abstract
- In recent years, there has been a convergence of Big Data (BD), High Performance Computing (HPC), and Machine Learning (ML) systems. This convergence is due to the increasing complexity of long data analysis pipelines on separated software stacks. With the increasing complexity of data analytics pipelines comes a need to evaluate their systems, in order to make informed decisions about technology selection, sizing and scoping of hardware. While there are many benchmarks for each of these domains, there is no convergence of these efforts. As a first step, it is also necessary to understand how the individual benchmark domains relate. In this work, we analyze some of the most expressive and recent benchmarks of BD, HPC, and ML systems. We propose a taxonomy of those systems based on individual dimensions such as accuracy metrics and common dimensions such as workload type. Moreover, we aim at enabling the usage of our taxonomy in identifying adapted benchmarks for their BD, HPC, and ML systems. Finally, we identify challenges and research directions related to the future of converged BD, HPC, and ML system benchmarking. Access provided by Humboldt Universität zu Berlin. Download conference paper PDF Similar content being viewed by others Big Data Analytics and Machine Learning Technologies for HPC Applications Chapter © 2021 ADABench - Towards an Industry Standard Benchmark for Advanced Analytics Chapter © 2020 Advanced Data Elaboration Chapter © 2026 Explore related subjects Discover the latest articles, books and news in related subjects, suggested using machine learning. Big Data Cloud Computing Data Science Data-driven Science, Modeling and Theory Building Data Analysis and Big Data Machine Learning Big Data Analytics in Healthcare Systems