The Oceanographic Multipurpose Software Environment (OMUSE, [1]) is an open source framework developed for oceanographic and other earth system modelling applications. OMUSE provides a homogeneous environment to interface with numerical simulation codes. It was developed at the IMAU (Utrecht) using coupling technology developed for astrophysical applications in the AMUSE project at Leiden Observatory[2,3]. OMUSE simplifies the use and deployment of numerical simulations codes. Furthermore, the design of the OMUSE interfaces (figure 1) allow codes that represent different physics or span different ranges of physical scales to be easily combined in novel numerical experiments. The use cases for OMUSE range from running simple numerical experiments with single codes and the addition of data analysis tools in model runs, to setting up fairly complicated and strongly coupled solvers for problems that are intrinsically multi-scale and/or require different physics. Here, we will present the design of OMUSE as well as give examples of the types of the couplings that can be implemented using OMUSE. The example provided by AMUSE and OMUSE suggests that application of the same interfacing philosophy to a more extensive set of disciplines is possible. In order to facilitate this a better separation of the core framework and domain specific code is necessary. We will present ongoing work to support meteorological and hydrological applications and the use of the framework as the computational core in the eWatercycle project [4]. For this, adaptations are made to improve the interoperability with existing interface efforts (such as the BMI) and we discuss developments regarding the encapsulation of OMUSE/AMUSE and its component models in containers. This will facilitate the installation for first time users, removing a barrier in this respect. In addition to this we anticipate this to also offer more flexible deployment options for the framework.

This talk will give a brief introduction to OMUSE, the Oceanographic Multipurpose Software Environment, which is currently being developed. OMUSE is a Python framework that provides high-level object-oriented interfaces to existing or newly developed numerical ocean simulation codes, simplifying their use and development In this way, OMUSE facilitates the efficient design of numerical experiments that combine ocean models representing different physics or spanning different ranges of physical scales, for example coupling a global open ocean simulation with a regional coastal ocean model. OMUSE enables its users to write high-level Python scripts that describe simulations. The functionality provided by OMUSE takes care of the low-level integration with the code and deploying simulations on high-performance computing resources, allowing its users to focus on the physics of the simulation. We give an overview of the design of OMUSE and the modules and model components currently included. In particular, we will discuss the process of creating a new OMUSE interface to an existing code, and explain how OMUSE keeps track of the internal state of a running simulation. In addition, we will discuss the grid data types and grid remapping functionality that OMUSE provides. We also give an example of performing online data analysis on a running simulation, which is becoming increasingly important as models simulate a broader range of scales, generating large datasets that cannot be fully stored for offline analysis.

We describe the political and technical complications encountered during the astronomical CosmoGrid project. CosmoGrid is a numerical study on the formation of large scale structure in the universe. The simulations are challenging due to the enormous dynamic range in spatial and temporal coordinates, as well as the enormous computer resources required. In CosmoGrid we dealt with the computational requirements by connecting up to four supercomputers via an optical network and make them operate as a single machine. This was challenging, if only for the fact that the supercomputers of our choice are separated by half the planet, as three of them are located scattered across Europe and fourth one is in Tokyo. The co-scheduling of multiple computers and the 'gridification' of the code enabled us to achieve an efficiency of up to 93% for this distributed intercontinental supercomputer. In this work, we find that high-performance computing on a grid can be done much more effectively if the sites involved are willing to be flexible about their user policies, and that having facilities to provide such flexibility could be key to strengthening the position of the HPC community in an increasingly Cloud-dominated computing landscape. Given that smaller computer clusters owned by research groups or university departments usually have flexible user policies, we argue that it could be easier to instead realize distributed supercomputing by combining tens, hundreds or even thousands of these resources.

We have simulated, for the first time, the long term evolution of the Milky Way Galaxy using 51 billion particles on the Swiss Piz Daint supercomputer with our N-body gravitational tree-code Bonsai. Herein, we describe the scientific motivation and numerical algorithms. The Milky Way model was simulated for 6 billion years, during which the bar structure and spiral arms were fully formed. This improves upon previous simulations by using 1000 times more particles, and provides a wealth of new data that can be directly compared with observations. We also report the scalability on both the Swiss Piz Daint and the US ORNL Titan. On Piz Daint the parallel efficiency of Bonsai was above 95%. The highest performance was achieved with a 242 billion particle Milky Way model using 18600 GPUs on Titan, thereby reaching a sustained GPU and application performance of 33.49 Pflops and 24.77 Pflops respectively.

Star cluster ecology is the field of research where stellar evolution, gravitational dynamics, hydrodynamcs and the background potential dynamics of the parent galaxy interact to a complex non-­-linear evolution of self gravitating stellar systems. I will review the processes related to the ecology of stellar clusters, discuss the numerical hurdles and the physical principles. In addition, I will introduce the AMUSE framework with which we are performing simulations of the ecology of stellar clusters. AMUSE is a general purpose framework for interconnecting existing scientific software with a homogeneous and unified interface. Since the framework is based on the standard message passing interface, any production ready code that is written in a language that supports its native bindings can be incorporated; in addition, our framework is intrinsically parallel and it conveniently separates all the numerical solvers in memory. The strict separation also enables the possibility to realize unit conversion between the different modules and to recover from fatalities in a unified and structured way. The time spent in the framework is relatively small, and for production simulations we measured an overhead of at most 10%, which in our case is acceptable. Due to the unified structure of the interface, incorporating new modules which address the same physics is relatively straightforward. The time stepping between the codes can be simply consecutive or realized via a mixed variable symplectic method in which the Hamiltonian of the problem is solved in separate steps and combined via a Verlet-­-leapfrog integration scheme. In our experience with an implementation for multiphysics simulations in astrophysics, we encounter relatively few problems with the strict separation in methods, and the results of our test simulations are consistent with earlier results that use a monolithic framework.