Xulu Chu

I am a Master’s student in Computer Science at DePaul University, working with Professor Tanu Malik at the DICE Lab. My research interests span across:

Primary Research Areas:

Databases and Data Management
High Performance Computing
AI for Scientific Computing
Systems Development

Specific Research Focus:

Big Data Systems for Scientific Computing
Data Provenance and Reproducibility
Scientific Data Management

I am planning to apply for PhD programs in Computer Science this year, with a focus on advancing research in scientific data management and reproducible computing.

In my free time, I enjoy running🏃‍♀️.

Publication

SSDBM
Accurate Differential Analysis using Record and Selective Replay

Yuta Nakamura, Xulu Chu, Tanu Malik, and 1 more author

International Conference on Scientific and Statistical Database Management (SSDBM), 2025

Abs Bib

To verify the reproducibility of an application, it is often necessary to execute it multiple times, each time with a different input, and evaluate whether the application outcome changes. Given the complexity of the software, any unexpected behavior in the outcome requires quick insight into application behavior. Efficient execution tracing is instrumental but traces must often be compared to attain that insight. Comparing execution traces is a significant challenge, especially for parallel MPI applications as tasks may exchange messages in a non-deterministic order. Our results demonstrate that, without selective replay, multiple sources of non-determinism lead to numerous false positives in comparing two runs. Through selective replay, we uncover and explain all divergences and convergences, achieving a reduction in false positives by more than 50%.
@article{lu2025accurate, title = {Accurate Differential Analysis using Record and Selective Replay}, author = {Nakamura, Yuta and Chu, Xulu and Malik, Tanu and Laguna, Ignacio}, journal = {International Conference on Scientific and Statistical Database Management (SSDBM)}, year = {2025}, }

Differential Analysis in Scientific Computing

My research explores reproducibility and trace-based debugging in distributed systems. I am a co-author of the paper "Accurate Differential Analysis using Record and Selective Replay" (SSDBM 2025), led by Yuta Nakamura. This work addresses the challenge of comparing execution traces in parallel MPI applications, where tasks may exchange messages in a non-deterministic order.

Our approach uses selective replay to reduce false positives by more than 50% when comparing execution runs, enabling more precise identification of divergence points in scientific applications. I contributed to the experimental design and evaluation of this method on real-world MPI benchmarks.

I have been introduced to the Floability Project, an NSF-funded research initiative led by Professor Tanu Malik. This project aims to enable the rapid and portable deployment of notebooks expressing complex scientific workflows across various cyberinfrastructure by addressing workflow incompleteness through capturing software dependencies, required datasets, and cluster hardware capabilities.

The Floability Project is a collaborative effort between the University of Notre Dame, the University of Missouri, and the University of Illinois, leveraging technologies like Jupyter, Sciunit, TaskVine, and Conda Forge.

Publication

Research

Differential Analysis in Scientific Computing

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