arXiv subjects

Shin-Rong Tsai

Publications and source records attributed to Shin-Rong Tsai.

2 recordsLinked to original sources

arXiv2025

libyt: an In Situ Interface Connecting Simulations with yt, Python, and Jupyter Workflows

In the exascale computing era, handling and analyzing massive datasets have become extremely challenging. In situ analysis, which processes data during simulation runtime and bypasses costly intermediate disk input and output steps, offers a promising solution. We present libyt (https://github.com/yt-project/libyt), an open-source C library that enables astrophysical simulations to analyze and visualize data in parallel computation with yt or other Python packages. libyt can invoke Python routines automatically or provide interactive entry points via a Python prompt or a Jupyter Notebook. It requires minimal intervention in researchers' workflow, allowing users to reuse job submission scripts and Python routines. We describe libyt's architecture for parallel computing in high-performance computing environments, including its bidirectional connection between simulation codes and Python, and its integration into the Jupyter ecosystem. We detail its methods for reading patch-based adaptive mesh refinement (AMR) simulations and handling in-memory data with minimal overhead, and procedures for yielding data when requested by Python. We describe how libyt maps simulation data to yt frontends, allowing post-processing scripts to be converted into in situ analysis with just two lines of change. We document libyt's API and demonstrate its integration into two astrophysical simulation codes, GAMER and Enzo, using examples including core-collapse supernovae, isolated dwarf galaxies, fuzzy dark matter, the Sod shock tube test, Kelvin-Helmholtz instability, and the AGORA galaxy simulation. Finally, we discuss libyt's performance, limitations related to data redistribution, extensibility, architecture, and comparisons with traditional post-processing approaches.

astro-ph.IM↗

arXiv2025

CRABS: A syntactic-semantic pincer strategy for bounding LLM interpretation of Python notebooks

Recognizing the information flows and operations comprising data science and machine learning Python notebooks is critical for evaluating, reusing, and adapting notebooks for new tasks. Investigating a notebook via re-execution often is impractical due to the challenges of resolving data and software dependencies. While Large Language Models (LLMs) pre-trained on large codebases have demonstrated effectiveness in understanding code without running it, we observe that they fail to understand some realistic notebooks due to hallucinations and long-context challenges. To address these issues, we propose a notebook understanding task yielding an information flow graph and corresponding cell execution dependency graph for a notebook, and demonstrate the effectiveness of a pincer strategy that uses limited syntactic analysis to assist full comprehension of the notebook using an LLM. Our Capture and Resolve Assisted Bounding Strategy (CRABS) employs shallow syntactic parsing and analysis of the abstract syntax tree (AST) to capture the correct interpretation of a notebook between lower and upper estimates of the inter-cell I/O set$\unicode{x2014}$the flows of information into or out of cells via variables$\unicode{x2014}$then uses an LLM to resolve remaining ambiguities via cell-by-cell zero-shot learning, thereby identifying the true data inputs and outputs of each cell. We evaluate and demonstrate the effectiveness of our approach using an annotated dataset of 50 representative, highly up-voted Kaggle notebooks that together represent 3454 actual cell inputs and outputs. The LLM correctly resolves 1397 of 1425 (98%) ambiguities left by analyzing the syntactic structure of these notebooks. Across 50 notebooks, CRABS achieves average F1 scores of 98% identifying cell-to-cell information flows and 99% identifying transitive cell execution dependencies.

cs.CL↗