This is a follow-up work on TOD, which selects one of multiple deep neural networks (DNNs) to perform real-time video analytics (object detection) on low-end devices, e.g., on the camera itself. TOD uses the median object size to determine which one out of 4 YOLO DNNs will meet the real-time […]
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Real-time video analytics on the edge is challenging as the computationally constrained resources typically cannot analyse video streams at full fidelity and frame rate, which results in loss of accuracy. We propose a Transprecise Object Detector (TOD) which maximises the real-time object detection accuracy on an edge device by selecting […]
This project aims to develop novel algorithms for the maximum weighted clique (MWC) problem, which appears in various data analysis pipelines in precision medicine. The MWC problem is NP-hard in nature, which makes it particularly challenging given the exponentially increasing amount of data it is applied to. Although several attempts […]
Computing devices implement computer arithmetic as basic functionality, and they implement the same, standardized number formats in order to support software portability. However, with Moore’s Law ending, we question whether it remains the best approach to achieve high performance and low energy consumption by applying the same standardized number formats […]
17 June 2021 Abstract: Large graphs are behind many problems in today’s computing landscape. Thegrowing sizes of such graphs, reaching 70 trillion edges recently, requireunprecedented amounts of compute power, storage, and energy. In this talk, weillustrate how to effectively process such extreme-scale graphs. We will firstdiscuss Slim Graph, the first […]
3 June 2021 Abstract: The emergence of big data in recent years due to the vast societal digitalization and large-scale sensor deployment has entailed significant interest in machine learning methods to enable automatic data analytics. In a majority of the learning algorithms used in industrial as well as academic settings, the first-order iterative […]
Abstract Graph algorithms are widely used in several application domains. It has been established that parallelizing graph algorithms is challenging. The parallelization issues get exacerbated when graphics processing unit (GPU) is used to execute graph algorithms. In particular, three important GPU-specific aspects affect performance: memory coalescing, memory latency, and thread […]
Dr Aydın Buluç29 April 2021 Solving systems of linear equations have traditionally driven the research in sparse matrix computation for decades. Direct and iterative solvers, together with finite element computations, still account for the primary use case for sparse matrix data structures and algorithms. These sparse “solvers” often serve as the workhorse of many algorithms in spectral graph theory […]
Dr Jeremy Singer, University of Glasgow25 February 2021 Abstract:Love ’em or hate ’em, interactive computational notebooks are here to stay as a mainstream code development medium. In particular, the Jupyter system is widely used by the data science community. This presentation explores some use cases for programmatic introspection of a Jupyter notebook […]
Dr Giorgis Georgakoudis, Lawrence Livermore National Laboratory18 February 2021 Abstract: This talk will present an overview of research on different areas of open problems in HPC. On fault tolerance, Giorgis will present the Reinit solution for fault tolerance in large scale MPI applications. Reinit improves the recovery time of checkpointed […]