A new arXiv preprint describes a method that uses predictive ranking of LLVM intermediate representation to accelerate transfer-learning-based performance autotuning in high-performance computing. The approach aims to reduce the cost of finding optimal configurations as HPC systems grow more complex. It is categorized under machine learning research.
A new arXiv paper introduces ECAS, an agentic system that uses large language models to turn a scientist's high-level goal into correct, target-scale runs on high-performance computing resources. The approach places control at the edge and gates execution behind validation checks, aiming to address how brittle and labor-intensive it currently is to translate research intent into working HPC workflows. The authors position the work as a step toward more reliable LLM-driven scientific computing.
A new arXiv paper argues that existing benchmarks for LLM-generated scientific code rely too heavily on functional correctness or task completion, which is inadequate for software built on production HPC libraries. The authors introduce an agentic evaluation framework that assesses AI-generated code in the PETSc numerical library across a broader set of criteria. The approach aims to give researchers a more thorough way to judge whether generated scientific software is fit for real-world use.
A new arXiv paper tackles how to schedule quantum circuits across systems that combine high-performance computing with several quantum processing units, where the QPUs differ in size, connectivity, native gate sets, and noise. The proposed approach factors in these hardware differences when assigning circuits, aiming to maximize fidelity on today's noisy devices where errors compound during compilation.
An updated arXiv preprint contends that low-precision FP8 matrix operations, when combined with the CRT-based Ozaki Scheme II error-compensation technique, can handle numerical workloads traditionally reserved for double-precision (FP64) hardware. The authors focus on NVIDIA's B300-class AI accelerators, claiming their tensor cores make this approach viable for a range of matrix-dominated scientific computing fields. The paper is the first part of a series challenging the assumption that dedicated FP64 units are essential for high-performance computing.