2 papers
cs.SE2026
CodegenBench: Can LLMs Write Efficient Code Across Architectures?
Jie Li, Wenzhao Wu, Junqi Hu +5
While large language models (LLMs) have been extensively evaluated on code generation tasks for general-purpose programming and GPU-accelerated environments (e.g., PyTorch, CUDA),…
cs.DC2026
DiT-HC: Enabling Efficient Training of Visual Generation Model DiT on HPC-oriented CPU Cluster
Jinxiao Zhang, Yunpu Xu, Xiyong Wu +7
Generative foundation models have become an important tool for data reconstruction and simulation in scientific computing, showing a tight integration with traditional numerical si…