<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Zhongchun Zhou | CAT LAB</title><link>https://deep-learning-profiling-tools.github.io/CAT-Lab/author/zhongchun-zhou/</link><atom:link href="https://deep-learning-profiling-tools.github.io/CAT-Lab/author/zhongchun-zhou/index.xml" rel="self" type="application/rss+xml"/><description>Zhongchun Zhou</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Fri, 06 Nov 2026 00:00:00 +0000</lastBuildDate><image><url>https://deep-learning-profiling-tools.github.io/CAT-Lab/media/icon_hu6808975029018430273.png</url><title>Zhongchun Zhou</title><link>https://deep-learning-profiling-tools.github.io/CAT-Lab/author/zhongchun-zhou/</link></image><item><title>TileBench: A Controlled Benchmark for Performance Evaluation and Bottleneck Diagnosis of Tile-Based Programming Models</title><link>https://deep-learning-profiling-tools.github.io/CAT-Lab/publication/tilebench_aacl2026/</link><pubDate>Fri, 06 Nov 2026 00:00:00 +0000</pubDate><guid>https://deep-learning-profiling-tools.github.io/CAT-Lab/publication/tilebench_aacl2026/</guid><description/></item><item><title>TileBench Accepted at AACL 2026</title><link>https://deep-learning-profiling-tools.github.io/CAT-Lab/news/2026-09-08-tilebench-aacl2026/</link><pubDate>Tue, 08 Sep 2026 00:00:00 +0000</pubDate><guid>https://deep-learning-profiling-tools.github.io/CAT-Lab/news/2026-09-08-tilebench-aacl2026/</guid><description>&lt;p>Our paper &lt;a href="https://deep-learning-profiling-tools.github.io/CAT-Lab/CAT-Lab/publication/tilebench_aacl2026/">&lt;em>TileBench: A Controlled Benchmark for Performance Evaluation and Bottleneck Diagnosis of Tile-Based Programming Models&lt;/em>&lt;/a>, by &lt;strong>Bowen Cui, Zhongchun Zhou, Hao Wu, Tejas Ramesh, Junyu Yin, Jialiang Gu, and Keren Zhou&lt;/strong>, has been accepted at &lt;strong>AACL-IJCNLP 2026&lt;/strong>. TileBench compares tile-based GPU programming models under controlled conditions and supports profiling-guided diagnosis of performance bottlenecks. Congratulations to all the authors!&lt;/p></description></item></channel></rss>