Synchronization-Point Driven Resource Management in Chip Multiprocessors.

With the proliferation of Chip Multiprocessors (CMPs), shared memory multi-threaded programs are expanding fast in every application domain. These programs exhibit execution characteristics that go beyond those observed in single-threaded programs, mainly due to data sharing and synchronization. To ensure that next generation CMPs will perform well on such anticipated workloads, it is vital to understand how these programs and architectures interact, and exploit the unique opportunities presented. This thesis examines the time-varying execution characteristics of the shared memory workloads in conjunction to the synchronization points that exist in the programs. The main hypothesis is that the type, the position, and the repetitive execution of synchronization constructs can be exploited to unfold important execution phases and enable new optimization opportunities. The research provides a simple application-driven approach for predicting the program behavior and effectively driving dynamic performance optimization and resource management actions in future CMPs. In the first part of this thesis, I show how synchronization points relate to various program-wide periodic behaviors. Based on the observations, I develop a framework where user-level synchronization primitives are exposed to the hardware and monitored to detect program phases and guide dynamic adaptation. Through workload-driven evaluation, I demonstrate the effectiveness of the framework in improving the performance/power in on-chip interconnects. The second part of the thesis explores in depth the inter-thread communication behaviors. I show that although synchronization points under the shared memory model do not expose any communication details, they indicate well the points where coherence communication patterns change or repeat. By leveraging this property, I design a synchronization-point-based coherence predictor that uncovers communication patterns with high accuracy, while consuming significantly less hardware resources compared to existing predictors. In the last part, I investigate the underlying reasons causing threads to wait in synchronization points, wasting resources. I show that these reasons can vary even across different programs phases, and existing critical-path predictors can render ineffective under certain conditions. I then present a new scheme that improves predictability by incorporating history information from previous points. The new design is robust and can amortize the run-time imbalances to improve the system's performance and/or energy

Models, services and security in modern online social networks

Modern online social networks have revolutionized the world the same way the radio and the plane did, crossing geographical and time boundaries, not without problems, more can be learned, they can still change our world and that their true worth is still a question for the future

A comparison of statistical machine learning methods in heartbeat detection and classification

In health care, patients with heart problems require quick responsiveness in a clinical setting or in the operating theatre. Towards that end, automated classification of heartbeats is vital as some heartbeat irregularities are time consuming to detect. Therefore, analysis of electro-cardiogram (ECG) signals is an active area of research. The methods proposed in the literature depend on the structure of a heartbeat cycle. In this paper, we use interval and amplitude based features together with a few samples from the ECG signal as a feature vector. We studied a variety of classification algorithms focused especially on a type of arrhythmia known as the ventricular ectopic fibrillation (VEB). We compare the performance of the classifiers against algorithms proposed in the literature and make recommendations regarding features, sampling rate, and choice of the classifier to apply in a real-time clinical setting. The extensive study is based on the MIT-BIH arrhythmia database. Our main contribution is the evaluation of existing classifiers over a range sampling rates, recommendation of a detection methodology to employ in a practical setting, and extend the notion of a mixture of experts to a larger class of algorithms