arXiv AI By Adhitya Charan, Adwaid Suresh, Anuj Kumar, Aparna A, Dhanakumar K, Dharun M S, Dinesh G, Goutham Kumar Reddy K, Harshini V M, Jenifa D, Jona Delcy C A, Kathirvel S, Killi Uma Maheswara Rao, Kiruthik Kanna M, Kurra Vishnu Sai, Madhumithaa G K, Navin Kumar V, Ram Charan Golla, Revathi T, Rishikkanth R, Sanjay Krishna M V, Surendra Vendra

BluTrain: A C++/CUDA Framework for AI Systems

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arXiv:2606. 24780v1 Announce Type: new Abstract: Progress in deep learning is, at scale, more a matter of systems engineering than of modelling: the behaviour of a model in training (its throughput, its memory footprint, and the numerical fidelity of the result) is determined less by the architecture itself than by how that architecture is expressed on the hardware.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

arXiv Machine Learning
Aug 4

Nova: An End-to-End MLIR Compiler for Deep Learning

arXiv:2608. 00029v1 Announce Type: cross Abstract: The performance of deep learning models at scale relies heavily on how effectively high-level mathematical operations are mapped to underlying physical hardware.

By Adwaid Suresh, Aparna A, Harshini V M, Jona Delcy C A, Killi Uma Maheswara Rao, Ram Charan Golla, Surendra Vendra
arXiv AI
4d ago

AI as a Compiler: Compiling Triton kernels without the Triton compiler

The paper explores using large language models (LLMs) to replace traditional compiler backends, a process termed AI lowering. An LLM agent translates Triton kernels directly into NVIDIA PTX, achieving 0.83x–3.34x the performance of autotuned Triton on a variety of GPUs and ML kernels. The study also extends a PTX verifier to support modern GPU features, highlighting the potential for AI compilers to reduce engineering effort for new hardware.

By Fran\c{c}ois Costa, Charly Castes, Thomas Bourgeat, Azalia Mirhoseini