Summary We present a methodology to enable the complete software development life cycle on Cray XC systems within a container that can hold any version of the Cray Programming Environment (CPE). The installation of the CPE inside a container facilitates many aspects of the typical HPC support and operation workloads of managing Cray XC systems such as testing new CPEs, comparing CPE performances, or keeping software built with an old CPE running on updated systems. The procedure for creating a container with a CPE inside consists of three steps: the creation of a container holding the targeted CPE, the compilation of the desired software within such containers, and the packaging of the resulting binaries, libraries, and dependencies within a second lightweight container. We showcase the methodology by fulfilling a user requirement of running a two‐year‐old version of the COSMO model built with an old CPE 16.11 on today's system.

MeteoSwiss, the Swiss national weather forecast institute, has selected densely populated accelerator servers as their primary system to compute weather forecast simulation. Servers with multiple accelerator devices that are primarily connected by a PCI-Express (PCIe) network achieve a significantly higher energy efficiency. Memory transfers between accelerators in such a system are subjected to PCIe arbitration policies. In this paper, we study the impact of PCIe topology and develop a congestion-aware performance model for PCIe communication. We present an algorithm for computing congestion factors of every communication in a congestion graph that characterizes the dynamic usage of network resources by an application. Our model applies to any PCIe tree topology. Our validation results on two different topologies of 8 GPU devices demonstrate that our model achieves an accuracy of over 97% within the PCIe network. We demonstrate the model on a weather forecast application to identify the best algorithms for its communication patterns among GPUs.

Using MPI as communication interface, one or several applications may introduce complex communication behaviors over the network cluster. This effect is increased when nodes of the cluster are multi-processors, and where communications can income or outgo from the same node with a common interval time. Our goal is to understand those behaviors to build a class of predictive models of bandwidth sharing, knowing, on the one hand the flow control mechanisms and, on the other hand, a set of experimental results. This paper present experiences that show how is shared the bandwidth on gigabit Ethernet, Myrinet 2000 and Infiniband network before to introduce the models for Gigabit Ethernet and Myrinet 2000 networks.