213 citations · 260 across the 28 of their papers we have counts for
29 papers
DB-SpMSpV: Dual-View Blocked Sparse Matrix-Sparse Vector Multiplication for Dynamic GPU Workloads
Xing Cong, Chenhao Xie, Rui Wang +3
Sparse Matrix-Sparse Vector Multiplication (SpMSpV) is a core primitive in graph traversal, sparse linear algebra, and sparse model inference. Its input vector is often dynamically…
Toward Auto-Research: Mining Falsifiable Research Ideas from Paper Knowledge Graphs with Categorical Structure
Yuchen Wang, Zhongzhi Luan
Automated research-idea generation systems built on large language models (LLMs) share a structural weakness: they reduce ideation to free-text recombination, random paper pairing,…
Can LLMs Really Recover Microservice Failures? A Recovery-Aware Evaluation of Diagnosis-to-Action Reasoning
Jiaxing Qi, Zhongzhi Luan, Hongyu Zhang +5
Large language models (LLMs) are increasingly used to interpret operational evidence and assist incident response in cloud-native microservice systems. However, recovery-oriented u…
RATrain: A Resource-Aware Training Runtime for Large Language Models on Bandwidth-Constrained Heterogeneous Supercomputing Platforms
Yao Lu, Shiqing Ma, Zhongzhi Luan +5
Production heterogeneous supercomputing platforms are increasingly used to host large language model (LLM) training workloads. However, existing GPU-oriented training runtimes typi…
Bandwidth-Aware LLM Inference on Heterogeneous Many-Core Supercomputers
Yao Lu, Zhongzhi Luan, Gen Li +6
Large language model (LLM) inference is limited by high computational cost and memory bandwidth demands, making deployment on heterogeneous many-core processors challenging. Taking…
PARNESS: A Paper Harness for End-to-End Automated Scientific Research with Dynamic Workflows, Full-Text Indexing, and Cross-Run Knowledge Accumulation
Yuchen Wang, Zhongzhi Luan
Recent autonomous research systems -- AI-Scientist, PaperOrchestra, AutoSOTA, DeepResearch, InternAgent, ResearchAgent and others -- show LLM agents can ideate, run experiments and…
PRAGMA: A Profiling-Reasoned Multi-Agent Framework for Automatic Kernel Optimization
Kelun Lei, Hailong Yang, Huaitao Zhang +5
Designing high-performance kernels requires expert-level tuning and a deep understanding of hardware characteristics. Recent advances in large language models (LLMs) have enabled a…
LOw-cOst yet High-Performant Sparse Matrix-Matrix Multiplication on Arm SME Architectures
Kelun Lei, Hailong Yang, Kaige Zhang +8
Sparse matrix-dense matrix multiplication (SpMM) is a critical kernel in both scientific computing and emerging graph learning workloads. The recent Armv9 architecture introduces S…
Strategic priorities for transformative progress in advancing biology with proteomics and artificial intelligence
Yingying Sun, Jun A, Zhiwei Liu +59
Artificial intelligence (AI) is transforming scientific research, including proteomics. Advances in mass spectrometry (MS)-based proteomics data quality, diversity, and scale, comb…
Beyond Window-Based Detection: A Graph-Centric Framework for Discrete Log Anomaly Detection
Jiaxing Qi, Chang Zeng, Zhongzhi Luan +5
Detecting anomalies in discrete event logs is critical for ensuring system reliability, security, and efficiency. Traditional window-based methods for log anomaly detection often s…
Quantum Machine Learning in Log-based Anomaly Detection: Challenges and Opportunities
Jiaxing Qi, Chang Zeng, Zhongzhi Luan +6
Log-based anomaly detection (LogAD) is the main component of Artificial Intelligence for IT Operations (AIOps), which can detect anomalous that occur during the system on-the-fly.…
On Domain-Adaptive Post-Training for Multimodal Large Language Models
Daixuan Cheng, Shaohan Huang, Ziyu Zhu +5
Adapting general multimodal large language models (MLLMs) to specific domains, such as scientific and industrial fields, is highly significant in promoting their practical applicat…
FDLoRA: Personalized Federated Learning of Large Language Model via Dual LoRA Tuning
Yao Lu, Jiaxing QI, Zhongzhi Luan +4
Large language models (LLMs) have emerged as important components across various fields, yet their training requires substantial computation resources and abundant labeled data. It…
INSPIRIT: Optimizing Heterogeneous Task Scheduling through Adaptive Priority in Task-based Runtime Systems
Yiqing Wang, Xiaoyan Liu, Hailong Yang +5
As modern HPC computing platforms become increasingly heterogeneous, it is challenging for programmers to fully leverage the computation power of massive parallelism offered by suc…
Exploring the Comprehension of ChatGPT in Traditional Chinese Medicine Knowledge
Li Yizhen, Huang Shaohan, Qi Jiaxing +3
No previous work has studied the performance of Large Language Models (LLMs) in the context of Traditional Chinese Medicine (TCM), an essential and distinct branch of medical knowl…
Minions: Accelerating Large Language Model Inference with Aggregated Speculative Execution
Siqi Wang, Hailong Yang, Xuezhu Wang +10
Large language models (LLM) have recently attracted surging interest due to their outstanding capabilities across various domains. However, enabling efficient LLM inference is chal…
LogGPT: Exploring ChatGPT for Log-Based Anomaly Detection
Jiaxing Qi, Shaohan Huang, Zhongzhi Luan +3
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-…
Scaling Sentence Embeddings with Large Language Models
Ting Jiang, Shaohan Huang, Zhongzhi Luan +2
Large language models (LLMs) have recently garnered significant interest. With in-context learning, LLMs achieve impressive results in various natural language tasks. However, the…
LogQA: Question Answering in Unstructured Logs
Shaohan Huang, Yi Liu, Carol Fung +3
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…
Mimose: An Input-Aware Checkpointing Planner for Efficient Training on GPU
Jianjin Liao, Mingzhen Li, Qingxiao Sun +8
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…
EasyScale: Accuracy-consistent Elastic Training for Deep Learning
Mingzhen Li, Wencong Xiao, Biao Sun +9
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 q…
FamilySeer: Towards Optimized Tensor Codes by Exploiting Computation Subgraph Similarity
Shanjun Zhang, Mingzhen Li, Hailong Yang +3
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…
Accelerating Sparse Approximate Matrix Multiplication on GPUs
Xiaoyan Liu, Yi Liu, Ming Dun +4
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…
The Deep Learning Compiler: A Comprehensive Survey
Mingzhen Li, Yi Liu, Xiaoyan Liu +7
The difficulty of deploying various deep learning (DL) models on diverse DL hardware has boosted the research and development of DL compilers in the community. Several DL compilers…