Abstract. In this paper we describe the development and application of a new web-based facility, UManSysProp (http://umansysprop.seaes.manchester.ac.uk), for automating predictions of molecular and atmospheric aerosol properties. Current facilities include pure component vapour pressures, critical properties, and sub-cooled densities of organic molecules; activity coefficient predictions for mixed inorganic–organic liquid systems; hygroscopic growth factors and CCN (cloud condensation nuclei) activation potential of mixed inorganic–organic aerosol particles; and absorptive partitioning calculations with/without a treatment of non-ideality. The aim of this new facility is to provide a single point of reference for all properties relevant to atmospheric aerosol that have been checked for applicability to atmospheric compounds where possible. The group contribution approach allows users to upload molecular information in the form of SMILES (Simplified Molecular Input Line Entry System) strings and UManSysProp will automatically extract the relevant information for calculations. Built using open-source chemical informatics, and hosted at the University of Manchester, the facilities are provided via a browser and device-friendly web interface, or can be accessed using the user's own code via a JSON API (application program interface). We also provide the source code for all predictive techniques provided on the site, covered by the GNU GPL (General Public License) license to encourage development of a user community. We have released this via a Github repository (doi:10.5281/zenodo.45143). In this paper we demonstrate its use with specific examples that can be simulated using the web-browser interface.

Passage time densities and quantiles are important performance and quality of service metrics, but their numerical derivation is, in general, computationally expensive. We present an iterative algorithm for the calculation of passage time densities in semi-Markov models, along with a theoretical analysis and empirical measurement of its convergence behaviour. In order to implement the algorithm efficiently in parallel, we use hypergraph partitioning to minimise communication between processors and to balance workloads. This enables the analysis of models with very large state spaces which could not be held within the memory of a single machine. We produce passage time densities and quantiles for very large semi-Markov models with over 15 million states and validate the results against simulation.

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Ever-increasing core counts create the need to develop parallel algorithms that avoid closelycoupled execution across all cores. In this paper we present performance analysis of several parallel asynchronous implementations of Jacobi's method for solving systems of linear equations, using MPI, SHMEM and OpenMP. In particular we have solved systems of over 4 billion unknowns using up to 32,768 processes on a Cray XE6 supercomputer. We show that the precise implementation details of asynchronous algorithms can strongly affect the resulting performance and convergence behaviour of our solvers in unexpected ways.