NonGEMM Bench: Understanding the Performance Horizon of the Latest ML Workloads with NonGEMM Workloads

arXiv:2404.11788v1 Announce Type: cross
Abstract: Machine Learning (ML) operators are the building blocks to design ML models with various target applications. GEneral Matrix Multiplication (GEMM) operators are the backbone of ML models. They are notorious for being computationally expensive requiring billions of multiply-and-accumulate. Therefore, significant effort has been put to study and optimize the GEMM operators in order to speed up the execution of ML models. GPUs and accelerators are widely deployed to accelerate ML workloads by optimizing the execution …

abstract applications arxiv building cs.ar cs.lg cs.pf design general horizon machine machine learning matrix matrix multiplication ml models operators performance type understanding workloads