Imperial CMSE provides a forum to advertise and promote news and events aligned with CMSE objectives. Find below a list of upcoming events.



Firedrake tutorial

Date: January 12th, 2018
Time: 14:00 - 17:00
Venue: EPSRC CDT space, access from Sherfield 2nd floor  (building 20 on the map)
Campus: South Kensington Campus, Imperial College London

Firedrake ( is a system which automates the numerical solution of partial differential equations using the finite element method. Firedrake users write high level mathematical code for the PDEs they wish to solve, and high performance parallel implementations are automatically generated at runtime. Firedrake makes it vastly easier to create high performance simulations, and even automates the creation of an adjoint simulation. Users at Imperial and around the world employ Firedrake for a wide range of forward and inverse simulation challenges.

The Firedrake team will be offering a half-day hands-on Firedrake tutorial at Imperial on 12 January 2018. If you're considering using Firedrake or are just curious about it, you're welcome to come and participate. The tutorial is free but registration is required. For more information and to sign up, go to:



A Parallel Framework for Generalized N-body Problems

 Speaker: Aparna Chandramowlishwaran, University of California, Irvine

You are warmly invited to this research talk, which should be of wide interest in spanning machine learning, data mining, computational science, and compiler technology.  She gave a terrific talk at a Dagstuhl Seminar a few weeks ago and I am pleased and excited to have her visit us.  If you would like to arrange a meeting, please contact Dr Pablo Gonzalez de Aledo (a senior RA in my group) who is hosting her visit.


Date: December 4th, 2017
Time: 10:00 - 11:00
Venue: room 342, Huxley  (building 13 on the map)
Campus: South Kensington Campus, Imperial College London



In this talk, I'll present our work on addressing key challenges in developing parallel algorithms and software for the class of N-body problems on current and future platforms. Our goal is to reduce the apparent gap in performance between code generated from high-level forms and that of hand-tuned code, which we address using extensive characterization of the optimization space for these computations and automating the process through domain-specific code generators. These application-specific compilers provide the domain scientists the ability to productively harness the power of these large machines and to enable large-scale scientific simulations and big data applications. 

In this talk, I’ll present Portal, a new high-performance domain-specific language (DSL) and code-generator designed to enable high-performance implementations of N-body problems on modern multicore machines. Our goal in the development of Portal is three-fold, (a) to implement scalable, fast algorithms which have O(n log n} and O(n) complexity, (b) design an intuitive language and API to enable rapid implementations of a variety of algorithms, and (c) to enable parallel large-scale problems to run on current and future architectures. The initial target includes N-body problems in machine learning and data mining such as k-nearest neighbors, kernel density estimation, Expectation Maximization, to name a few which are commonly used in various applications. To our knowledge, there are no known libraries or currently available frameworks that implement parallel machine learning N-body algorithms and Portal aims to fill this gap. Moreover, our language and intermediate algorithm representation are independent of the architecture, making our approach portable and easily extensible for different platforms. 


Aparna Chandramowlishwaran is an assistant professor at the University of California, Irvine, in the Department of Electrical Engineering and Computer Science. Her research lab, HPC Factory, is interested in "all-things" high-performance computing with an emphasis on parallel algorithms, performance analysis and tuning, and domain-specific compilers. She is a recipient of the Intel Ph.D. fellowship (2012), ACM/IEEE George Michael Memorial HPC fellowship (2010), Best Paper Award at IEEE Parallel and Distributed Processing Symposium (IPDPS, 2010), and co-recipient of the ACM Gordon Bell Prize (2010) among others. She received her Ph.D. in Computational Science and Engineering from Georgia Institute of Technology (Georgia Tech) in 2013 and was a research scientist at MIT Computer Science and Artificial Intelligence Laboratory (CSAIL) with the Compilers at MIT (COMMIT) research group.



HPC summer school 2017

September 18 - 22, 2017

The third instalment of the HPC summer school will be held on September 18 -22 in SAF 121 and 120, South Kensington campus.

The programme includes a two-day scientific python tutorial for beginners, a one-day workshop on deep learning and a thorough introduction to Amazon Web Services.   In addition, we are planning a Research Software Engineering session, a session in Bioinformatics and Research Data Management.  The last day of summer school is dedicated to quantum technologies.  

You can register for separate workshops or community sessions.  All are welcome!