171 citations · 174 across the 2 of their papers we have counts for
3 papers · 1 filter
KForge: Program Synthesis for Diverse AI Hardware Accelerators
Taras Sereda, Tom St. John, Burak Bartan +3
GPU kernels are critical for ML performance but difficult to optimize across diverse accelerators. We present KForge, a platform-agnostic framework built on two collaborative LLM-b…
MLPerf HPC: A Holistic Benchmark Suite for Scientific Machine Learning on HPC Systems
Steven Farrell, Murali Emani, Jacob Balma +40
Scientific communities are increasingly adopting machine learning and deep learning models in their applications to accelerate scientific insights. High performance computing syste…
MLPerf Training Benchmark
Peter Mattson, Christine Cheng, Cody Coleman +34
Machine learning (ML) needs industry-standard performance benchmarks to support design and competitive evaluation of the many emerging software and hardware solutions for ML. But M…