Document Type
Conference Proceeding
Publication Date
11-1-2020
Abstract
Arm® technology is becoming increasingly important in HPC. Recently, Fugaku, an Arm®-based system, was awarded the number one place in the Top500 list. Raspberry Pis provide an inexpensive platform to become familiar with this architecture. However, Pis can also be useful on their own. Here we describe our efforts to configure and benchmark the use of a Raspberry Pi cluster with the HPX/Phylanx platform (normally intended for use with HPC applications) and document the lessons we learned. First, we highlight the required changes in the configuration of the Pi to gain performance. Second, we explore how limited memory bandwidth limits the use of all cores in our shared memory benchmarks. Third, we evaluate whether low network bandwidth affects distributed performance. Fourth, we discuss the power consumption and the resulting trade-off in cost of operation and performance.
Publication Source (Journal or Book title)
Proceedings of ESPM2 2020: 5th International IEEE Workshop on Extreme Scale Programming Models and Middleware, Held in conjunction with SC 2020: The International Conference for High Performance Computing, Networking, Storage and Analysis
First Page
11
Last Page
20
Recommended Citation
Gupta, N., Brandt, S., Wagle, B., Wu, N., Kheirkhahan, A., DIehl, P., Baumann, F., & Kaiser, H. (2020). Deploying a Task-based Runtime System on Raspberry Pi Clusters. Proceedings of ESPM2 2020: 5th International IEEE Workshop on Extreme Scale Programming Models and Middleware, Held in conjunction with SC 2020: The International Conference for High Performance Computing, Networking, Storage and Analysis, 11-20. https://doi.org/10.1109/ESPM251964.2020.00007
