Author

Ajit Mathews

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#machine learning Preprint Oct 2026

Architecting for Hardware Diversity: Meta's End-to-End Co-Design for High-Performance AI Training on AMD GPUs

Meta deploys a heterogeneous fleet comprising NVIDIA GPUs, AMD GPUs, and proprietary MTIA accelerators to reduce costs and mitigate vendor lock-in. However, the extensive history of NVIDIA-specific optimizations hinders performance portability to other hardware. This paper presents our methods for optimizing alternativ...

Hao-Yu Zhang, Arshad Rahman, Adnan Aziz et al. · 0 citations

#artificial intelligence Preprint Sep 2026

Crossflow: Prefill-Decode Elasticity for Agentic LLM Serving

This work presents Crossflow, which makes this boundary elastic without changing node roles, and improves token throughput by 16.2-17.4% on geometric mean over static P/D, and by up to 43.4% at high load, while reducing mean TTFT at every evaluated point.

Yi Xu, Ehsan K. Ardestani, Wen-Yin Fu et al. · 0 citations

Preprint Jul 2026

Triton for MTIA: Bridging the Programming Model Gaps for Custom AI Accelerators

This work presents the first production-scale application of Triton on a custom ML accelerator, MTIA-2i, developed by Meta, and develops a new compiler backend that targets it, introduces enhancements to TorchInductor code generation, and proposes minimal language extensions that expose MTIA-specific architectural feat...

Haishan Zhu, Domi Yan, Michael Levesque-Dion et al. · 0 citations