NHR PerfLab Seminar: Learning Efficient Sparse Encoding for High-Performance Tensor Computations (March 3, hybrid)
Topic: Learning Efficient Sparse Encoding for High-Performance Tensor Computations
Speaker: Jee Whan Choi, Assistant Professor University of Oregon
Date and time: Tuesday, March 3, 2026, at 2:00 p.m. CET
Location: Seminar Room 2.049 (RRZE) and online via Zoom
Abstract:
We present the reinforcement-learned adaptive tensor encoding (ReLATE) framework, a novel learning-augmented method that automatically constructs efficient sparse tensor representations without labeled training samples. ReLATE employs an autonomous agent that discovers optimized tensor encodings through direct interaction with the application environment, leveraging a hybrid model-free and model-based algorithm to learn from both real and imagined actions. Moreover, ReLATE introduces rule-driven action masking and dynamics-informed action filtering mechanisms that ensure functionally correct tensor encoding with bounded execution time, even during early learning stages. By automatically adapting to both irregular tensor shapes and data distributions, ReLATE generates sparse tensor representations that consistently outperform expert-designed formats across diverse sparse tensor data sets, achieving up to 2X speedup compared to the best sparse format.
Bio:
Jee Whan Choi is an Assistant Professor in the Department of Computer Science at the University of Oregon. During his PhD, he worked on designing parallel and scalable algorithms for scientific applications and modeling their performance and energy efficiency on the latest high-performance computing (HPC) systems. After graduation, he worked as a research staff member at the IBM T. J. Watson Research Center on designing and optimizing tensor decomposition algorithms for Big Data analytics. His current research focuses on designing high-performance tensor algorithms and sparse formats using both traditional and AI-driven methods, and on developing new parallel algorithms for solving PDEs.
For a list of past and upcoming NHR PerfLab seminar events, please see: https://hpc.fau.de/research/nhr-perflab-seminar-series/