DistDGL: Distributed Graph Neural Network Training for Billion-Scale Graphs. - Da Zheng; Chao Ma 025; Minjie Wang; Jinjing Zhou; Qidong Su; Xiang Song 003; Quan Gan; Zheng Zhang 001; George Karypis (IA3@SC, 2020)

Distributed Hybrid CPU and GPU training for Graph Neural Networks on Billion-Scale Graphs. - Da Zheng; Xiang Song 003; Chengru Yang; Dominique LaSalle; Qidong Su; Minjie Wang; Chao Ma 025; George Karypis (CoRR, 2021)

TorchProbe: Fuzzing Dynamic Deep Learning Compilers. - Qidong Su; Chuqin Geng; Gennady Pekhimenko; Xujie Si (APLAS, 2023)

The Synergy of Speculative Decoding and Batching in Serving Large Language Models. - Qidong Su; Christina Giannoula; Gennady Pekhimenko (CoRR, 2023)

BOOM: Use your Desktop to Accurately Predict the Performance of Large Deep Neural Networks. - Qidong Su; Jiacheng Yang; Gennady Pekhimenko (PACT, 2024)

A Survey on Deep Learning for Theorem Proving. - Zhaoyu Li; Jialiang Sun; Logan Murphy; Qidong Su; Zenan Li; Xian Zhang; Kaiyu Yang; Xujie Si (CoRR, 2024)

APPL: A Prompt Programming Language for Harmonious Integration of Programs and Large Language Model Prompts. - Honghua Dong; Qidong Su; Yubo Gao; Zhaoyu Li; Yangjun Ruan; Gennady Pekhimenko; Chris J. Maddison; Xujie Si (ACL (1), 2025)

Mist: Efficient Distributed Training of Large Language Models via Memory-Parallelism Co-Optimization. - Zhanda Zhu; Christina Giannoula; Muralidhar Andoorveedu; Qidong Su; Karttikeya Mangalam; Bojian Zheng; Gennady Pekhimenko (EuroSys, 2025)

Seesaw: High-throughput LLM Inference via Model Re-sharding. - Qidong Su; Wei Zhao; Xin Li; Muralidhar Andoorveedu; Chenhao Jiang; Zhanda Zhu; Kevin Song; Christina Giannoula; Gennady Pekhimenko (CoRR, 2025)