Analysis of Intel's Haswell Microarchitecture Using The ECM Model and Microbenchmarks

This paper presents an in-depth analysis of Intel's Haswell microarchitecture for streaming loop kernels. Among the new features examined is the dual-ring Uncore design, Cluster-on-Die mode, Uncore Frequency Scaling, core improvements as new and improved execution units, as well as improvements throughout the memory hierarchy. The Execution-Cache-Memory diagnostic performance model is used together with a generic set of microbenchmarks to quantify the efficiency of the microarchitecture. The set of microbenchmarks is chosen such that it can serve as a blueprint for other streaming loop kernels.Comment: arXiv admin note: substantial text overlap with arXiv:1509.0311

Automated Instruction Stream Throughput Prediction for Intel and AMD Microarchitectures

An accurate prediction of scheduling and execution of instruction streams is a necessary prerequisite for predicting the in-core performance behavior of throughput-bound loop kernels on out-of-order processor architectures. Such predictions are an indispensable component of analytical performance models, such as the Roofline and the Execution-Cache-Memory (ECM) model, and allow a deep understanding of the performance-relevant interactions between hardware architecture and loop code. We present the Open Source Architecture Code Analyzer (OSACA), a static analysis tool for predicting the execution time of sequential loops comprising x86 instructions under the assumption of an infinite first-level cache and perfect out-of-order scheduling. We show the process of building a machine model from available documentation and semi-automatic benchmarking, and carry it out for the latest Intel Skylake and AMD Zen micro-architectures. To validate the constructed models, we apply them to several assembly kernels and compare runtime predictions with actual measurements. Finally we give an outlook on how the method may be generalized to new architectures.Comment: 11 pages, 4 figures, 7 table

Statistical Models for Co-occurrence Data

Modeling and predicting co-occurrences of events is a fundamental problem of unsupervised learning. In this contribution we develop a statistical framework for analyzing co-occurrence data in a general setting where elementary observations are joint occurrences of pairs of abstract objects from two finite sets. The main challenge for statistical models in this context is to overcome the inherent data sparseness and to estimate the probabilities for pairs which were rarely observed or even unobserved in a given sample set. Moreover, it is often of considerable interest to extract grouping structure or to find a hierarchical data organization. A novel family of mixture models is proposed which explain the observed data by a finite number of shared aspects or clusters. This provides a common framework for statistical inference and structure discovery and also includes several recently proposed models as special cases. Adopting the maximum likelihood principle, EM algorithms are derived to fit the model parameters. We develop improved versions of EM which largely avoid overfitting problems and overcome the inherent locality of EM--based optimization. Among the broad variety of possible applications, e.g., in information retrieval, natural language processing, data mining, and computer vision, we have chosen document retrieval, the statistical analysis of noun/adjective co-occurrence and the unsupervised segmentation of textured images to test and evaluate the proposed algorithms

Professional learning, organisational change and clinical leadership development outcomes.

INTRODUCTION: The aim of this study is to develop a conceptually sound outcome model for clinical leadership (CL) development in healthcare, linking individual professional learning and organisational change. Frontline doctors' CL is often offered as a solution to healthcare challenges worldwide. However, there is a paucity of rigorous evidence of effectiveness of CL development, or theories supporting it. Importantly, the literature currently lacks robust outcome models for CL development, impeding robust impact evaluations. METHODS: This multi-source, sequential integrated mixed-methods study draws on systematic content analysis of NHS policy documents and empirical data from a CL programme evaluation study: exploratory factor analysis (EFA) of 142 participants' survey responses and thematic qualitative analysis of 30 in-depth participant interviews across six cohorts. Through integrating findings from the three analyses we examine: (a) the expected organisational outcomes of CL, (b) individual learning outcomes of CL development, and (c) the mechanisms linking the two. RESULTS: The policy analysis identified three desired solutions to key healthcare problems which CL is expected to offer: Speeding up good practice, Inter-professional collaboration and dialogue, and Change and transformation. Triangulating the EFA results with the qualitative analysis produced five individual outcome constructs: Self-efficacy, Engaging stakeholders, Agency, Boundary-crossing expertise, and Willingness to take risks and to learn from risks and failures. Further qualitative analysis uncovered key mechanisms linking the individual outcomes with the desired organisational changes. DISCUSSION: Despite significant investments into CL development in the UK and worldwide, the absence of conceptually robust and operationally specific outcome models linking individual and organisational impact impedes rigorous evaluations of programme effectiveness. Our study developed a novel individual and organisational outcome model including a theory of change for clinical leadership. Our findings further contribute to professional learning theory in medical settings by conceptualising and operationalising the mechanisms operating between individual and organisational learning outcomes.The authors received a grant from the Cambridge University Health Partners (CUHP) who run the Chief Residents Leadership and Management programme which covered the empirical part of the work reported here

Learning Aerial Image Segmentation from Online Maps

This study deals with semantic segmentation of high-resolution (aerial) images where a semantic class label is assigned to each pixel via supervised classification as a basis for automatic map generation. Recently, deep convolutional neural networks (CNNs) have shown impressive performance and have quickly become the de-facto standard for semantic segmentation, with the added benefit that task-specific feature design is no longer necessary. However, a major downside of deep learning methods is that they are extremely data-hungry, thus aggravating the perennial bottleneck of supervised classification, to obtain enough annotated training data. On the other hand, it has been observed that they are rather robust against noise in the training labels. This opens up the intriguing possibility to avoid annotating huge amounts of training data, and instead train the classifier from existing legacy data or crowd-sourced maps which can exhibit high levels of noise. The question addressed in this paper is: can training with large-scale, publicly available labels replace a substantial part of the manual labeling effort and still achieve sufficient performance? Such data will inevitably contain a significant portion of errors, but in return virtually unlimited quantities of it are available in larger parts of the world. We adapt a state-of-the-art CNN architecture for semantic segmentation of buildings and roads in aerial images, and compare its performance when using different training data sets, ranging from manually labeled, pixel-accurate ground truth of the same city to automatic training data derived from OpenStreetMap data from distant locations. We report our results that indicate that satisfying performance can be obtained with significantly less manual annotation effort, by exploiting noisy large-scale training data.Comment: Published in IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSIN