@booklet {Pentecost2019b,
	title = {CHAMPVis: Comparative Hierarchical Analysis of Microarchitectural Performance},
	howpublished = {ProTools workshop co-located with Supercomputing},
	year = {2019},
	abstract = {Performance analysis and optimization are essential tasks for hardware and software engineers. In the age of datacenter-scale computing, it is particularly important to conduct comparative performance analysis to understand discrepancies and limitations among different hardware systems and applications. However, there is a distinct lack of productive visualization tools for these comparisons. We present CHAMPVis [1], a web-based, interactive visualization tool that leverages the hierarchical organization of hardware systems to enable productive performance analysis. With CHAMPVis, users can make definitive performance comparisons across applications or hardware platforms. In addition, CHAMPVis provides methods to rank and cluster based on performance metrics to identify common optimization opportunities. Our thorough task analysis reveals three types of datacenter-scale performance analysis tasks: summarization, detailed comparative analysis, and interactive performance bottleneck identification. We propose techniques for each class of tasks including (1) 1-D feature space projection for similarity analysis; (2) Hierarchical parallel coordinates for comparative analysis; and (3) User interactions for rapid diagnostic queries to identify optimization targets. We evaluate CHAMPVis by analyzing standard datacenter applications and machine learning benchmarks in two different case studies.},
	keywords = {deep learning, DNN benchmarking and characterization, profiling},
	url = {https://doi.org/10.1109/ProTools49597.2019.00013},
	author = {Lillian Pentecost and Udit Gupta and Ngan, Elisa and Gu  Wei and David Brooks and Beyer, Johanna and Behrisch, Michael}
}
