6 papers
Can Large Language Models Recover Semantic Optimization Opportunities That Compilers Miss?
Hailong Jiang, Feng Yu, Emran Hossain +4
Optimizing compilers miss profitable transformations when their enabling semantics are absent from the analyzed program representation. We ask whether large language models (LLMs)…
BMOA: Baseline-Mechanism-Outcome Attribution for Compiler-Induced Numerical Deviations
Hailong Jiang, Emran Hossain, Feng Yu +4
Formalizing compiler-aware numerical correctness requires distinguishing what an observed floating-point difference means, what compiler behavior the evidence supports, and what nu…
Readout-Side Bypass for Residual Hybrid Quantum-Classical Models
Guilin Zhang, Wulan Guo, Ziqi Tan +3
Quantum machine learning (QML) promises compact and expressive representations, but suffers from the measurement bottleneck - a narrow quantum-to-classical readout that limits perf…
KIS-S: A GPU-Aware Kubernetes Inference Simulator with RL-Based Auto-Scaling
Guilin Zhang, Wulan Guo, Ziqi Tan +2
Autoscaling GPU inference workloads in Kubernetes remains challenging due to the reactive and threshold-based nature of default mechanisms such as the Horizontal Pod Autoscaler (HP…
Can Large Language Models Understand Intermediate Representations in Compilers?
Hailong Jiang, Jianfeng Zhu, Yao Wan +4
Intermediate Representations (IRs) play a critical role in compiler design and program analysis, yet their comprehension by Large Language Models (LLMs) remains underexplored. In t…
MEMQSim: Highly Memory-Efficient and Modularized Quantum State-Vector Simulation
Boyuan Zhang, Bo Fang, Qiang Guan +2
In this extended abstract, we have introduced a highly memory-efficient state vector simulation of quantum circuits premised on data compression, harnessing the capabilities of bot…