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  • Wynn Kaza - Meta MTIA DV | LinkedIn
    View Wynn Kaza’s profile on LinkedIn, a professional community of 1 billion members
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    View Wynn Kaza's WayUp profile See Wynn's professional qualifications and educational background at Oakland University
  • Wynn Kaza - Home - ACM Digital Library
    Coverage of ACM publications is comprehensive from the 1950's Coverage of other publishers generally starts in the mid 1980's The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it
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  • Publications - Yichao Yuan’s Home Page
    Everest: GPU-Accelerated System For Mining Temporal Motifs Yichao Yuan, Haojie Ye, Sanketh Vedula, Wynn Kaza, Nishil Talati Published in VLDB 2024 Download here
  • Wynn Kaza | ZoomInfo
    Get the details of Wynn Kaza's business profile including email address, phone number, work history and more
  • Wynn Kaza - dblp
    What is the meaning of the colors in the coauthor index? To protect your privacy, all features that rely on external API calls from your browser are turned off by default You need to opt-in for them to become active All settings here will be stored as cookies with your web browser For more information see our F A Q
  • Everest: GPU-Accelerated System For Mining Temporal Motifs
    Everest generates motif-specific mining code to reduce long-latency memory accesses and frequent thread divergence operations Everest incorporates novel low-cost runtime mechanisms to enable load balancing to improve GPU hardware utilization
  • EECS 583 Lecture Notes Fall 2023
    Lecture notes are available in PDF, and the original Powerpoint formats Its best to print these out, so you can take notes on them during class Lectures will be presented live during the scheduled class time Recordings of the lecture are also provided after class, recording links are here Course introduction and organization
  • Everest: GPU-Accelerated System For Mining Temporal Motifs
    Our evaluation shows that, using proposed optimizations, Everest improves the performance of a baseline GPU implementation by 19x, on average





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