Zobrazeno 1 - 10
of 204
pro vyhledávání: '"Luan, Zhongzhi"'
Large language models (LLMs) have emerged as important components across various fields, yet their training requires substantial computation resources and abundant labeled data. It poses a challenge to robustly training LLMs for individual users (cli
Externí odkaz:
http://arxiv.org/abs/2406.07925
Autor:
Wang, Yiqing, Liu, Xiaoyan, Yang, Hailong, Yang, Xinyu, Wang, Pengbo, Liu, Yi, Luan, Zhongzhi, Qian, Depei
As modern HPC computing platforms become increasingly heterogeneous, it is challenging for programmers to fully leverage the computation power of massive parallelism offered by such heterogeneity. Consequently, task-based runtime systems have been pr
Externí odkaz:
http://arxiv.org/abs/2404.03226
Autor:
Wang, Siqi, Yang, Hailong, Wang, Xuezhu, Liu, Tongxuan, Wang, Pengbo, Liang, Xuning, Ma, Kejie, Feng, Tianyu, You, Xin, Bao, Yongjun, Liu, Yi, Luan, Zhongzhi, Qian, Depei
Large language models (LLM) have recently attracted surging interest due to their outstanding capabilities across various domains. However, enabling efficient LLM inference is challenging due to its autoregressive decoding that generates tokens only
Externí odkaz:
http://arxiv.org/abs/2402.15678
The increasing volume of log data produced by software-intensive systems makes it impractical to analyze them manually. Many deep learning-based methods have been proposed for log-based anomaly detection. These methods face several challenges such as
Externí odkaz:
http://arxiv.org/abs/2309.01189
Large language models (LLMs) have recently garnered significant interest. With in-context learning, LLMs achieve impressive results in various natural language tasks. However, the application of LLMs to sentence embeddings remains an area of ongoing
Externí odkaz:
http://arxiv.org/abs/2307.16645
Modern systems produce a large volume of logs to record run-time status and events. System operators use these raw logs to track a system in order to obtain some useful information to diagnose system anomalies. One of the most important problems in t
Externí odkaz:
http://arxiv.org/abs/2303.11715
Autor:
Liao, Jianjin, Li, Mingzhen, Sun, Qingxiao, Hao, Jiwei, Yu, Fengwei, Chen, Shengdong, Tao, Ye, Zhang, Zicheng, Yang, Hailong, Luan, Zhongzhi, Qian, Depei
Larger deep learning models usually lead to higher model quality with an ever-increasing GPU memory footprint. Although tensor checkpointing techniques have been proposed to enable training under a restricted GPU memory budget, the input tensor dynam
Externí odkaz:
http://arxiv.org/abs/2209.02478
Autor:
Li, Mingzhen, Xiao, Wencong, Sun, Biao, Zhao, Hanyu, Yang, Hailong, Ren, Shiru, Luan, Zhongzhi, Jia, Xianyan, Liu, Yi, Li, Yong, Lin, Wei, Qian, Depei
Distributed synchronized GPU training is commonly used for deep learning. The resource constraint of using a fixed number of GPUs makes large-scale training jobs suffer from long queuing time for resource allocation, and lowers the cluster utilizatio
Externí odkaz:
http://arxiv.org/abs/2208.14228
Deploying various deep learning (DL) models efficiently has boosted the research on DL compilers. The difficulty of generating optimized tensor codes drives DL compiler to ask for the auto-tuning approaches, and the increasing demands require increas
Externí odkaz:
http://arxiv.org/abs/2201.00194
Although the matrix multiplication plays a vital role in computational linear algebra, there are few efficient solutions for matrix multiplication of the near-sparse matrices. The Sparse Approximate Matrix Multiply (SpAMM) is one of the algorithms to
Externí odkaz:
http://arxiv.org/abs/2103.13042