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SUMMARY:HSI Equity Talk
DESCRIPTION:Title: Understanding the advising praxes central to student success at a four-year Hispanic-Serving Research Institution \nPresenter: Dr. Lydia Iyeczohua Zendejas \nLocation: Via Zoom (link provided via RSVP) \nAbstract: Higher education scholars increasingly recognize academic advising as a critical strategy for supporting the persistence of systemically marginalized students. Since the 1990s\, UC Santa Cruz has undergone significant growth and demographic shifts—undergraduate enrollment grew from 10\,269 in 1999 to 17\,517 in 2019\, with sharp increases in underrepresented\, first-generation\, and Hispanic students—creating both challenges and opportunities for advancing equitable outcomes. \nDr. Zendejas’s interview-based qualitative study examines how UCSC’s decentralized\, dual shared advising model shapes advisors’ ability to provide holistic\, culturally responsive advising. In this HSI equity talk\, she will share how advising structures\, practices\, and policies impact advisors’ capacity to support students\, how the current model can act as a structural barrier to collaboration\, and the advising praxis advisors identify as essential to student success\, persistence\, and retention. \nPlease complete this RSVP form if you plan to attend. The Zoom information and a calendar invitation will be sent to those who RSVP. 
URL:https://events.ucsc.edu/event/hsi-equity-talk/
LOCATION:CA
CATEGORIES:Lectures & Presentations,Ph.D. Presentations
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DTSTART;TZID=America/Los_Angeles:20260123T093000
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SUMMARY:Sharma\, R. (CSE) - Automatically Evolving GPU Libraries for Performance Portable AI Kernels
DESCRIPTION:GPUs are the workhorses of modern AI\, widely deployed and developed by many vendors including Apple\, Qualcomm\, Intel\, AMD\, and NVIDIA. While these GPUs all offer high compute potential\, programming them effectively is difficult because they differ in performance-critical features like SIMT width\, cache capacity\, and memory bandwidth\, demanding different optimization strategies. Tunable kernels address this by exposing parameters such as tiling dimensions and workgroup sizes\, enabling per-device specialization. Yet this produces static libraries: tuned once\, then frozen\, degrading as new hardware emerges. We propose automatically evolving libraries that expand their tuning knowledge as new hardware emerges\, with minimal impact on user experience. \nTo build such libraries\, we first need to understand the tuning landscape. We address this through GPU Goldmines\, a WebGPU-based framework for exhaustively collecting tuning data across diverse devices. Our tuned matrix multiplication kernels outperform an optimized baseline by 8.4x on average\, while matrix-vector kernels achieve 93% of platform bandwidth. We find that hyper-tuning for a single GPU causes 50% performance degradation on other devices\, whereas data-driven portability methods recover 88% of peak performance. These kernels are fundamental to the prefill and decode phases of LLM inference. We integrate them into llama.cpp as our evaluation platform\, where they outperform CPU and Vulkan backends. \nBuilding on this data\, we are developing Living Libraries to improve performance continuously without disrupting users. This means choosing good parameters upfront\, learning from real-world execution\, and knowing when to keep searching versus when to stop\, though hand-designed parameter spaces remain inherently bounded. To move beyond this\, we extend toward LLM-based kernel evolution\, where language models propose entirely new kernel variants\, opening a less structured but higher potential search space. \nEvent Host: Rithik Sharma\, Ph.D. Student\, Computer Science and Engineering \nAdvisor: Tyler Sorensen & Yuanchao Xu   \n  \nZoom: https://ucsc.zoom.us/j/92739836317?pwd=0ydDzimUFIoaLDUKst96dk27th4lvW.1 \nPasscode: 089560
URL:https://events.ucsc.edu/event/sharma-r-cse-automatically-evolving-gpu-libraries-for-performance-portable-ai-kernels/
LOCATION:Engineering 2\, Engineering 2 1156 High Street\, Santa Cruz\, CA\, 95064
CATEGORIES:Ph.D. Presentations
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