Few population-wide datasets which include clinical and treatment information are available in Australia to investigate disparities. This study examines socio-demographic differences in survival for CRC patients in South Australia (SA), using a population-wide database derived via linkage of administrative and surveillance datasets. Methods: The study population comprised all cases of CRC diagnosed in 2003-2008 among SA residents aged 50-79 yrs in the SA Central Cancer Registry. Measures of socioeconomic status (area level), geographical remoteness, clinical characteristics, comorbid conditions, treatments and outcomes were derived through record linkage of central cancer registry, hospital-based clinical registries, hospital separations, and radiotherapy services data sources. Socio-demographic disparities in CRC survival were examined using competing risk regression analysis. Results: Four thousand six hundred and forty one eligible cases were followed for an average of 4.7 yrs, during which time 1525 died from CRC and 416 died from other causes. Results of competing risk regression indicated higher risk of CRC death with higher grade (HR high v low =2.25, 95 % CI 1.32-3.84), later stage (HR C v A = 7.74, 95 % CI 5.75-10.4), severe comorbidity (HR severe v none =1.21, 95 % CI 1.02-1.44) and receiving radiotherapy (HR = 1.41, 95 % CI 1.18-1.68). Patients from the most socioeconomically advantaged areas had significantly better outcomes than those from the least advantaged areas (HR =0.75, 95 % 0.62-0.91). Patients residing in remote locations had significantly worse outcomes than metropolitan residents, though this was only evident for stages A-C (HR = 1.35, 95 % CI 1.01-1.80). These disparities were not explained by differences in stage at diagnosis between socioeconomic groups or area of residence. Nor were they explained by differences in patient factors, other tumour characteristics, comorbidity, or treatment modalities. Conclusions: Socio-economic and regional disparities in survival following CRC are evident in SA, despite having a universal health care system. Of particular concern is the poorer survival for patients from remote areas with potentially curable CRC. Reasons for thes

Developing and enhancing biodiversity monitoring programmes: a collaborative assessment of priorities

1.Biodiversity is changing at unprecedented rates, and it is increasingly important that these changes are quantified through monitoring programmes. Previous recommendations for developing or enhancing these programmes focus either on the end goals, that is the intended use of the data, or on how these goals are achieved, for example through volunteer involvement in citizen science, but not both. These recommendations are rarely prioritized. 2.We used a collaborative approach, involving 52 experts in biodiversity monitoring in the UK, to develop a list of attributes of relevance to any biodiversity monitoring programme and to order these attributes by their priority. We also ranked the attributes according to their importance in monitoring biodiversity in the UK. Experts involved included data users, funders, programme organizers and participants in data collection. They covered expertise in a wide range of taxa. 3.We developed a final list of 25 attributes of biodiversity monitoring schemes, ordered from the most elemental (those essential for monitoring schemes; e.g. articulate the objectives and gain sufficient participants) to the most aspirational (e.g. electronic data capture in the field, reporting change annually). This ordered list is a practical framework which can be used to support the development of monitoring programmes. 4.People's ranking of attributes revealed a difference between those who considered attributes with benefits to end users to be most important (e.g. people from governmental organizations) and those who considered attributes with greatest benefit to participants to be most important (e.g. people involved with volunteer biological recording schemes). This reveals a distinction between focussing on aims and the pragmatism in achieving those aims. 5.Synthesis and applications. The ordered list of attributes developed in this study will assist in prioritizing resources to develop biodiversity monitoring programmes (including citizen science). The potential conflict between end users of data and participants in data collection that we discovered should be addressed by involving the diversity of stakeholders at all stages of programme development. This will maximize the chance of successfully achieving the goals of biodiversity monitoring programmes

High-level transgene expression by homologous recombination-mediated gene transfer

Gene transfer and expression in eukaryotes is often limited by a number of stably maintained gene copies and by epigenetic silencing effects. Silencing may be limited by the use of epigenetic regulatory sequences such as matrix attachment regions (MAR). Here, we show that successive transfections of MAR-containing vectors allow a synergistic increase of transgene expression. This finding is partly explained by an increased entry into the cell nuclei and genomic integration of the DNA, an effect that requires both the MAR element and iterative transfections. Fluorescence in situ hybridization analysis often showed single integration events, indicating that DNAs introduced in successive transfections could recombine. High expression was also linked to the cell division cycle, so that nuclear transport of the DNA occurs when homologous recombination is most active. Use of cells deficient in either non-homologous end-joining or homologous recombination suggested that efficient integration and expression may require homologous recombination-based genomic integration of MAR-containing plasmids and the lack of epigenetic silencing events associated with tandem gene copies. We conclude that MAR elements may promote homologous recombination, and that cells and vectors can be engineered to take advantage of this property to mediate highly efficient gene transfer and expressio