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My research interests lie in the general area of high-performance computing, particularly in parallel programming and algorithms, with the focus on mitigating load imbalance in parallel applications and designing memory-efficient parallel algorithms. I am an EECS PhD student at UC Berkeley, co-advisded by Jim Demmel and Aydın Buluç. I am associated with the BeBOP and the PASSION groups. I received my Bachelor's degree in Computer Engineering from UIUC in 2022, with Highest Honors and with a minor in Mathematics. I have had the privilege to work with Prof. Wen-mei Hwu, Prof. Rakesh Nagi, and Prof. Jinjun Xiong at Illinois. If you would like to learn more about me, please see my résumé or contact me via email. |
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[2] Mohammad Almasri*, Yen-Hsiang Chang*, Izzat El Hajj, Rakesh Nagi, Jinjun Xiong, Wen-mei Hwu (*Equal contribution) 32nd International Conference on Parallel Architectures and Compilation Techniques (PACT'23) arxiv / code Instead of the breadth-first approaches used in prior GPU works, we propose to parallelize maximal clique enumeration on GPUs by performing depth-first traversal using the Bron-Kerbosch algorithm. We also propose a worker list for dynamic load balancing, as well as partial induced subgraphs and a compact representation of excluded vertex sets to regulate memory consumption. Our evaluation shows that our implementation on a single GPU outperforms the state-of-the-art parallel CPU implementation by a geometric mean of 4.9X (up to 16.7X), and scales efficiently to multiple GPUs. |
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[1] Yen-Hsiang Chang, Jianhao Pu, Jinjun Xiong, Wen-mei Hwu 2021 BenchCouncil International Symposium on Benchmarking, Measuring and Optimizing (Bench'21) arxiv / code / presentation We propose MLHarness, a scalable benchmarking harness system for MLCommons Inference developed on top of the MLModelScope system with three distinctive features: (1) it codifies the standard benchmark process as defined by MLCommons Inference including the models, datasets, DL frameworks, and software and hardware systems; (2) it provides an easy and declarative approach for model developers to contribute their models and datasets to MLCommons Inference; and (3) it includes the support of a wide range of models with varying inputs/outputs modalities so that we can scalably benchmark these models across different datasets, frameworks, and hardware systems. |
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This is an academic website for Yen-Hsiang Chang to share his experiences, projects, publications. |