Shaowei Zhu

Genghan Zhang, Shaowei Zhu, Anjiang Wei et al. · stanford

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-provided hardware-specific optimization knowledge. AccelOpt explores the kernel optimization space through iterative generation, informed by an optimization memory that curates experiences and insights from previously encountered slow-fast kernel pairs. We build NKIBench, a new benchmark suite of AWS Trainium accelerator kernels with varying complexity extracted from real-world LLM workloads to evaluate the effectiveness of AccelOpt. Our evaluation confirms that AccelOpt's capability improves over time, boosting the average percentage of peak throughput from $49\%$ to $61\%$ on Trainium 1 and from $45\%$ to $59\%$ on Trainium 2 for NKIBench kernels. Moreover, AccelOpt is highly cost-effective: using open-source models, it matches the kernel improvements of Claude Sonnet 4 while being $26\times$ cheaper.

Kahfi S. Zulkifli, Wenbo Qian, Shaowei Zhu et al.

Modern machine learning frameworks support very large models by incorporating parallelism and optimization techniques. Yet, these very techniques add new layers of complexity, introducing silent errors that severely degrade model performance. Existing solutions are either ad hoc or too costly for production. We present Scalify, a lightweight framework that exposes silent errors by verifying semantic equivalence of computational graphs using equality saturation and Datalog-style reasoning. To scale, Scalify partitions graphs with parallel rewriting and layer memoization, reuses rewrite templates, and augments equality saturation with relational reasoning and symbolic bijection inference. It further localizes discrepancies to precise code sites, turning verification results into actionable debugging guidance. Scalify verifies models as large as Llama-3.1-405B within minutes on a commodity machine and exposed five unknown bugs in Amazon production machine learning frameworks.