2 citations · 2 across the 10 of their papers we have counts for
10 papers
PTXBench: Benchmarking and Adapting LLMs for GPU Kernel Optimization with Architecture-specific PTX
Genghan Zhang, Yixin Dong, Chengze Fan +4
We introduce PTXBench, a benchmark for evaluating and adapting large language models (LLMs) to use architecture-specific PTX for GPU kernel optimization. PTXBench measures function…
Axon: A Synthesizing Superoptimizer for Tensor Programs
Akash Kothari, Shaowei Zhu, Daniel Kroening +1
Writing high performance kernels for AI accelerators requires deep expertise in tiling, instruction selection, data layout, and operator fusion placing a significant burden on prog…
MKEvolve: A Modular Multi-Agent Framework for Kernel Code Generation
Jason Yoo, Rajarshi Saha, Shaowei Zhu +3
Despite rapid progress in LLM-based code generation, writing correct and performant kernels for hardware accelerators remains a key bottleneck in scaling modern ML workloads. We pr…
Ekka: Automated Diagnosis of Silent Errors in LLM Inference
Yile Gu, Zhen Zhang, Shaowei Zhu +4
LLM serving frameworks are quickly evolving with a complex software stack and a vast number of optimizations. The rapid development process can introduce silent errors where output…
A Categorical Basis for Robust Program Analysis
Zachary Kincaid, Shaowei Zhu
Users of program analyses expect that results change predictably in response to changes in their programs, but many analyses fail to provide such robustness. This paper introduces…
AccelOpt: A Self-Improving LLM Agentic System for AI Accelerator Kernel Optimization
Genghan Zhang, Shaowei Zhu, Anjiang Wei +6
We present AccelOpt, a self-improving large language model (LLM) agentic system that autonomously optimizes kernels for emerging AI acclerators, eliminating the need for expert-pro…
TritonRL: Training LLMs to Think and Code Triton Without Cheating
Jiin Woo, Shaowei Zhu, Allen Nie +3
The rapid evolution of Large Language Models (LLMs) has driven a growing demand for automated, high-performance system kernels to accelerate machine learning workloads. We introduc…
Verifying Computational Graphs in Production-Grade Distributed Machine Learning Frameworks
Kahfi S. Zulkifli, Wenbo Qian, Shaowei Zhu +3
Modern machine learning frameworks support very large models by incorporating parallelism and optimization techniques. Yet, these very techniques add new layers of complexity, intr…
TTrace: Lightweight Error Checking and Diagnosis for Distributed Training
Haitian Jiang, Shaowei Zhu, Zhen Zhang +5
Distributed training is essential for scaling the training of large neural network models, such as large language models (LLMs), across thousands of GPUs. However, the complexity o…
Breaking the Mold: Nonlinear Ranking Function Synthesis Without Templates
Shaowei Zhu, Zachary Kincaid
This paper studies the problem of synthesizing (lexicographic) polynomial ranking functions for loops that can be described in polynomial arithmetic over integers and reals. While…