114 citations · 156 across the 34 of their papers we have counts for
37 papers
RVANNS: Mixed-Precision Indexing and Locality-Aware Graph Traversal on RISC-V
Chengying Huan, Yudong Liu, Jianguo Wang +9
Approximate nearest neighbor search (ANNS) on CPUs is increasingly constrained by candidate-vector movement and decoding rather than peak arithmetic throughput. Although the RISC-V…
EasyBalance: Cross-Layer Load Balancing in Distributed MoE Inference
Yize Wu, Ke Gao, Ling Li +1
Load Balancing has emerged as a critical problem in expert-parallel distributed inference of Mixture-of-Experts (MoE) models. As routing distributions are typically skewed across e…
AgenticDB: Self-Evolving Reconfiguration Framework for Database Workloads
Xinyue Yang, Chaozheng Wang, Chen Zheng +2
Configuration tuning is critical to database performance but remains difficult in real deployments. Despite notable advances, prior methods still leave substantial performance pote…
Stable-LoRA: Stabilizing Feature Learning of Low-Rank Adaptation
Yize Wu, Ke Gao, Ling Li +1
Low-Rank Adaptation (LoRA) is a widely adopted parameter-efficient method for fine-tuning Large Langauge Models. It updates the weight matrix as , where is the ori…
ECCO: Evidence-Driven Causal Reasoning for Compiler Optimization
Haolin Pan, Lianghong Huang, Jinyuan Dong +2
Compiler auto-tuning faces a dichotomy between traditional black-box search methods, which lack semantic guidance, and recent Large Language Model (LLM) approaches, which often suf…
A Large Scale Empirical Analysis on the Adherence Gap between Standards and Tools in SBOM
Chengjie Wang, Jingzheng Wu, Hao Lyu +4
A Software Bill of Materials (SBOM) is a machine-readable artifact that systematically organizes software information, enhancing supply chain transparency and security. To facilita…
Feature Slice Matching for Precise Bug Detection
Ke Ma, Jianjun Huang, Wei You +4
Measuring the function similarity to detect bugs is effective, but the statements unrelated to the bugs can impede the performance due to the noise interference. Suppressing the no…
QiMeng-Kernel: Macro-Thinking Micro-Coding Paradigm for LLM-Based High-Performance GPU Kernel Generation
Xinguo Zhu, Shaohui Peng, Jiaming Guo +10
Developing high-performance GPU kernels is critical for AI and scientific computing, but remains challenging due to its reliance on expert crafting and poor portability. While LLMs…
VecIntrinBench: Benchmarking Cross-Architecture Intrinsic Code Migration for RISC-V Vector
Liutong Han, Chu Kang, Mingjie Xing +1
Intrinsic functions are specialized functions provided by the compiler that efficiently operate on architecture-specific hardware, allowing programmers to write optimized code in a…
L2T-Tune:LLM-Guided Hybrid Database Tuning with LHS and TD3
Xinyue Yang, Chen Zheng, Yaoyang Hou +4
Configuration tuning is critical for database performance. Although recent advancements in database tuning have shown promising results in throughput and latency improvement, chall…
Exploring the Feasibility of End-to-End Large Language Model as a Compiler
Hongbin Zhang, Shihao Gao, Yang Liu +3
In recent years, end-to-end Large Language Model (LLM) technology has shown substantial advantages across various domains. As critical system software and infrastructure, compilers…
A Hybrid, Knowledge-Guided Evolutionary Framework for Personalized Compiler Auto-Tuning
Haolin Pan, Hongbin Zhang, Mingjie Xing +1
Compiler pass auto-tuning is critical for enhancing software performance, yet finding the optimal pass sequence for a specific program is an NP-hard problem. Traditional, general-p…
Synergy-Guided Compiler Auto-Tuning of Nested LLVM Pass Pipelines
Haolin Pan, Jinyuan Dong, Mingjie Xing +1
Compiler optimization relies on sequences of passes to improve program performance. Selecting and ordering these passes automatically, known as compiler auto-tuning, is challenging…
Behavioral Embeddings of Programs: A Quasi-Dynamic Approach for Optimization Prediction
Haolin Pan, Jinyuan Dong, Hongbin Zhang +3
Learning effective numerical representations, or embeddings, of programs is a fundamental prerequisite for applying machine learning to automate and enhance compiler optimization.…
AwareCompiler: Agentic Context-Aware Compiler Optimization via a Synergistic Knowledge-Data Driven Framework
Hongyu Lin, Haolin Pan, Haoran Luo +5
Compiler optimization is crucial for enhancing program performance by transforming the sequence of optimization passes while maintaining correctness. Despite the promising potentia…
GRACE: Cluster-Specific Sequence Reuse for Compiler Auto-Tuning
Haolin Pan, Chao Zha, Jinyuan Dong +2
Compiler auto-tuning aims to improve optimization quality beyond fixed compiler heuristics, but existing approaches often face a trade-off between effectiveness and deployability.…
IntrinTrans: LLM-based Intrinsic Code Translator for RISC-V Vector
Liutong Han, Zhiyuan Tan, Hongbin Zhang +4
The use of intrinsic functions to leverage hardware-specific capabilities is a crucial approach for optimizing library performance. Many mainstream libraries implement a large numb…
TuneAgent: Agentic Operating System Kernel Tuning with Reinforcement Learning
Hongyu Lin, Yuchen Li, Haoran Luo +4
Linux kernel tuning is essential for optimizing operating system (OS) performance, yet remains challenging due to the complex kernel space, sparse performance feedback, and strong…
QiMeng-Attention: SOTA Attention Operator is generated by SOTA Attention Algorithm
Qirui Zhou, Shaohui Peng, Weiqiang Xiong +11
The attention operator remains a critical performance bottleneck in large language models (LLMs), particularly for long-context scenarios. While FlashAttention is the most widely u…
Compiler-R1: Towards Agentic Compiler Auto-tuning with Reinforcement Learning
Haolin Pan, Hongyu Lin, Haoran Luo +5
Compiler auto-tuning optimizes pass sequences to improve performance metrics such as Intermediate Representation (IR) instruction count. Although recent advances leveraging Large L…
QiMeng-TensorOp: Automatically Generating High-Performance Tensor Operators with Hardware Primitives
Xuzhi Zhang, Shaohui Peng, Qirui Zhou +12
Computation-intensive tensor operators constitute over 90\% of the computations in Large Language Models (LLMs) and Deep Neural Networks.Automatically and efficiently generating hi…
KGCompiler: Deep Learning Compilation Optimization for Knowledge Graph Complex Logical Query Answering
Hongyu Lin, Haoran Luo, Hanghang Cao +6
Complex Logical Query Answering (CLQA) involves intricate multi-hop logical reasoning over large-scale and potentially incomplete Knowledge Graphs (KGs). Although existing CLQA alg…
BYOS: Knowledge-driven Large Language Models Bring Your Own Operating System More Excellent
Hongyu Lin, Yuchen Li, Haoran Luo +6
Operating system (OS) kernel tuning is a critical yet challenging problem for performance optimization, due to the large configuration space, complex interdependencies among config…
EasySpec: Layer-Parallel Speculative Decoding for Efficient Multi-GPU Utilization
Yize Wu, Ke Gao, Ling Li +1
Speculative decoding is an effective and lossless method for Large Language Model (LLM) inference acceleration. It employs a smaller model to generate a draft token sequence, which…