UBC ML Computational Cluster

点击翻译按钮获取中文摘要

Machine learning research relies heavily on the availability of computational resources. Unfortunately, due to high demand, not all faculty members are able to gain access to allocations on existing high performance computing facilities. Moreover, allocations on these systems, while suitable for extensive computation, are not designed for developmental work needed to reach a final model structure. Finally, the hardware available at these facilities is not necessarily most suited to machine learning applications, as the facilities are designed to service all disciplines. To resolve these issues, we propose setting up an extendable local computational cluster, equipped with the latest NVIDIA RTX-3090 and 100 TB of storage to support research performed by 5 faculty members and more than 50 graduate students and postdoctoral fellows in computer science and statistics, whose research is funded by NSERC, CIFAR, MITACS and other sources. This system is designed to complement allocations from existing high performance computing facilities such as Compute Canada and UBC ARC Sockeye. The system is designed to be extendable, so new hardware can be added if needed, and new faculty members can be given access in future years.