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1. Computing Research Repository

   Poppe, Olga; Lei, Chuan; Rozet, Allison; Rundensteiner, Elke. (April 2021, SIGMOD)

   ABSTRACT Complex event processing (CEP) systems continuously evaluate large workloads of pattern queries under tight time constraints. Event trend aggregation queries with Kleene patterns are commonly used to retrieve summarized insights about the recent trends in event streams. State-of-art methods are limited either due to repetitive computations or unnecessary trend construction. Existing shared approaches are guided by statically selected and hence rigid sharing plans that are often sub-optimal under stream fluctuations. In this work, we propose a novel framework Hamlet that is the first to overcome these limitations. Hamlet introduces two key innovations. First, Hamlet adaptively decides at run time whether to share or not to share computations depending on the current stream properties to harvest the maximum sharing benefit. Second, Hamlet is equipped with a highly efficient shared trend aggregation strategy that avoids trend construction. Our experimental study on both real and synthetic data sets demonstrates that Hamlet consistently reduces query latency by up to five orders of magnitude compared to state-of-the-art approaches. 

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2. Research Opportunities in Heterogeneous Computing for Machine Learning

   https://doi.org/10.1109/HPCS.2018.00094  

   Lam, Herman; Ojika, David (July 2018, International Workshop on High Performance and Dynamic Reconfigurable Systems and Networks (DRSN))

   In recent times, AI and deep learning have witnessed explosive growth in almost every subject involving data. Complex data analyses problems that took prolonged periods, or required laborious manual effort, are now being tackled through AI and deep-learning techniques with unprecedented accuracy. Machine learning (ML) using Convolutional Neural Networks (CNNs) has shown great promise for such applications. However, traditional CPU-based sequential computing no longer can meet the requirements of mission-critical applications which are compute-intensive and require low latency. Heterogeneous computing (HGC), with CPUs integrated with accelerators such as GPUs and FPGAs, offers unique capabilities to accelerate CNNs. In this presentation, we will focus on using FPGA-based reconfigurable computing to accelerate various aspects of CNN. We will begin with the current state of the art in using FPGAs for CNN acceleration, followed by the related R&D activities (outlined below) in the SHREC* Center at the University of Florida, based on which we will discuss the opportunities in heterogeneous computing for machine learning. 

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3. A Comprehensive Solution for Research-Oriented Cloud Computing

   Demir, Mevlut; Wagner, Weslyn; Dandona, Divyaansh; Prevost, John J (June 2018, International Conference on Cloud Computing)
4. Remote Early Research Experiences for Undergraduate Students in Computing

   Alm, Cecilia O; Bailey, Reynold; Miller, Hannah (January 2022, SIGCSE Technical Symposium)

   We provide an experience report about a remote framework for early undergraduate research experiences, which was thematically focused on sensing humans computationally. The framework included three complementary components. First, students experienced a team-based research cycle online, spanning formulating research questions, conducting literature review, performing fully remote human subject data collection experiments and data processing, analyzing and making inference over acquired data with computational experimentation, and disseminating findings. Second, the virtual program offered a set of professional development activities targeted to developing skills and knowledge for graduate school and research career trajectories. Third, it offered interactional and cohort-networking programming for community-building. We discuss not only the unique challenges of the virtual format and the steps put in place to address them but also the opportunities that being online afforded to innovate undergraduate research training remotely. We evaluate the remote training intervention through the organizing team’s post-program reflection and the students’ perceptions conveyed in exit interviews and a mid-program focus group. In addition to outlining lessons learned about more or less successful framework elements, we offer recommendations for applying the framework at other institutions as well as how to transfer activities to in-person formats. 

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5. Onboarding Research Computing and Data Professionals

   https://doi.org/10.1145/3626203.3670582  

   Andino, Gladys; Delinger, Scott; Fosso_Tande, Jacob; Middelkoop, Timothy; Mizumoto, Claire; Reddy, David; Weiner, Michael (July 2024, ACM)