arXiv:2511. 10480v3 Announce Type: replace-cross Abstract: Optimizing the performance of large language models (LLMs) on large-scale AI training and inference systems requires a scalable and expressive mechanism to model distributed workload execution.
By Changhai Man, Joongun Park, Hanjiang Wu, Huan Xu, Srinivas Sridharan, Tushar Krishna
arXiv:2606. 04023v1 Announce Type: cross Abstract: While large language models (LLMs) have been extensively evaluated on code generation tasks for general-purpose programming and GPU-accelerated environments (e.
By Jie Li, Wenzhao Wu, Junqi Hu, Qinrui Zheng, Bowen Wu, Juepeng Zheng, Yutong Lu, Haohuan Fu
arXiv:2603.24595v2 Announce Type: replace-cross
Abstract: Large language model (LLM) inference systems rely on CUDA kernels for core GPU computations, yet the interface between models and kernels is...
By Mengting He, Shihao Xia, Haomin Jia, Wenfei Wu, Linhai Song
The paper introduces OmniEvaluator, a composable evaluation system designed to streamline reproducible testing of omni‑modal foundation models across text, image, video, and audio. It unifies disparate inference engines, prompt conventions, and metric implementations by providing a single interface that supports four inference backends, four evaluation frameworks, and over a thousand benchmarks. Each evaluation run is logged as an artifact for exact reproducibility, with results displayed on a shared dashboard; a federated mode allows GPU inference servers to be shared, and a lightweight verifier ensures stable scoring across engines and prompts without incurring API costs.
By Hodong Lee, Sanghee Park, Dohoon Ryu, Jungwhan Kim, Junyeob Kim, Soyoon Kim, Geewook Kim
arXiv:2606. 07665v1 Announce Type: cross Abstract: Transformer inference increasingly depends on specialized compiler and runtime support, but real model graphs still require semantic decisions about which regions are worth specializing and which CUDA implementation families are plausible.
By Xuanzhe Li, Ziyan Weng, Zhiyu Zhu, Junhui Hou