“Ten simple rules” for establishing a national scale OpenStack cloud e-infrastructure for science

The SNIC Science Cloud (SSC) team has published a paper in the 2017 conference on IEEE eScience.  SNIC Science cloud has been an infrastructure project run by the Swedish National Infrastructure for Computing (SNIC) with the purpose to assess if and how SNIC should offer cloud infrastructure to the scientific community. The project is now coming to an end, and the SSC OpenStack resources will become a part of the SNIC production infrastructure landscape. In the paper, we summarize the experiences gained in the project and provide recommendations in the form of “ten simple rules”  for how to successfully establish a science cloud community cloud infrastructure on a national scale. Read the paper here:

http://ieeexplore.ieee.org/document/8109140/

Mesoscopic-microscopic hybrid algorithm with automatic partitioning

We have developed a multiscale method coupling the mesoscopic and microscopic scales. On the mesoscopic scale, systems are modeled as discrete jump processes on a structured or unstructured grid, while on the microscopic scale, molecules are modeled by hard spheres diffusing in continuous space.

Microscopic simulations are accurate but computationally expensive. In this paper we try to automatically detect which parts of a system that need high accuracy to be accurately resolved, and which parts can be simulated on the coarser mesoscopic scale. We also extend a previously developed hybrid algorithm (http://epubs.siam.org/doi/abs/10.1137/110832148), to improve its convergence properties.

This new algorithm makes it possible to simulate larger systems with greater accuracy than before, thus significantly widening the scope of problems that can be simulated at the particle level.

The manuscript has been submitted and is under review. It is available on Arxiv at https://arxiv.org/abs/1709.00475

Stefan Hellander joins the lab

 

We are delighted to have Stefan Hellander join the lab!  Stefan obtained his Ph.D. in scientific computing from Uppsala University in 2013, with the thesis “Stochastic Simulation of Reaction-Diffusion Processes”, advised by Prof. Em. Per Lötstedt. He then went on to work as a postdoc in the lab of Prof. Linda Petzold at UCSB, until returning to Uppsala in August of 2017. His research interests include microscopic and mesoscopic modeling and simulation of reaction-diffusion systems, as well as multiscale modeling with the aim of accurately integrating the different scales.

HASTE is granted 29 MSEK funding from SSF

Our project Hierarchical Analysis of Spatial and TEmporal Data (HASTE) is granted 29 MSEK funding from SSF. The project, with PI Carolina Wählby  and co-PIs Andreas Hellander, Ola Spjuth and Mats Nilsson, will explore new ways to gain insight from massive amounts of spatial and temoral image data through hierarchical analysis models and smart cloud systems for managing data, see http://haste.research.it.uu.se.

Postdoc in Scientific Computing/Applied Cloud Computing

We are looking for a talented individual to join our efforts on creating smart and scalable cloud services to support simulation-driven scientific discovery via large-scale computational experiments such as parameter sweeps. This is a classic and very important problem that we will approach in new ways, leveraging recent advances in cloud computing, data-intensive computing and machine learning. See the full advertisement here (Deadline Sept. 1):

http://www.uu.se/en/about-uu/join-us/details/?positionId=107117

The successful candidate will contribute to the interdisciplinary research group Distributed Computing Applications (DCA) at the Department of Information Technology, Uppsala University.

 

 

try.stochss.org: Try StochSS as a Service

To make it easy to try StochSS, our software for rapid model development and simulation of stochastic regulatory networks, we are now providing it as a service on http://try.stochss.org. To use it, simply follow the link to set up your account.

Since this only require a modern browser and a valid email address (no software installation), we hope that this service will help experienced modelers evaluate the software, and importantly, that it will reduce the barrier for new modelers to explore the possibilities of stochastic simulations in systems biology.

Screen Shot 2016-06-22 at 10.12.36 AM
Screenshot showing volume rendering of a spatial stochastic simulation of a spatial negative feedback loop modeling the Hes1 regulatory network as described further in http://rsif.royalsocietypublishing.org/content/10/80/20120988

After testing StochSS, if you think it will be useful in your research, there are multiple options for you to use it on your own resources. The simplest way to get started is to download the binary package (uses Docker).

Our trial server is deployed in the SNIC Science Cloud. If you would like to provide StochSS as a service for your reserach group or for a distributed collaboration, you can do this easily on your own servers, or in another cloud infrastructure provider such as Amazon EC2. MOLNs, another member of the StochSS-suite of tools, can help you to configure and deploy an identical setup. Please do not hesitate to reach out to us if you need help with this process.

Many of you also like the possibility to work with solvers in a programming environment. All of the tools that are powering StochSS are also available as stand alone libraries:

  • PyURDME (Python API for spatial stochastic modeling and simulation )
  • Gillespy (Python API for well-mixed simulations, based on StochKit2)

In addition, if you have access to cloud infrastructure, and would like to work in a pre-configured environment powered by a Jupyther Notebook frontend and interactive parallel computing, you should check out MOLNs:

  • MOLNs: Cloud platform/orchestration framework for large-scale computational experiments such as ensembles and parameter sweeps,  backed by Jupyther and Ipython Parallel.