Paper Info
Title
Hierarchical Roofline Analysis For Gpus: Accelerating Performance Optimization For The Nersc-9 Perlmutter System
Abstract
The Roofline performance model provides an intuitive and insightful approach to identifying performance bottlenecks and guiding performance optimization. In preparation for the next-generation supercomputer Perlmutter at NERSC, this paper presents a methodology to construct a hierarchical Roofline on NVIDIA GPUs and extends it to support reduced precision and Tensor Cores. The hierarchical Roofline incorporates L1, L2, device memory, and system memory bandwidths into one single figure, and it offers more profound insights into performance analysis than the traditional DRAM-only Roofline. We use our Roofline methodology to analyze three proxy applications: GPP from BerkeleyGW, HPGMG from AMReX, and conv2d from TensorFlow. In doing so, we demonstrate the ability of our methodology to readily understand various aspects of performance and performance bottlenecks on NVIDIA GPUs and motivate code optimizations.
Year
DOI
Venue
2020
10.1002/cpe.5547
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
Keywords
DocType
Volume
code optimization, Cray, NVIDIA GPU, performance analysis, Roofline, tensor core
Journal
32
Issue
ISSN
Citations 
20
1532-0626
4
PageRank 
References 
Authors
0.48
0
3
Name
Order
Citations
PageRank
Charlene Yang140.48
Thorsten Kurth2578.36
Samuel Williams3128298.56