HPC the easier way: Tools and techniques for managing software and making the most of your High Performance Computing resources

Date
30 July 2019 - 12:00-13:00
Location
COM-G12-Main Lewin
Speaker
Phil Tooley, Numerical Algorithms Group (NAG)

Abstract: Modern HPC infrastructure such as ShARC at the University of Sheffield allows researchers across a broad range of fields access to resources for solving computationally complex problems. However, accessing and making best use of these resources is not always straightforward. Common problems faced by researchers wishing to use HPCs include difficulty in building the software packages that they need to use, as well as locating the documentation and information to enable them to make most efficient use of the available resources.

In this seminar I will describe some common problems faced by HPC users and demonstrate some of the available tools and techniques to overcome them. In particular I will look at how to use information gathered from previous jobs to choose the optimum parameters to submit a job with, optimising for minimum queuing time and maximum efficiency. I will also tackle the problem of installing software and dependencies, highlighting how package managers such as Conda and S-pack can be used to manage the installation of software. Using package managers can vastly reduce the complexity and time needed to manage research software as well as increasing reliability for the user.

Bio: Phil Tooley was a member of the RSE team here in Sheffield before leaving to take on the role of HPC Application analyst at the Numerical Algorithms group. He is a former theoretical and computational plasma physicist with particular interest in mathematical modelling, code optimisation and parallelism. He is an experienced developer of “traditional” parallel HPC codes using MPI and OpenMP in C, C++ and Fortran, but also champions the use of the Numpy/Scipy stack for scientific computing with python. This includes the use of accelerator technologies including Numba and Cython to write custom python code which is speed competitive with traditional compiled languages, possible in conjunction with parallel frameworks such as Dask.

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