1 code implementation • 9 May 2025 • JinKun Lin, Ziheng Jiang, Zuquan Song, Sida Zhao, Menghan Yu, Zhanghan Wang, Chenyuan Wang, Zuocheng Shi, Xiang Shi, Wei Jia, Zherui Liu, Shuguang Wang, Haibin Lin, Xin Liu, Aurojit Panda, Jinyang Li
Large language model (LLM) training is one of the most demanding distributed computations today, often requiring thousands of GPUs with frequent synchronization across machines.
no code implementations • 23 Mar 2024 • Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, JinKun Lin, Yangguang Mei, Lingnan Xu
In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts.
no code implementations • 9 Dec 2023 • Lingfan Yu, JinKun Lin, Jinyang Li
Large Language Models (LLMs) are wildly popular today and it is important to serve them efficiently.
no code implementations • 29 Apr 2023 • Peng Lin, Shaowei Cai, Mengchuan Zou, JinKun Lin
We propose a new local search framework that switches between three modes, namely Search, Improve, and Restore modes.
1 code implementation • 26 Jul 2022 • Jiawei Liu, JinKun Lin, Fabian Ruffy, Cheng Tan, Jinyang Li, Aurojit Panda, Lingming Zhang
In this work, we propose a new fuzz testing approach for finding bugs in deep-learning compilers.
1 code implementation • 20 Jun 2022 • JinKun Lin, Anqi Zhang, Mathias Lecuyer, Jinyang Li, Aurojit Panda, Siddhartha Sen
Our algorithm estimates the AME, a quantity that measures the expected (average) marginal effect of adding a data point to a subset of the training data, sampled from a given distribution.
no code implementations • 4 Feb 2019 • Qinyi Luo, JinKun Lin, Youwei Zhuo, Xuehai Qian
Based on a unique characteristic of decentralized training that we have identified, the iteration gap, we propose a queue-based synchronization mechanism that can efficiently implement backup workers and bounded staleness in the decentralized setting.