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Publications of SPCL

A. Ivanov, S. Shen, G. Gottardo, M. Chrapek, A. Boudaoud, T. Schneider, L. Benini, T. Hoefler:
	PerfDojo: Automated ML Library Generation for Heterogeneous Architectures (In Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'25), presented in St. Louis, MO, USA, Nov. 2025) Abstract The increasing complexity of machine learning models and the proliferation of diverse hardware architectures (CPUs, GPUs, accelerators) make achieving optimal performance a significant challenge. Heterogeneity in instruction sets, specialized kernel requirements for different data types and model features (e.g., sparsity, quantization), and architecture-specific optimizations complicate performance tuning. Manual optimization is resource-intensive, while existing automatic approaches often rely on complex hardware-specific heuristics and uninterpretable intermediate representations, hindering performance portability. We introduce PerfLLM, a novel automatic optimization methodology leveraging Large Language Models (LLMs) and Reinforcement Learning (RL). Central to this is PerfDojo, an environment framing optimization as an RL game using a human-readable, mathematically-inspired code representation that guarantees semantic validity through transformations. This allows effective optimization without prior hardware knowledge, facilitating both human analysis and RL agent training. We demonstrate PerfLLM's ability to achieve significant performance gains across diverse CPU (x86, Arm, RISC-V) and GPU architectures. Documents download article: access preprint on arxiv: Recorded talk (best effort)
	BibTeX
@inproceedings{ivanov2025perfdojo,   author={Andrei Ivanov and Siyuan Shen and Gioele Gottardo and Marcin Chrapek and Afif Boudaoud and Timo Schneider and Luca Benini and Torsten Hoefler},   title={{PerfDojo: Automated ML Library Generation for Heterogeneous Architectures}},   year={2025},   month={11},   booktitle={Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis (SC'25)},   location={St. Louis, MO, USA},   doi={10.1145/3712285.3759900}, }