June 21, 2018 — SURF’s Lisa Compute Cluster has been expanded with a GPU cluster for extremely rapid computations. These graphics processing units are especially suitable for machine learning, and can dramatically speed up scientific research.
The Lisa Compute Cluster is a centrally managed Linux cluster that is ideally suited for large-scale computations. The new GPU cluster contains 23 nodes, each with four single precision Nvidia 1080Ti GPUs. This equates to an extension of the processing power of 1 Pflop/s (1 petaflop = 1015 single precision floating point computations per second). In addition, each node contains 1.5 TB of high-speed local storage and 256 GB of working memory. There are also two additional nodes, each with four NVIDIA Titan V (Volta) cards, which are intended for testing and experimentation.
Large-scale parallel computations
A graphics processing unit (GPU) is a processor used for large-scale parallel computations. This allows it to take over these tasks from the CPU (central processing unit). In high-performance computing, GPUs are primarily used for machine learning. They are also used for modelling and pattern recognition within research fields such as molecular dynamics, radio astronomy and transmission electron cryomicroscopy.
Lisa’s GPU extension will grow following the demand of our user communities. The extra capacity is available for all research universities with an RCCS contract. Pay-per-use is also an option. Research Capacity Computing Service (RCCS) is the SURF insourcing service unit that directly connects computing systems to the SURF member institutions.