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FTTN: Feature-Targeted Testing for Numerical Properties of NVIDIA & AMD Matrix Accelerators

Xinyi Li, Ang Li, Bo Fang, Katarzyna Swirydowicz, Ignacio Laguna, Ganesh Gopalakrishnan

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Tags: AMD Radeon Instinct MI100, AMD Radeon Instinct MI250X, ATI, Computer science, Hardware Architecture, HPC, Matrix multiplication, nVidia, nVidia A100, nVidia H100, nVidia V100, PTX

March 10, 2024 by hgpu

A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures

Fabrizio Ferrandi, Serena Curzel, Leandro Fiorin, Daniele Ielmini, Cristina Silvano, Francesco Conti, Alessio Burrello, Francesco Barchi, Luca Benini, Luciano Lavagno, Teodoro Urso, Enrico Calore, Sebastiano Fabio Schifano, Cristian Zambelli, Maurizio Palesi, Giuseppe Ascia, Enrico Russo, Nicola Petra, Davide De Caro, Gennaro Di Meo, Valeria Cardellini, Salvatore Filippone, Francesco Lo Presti, Francesco Silvestri, Paolo Palazzari, Stefania Perri

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Tags: AI, Artificial intelligence, Computer science, CUDA, Deep learning, Design space exploration, Hardware Architecture, Heterogeneous systems, Machine learning, Neural networks, nVidia, nVidia H100, OpenCL, survey

December 3, 2023 by hgpu

FIKIT: Priority-Based Real-time GPU Multi-tasking Scheduling with Kernel Identification

Wenqing Wu

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Tags: Computer science, CUDA, nVidia, nVidia A100, nVidia GeForce RTX 3090, nVidia GeForce RTX 4080 Ti, nVidia H100, Task scheduling

November 27, 2023 by hgpu

Scope is all you need: Transforming LLMs for HPC Code

Tal Kadosh, Niranjan Hasabnis, Vy A. Vo, Nadav Schneider, Neva Krien, Abdul Wasay, Nesreen Ahmed, Ted Willke, Guy Tamir, Yuval Pinter, Timothy Mattson, Gal Oren

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