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antimicrobial resistance computing distributed computing GPUs molecular dynamics publication research

New publication: how quickly can be calculate the effect of a mutation on an antibiotic?

The idea for this paper arose during talking over coffee at the BioExcel Alchemical Free Energy workshop in May 2019. We’d previously shown that alchemical free energy methods could successfully predict which mutations in S. aureus DHFR  confer resistance to trimethoprim (and crucially, which do not). That is all well and good, but to do […]

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antimicrobial resistance clinical microbiology computing GPUs molecular dynamics publication research

New preprint: rapid prediction of AMR by free energy methods

The story behind this preprint goes back to the workshop on free energy methods run by BioExcel in Göttingen in May 2019. I gave a talk, based in part on the work I’d previously published showing how alchemical free energy methods are able to predict which mutations in S. aureus DHFR confer resistance to trimethoprim.

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computing GPUs molecular dynamics

GROMACS2018 on NVIDIA DGX-1s

HECBioSim advertised for proposals to use JADE, the new Tier-2 UK GPU high performance computer back in April 2019. JADE is built around NVIDIA DGX-1s, each of which contains 8 Tesla V100 GPUs. I’d previously run some alchemical free energy calculations on ARCHER, the Tier-1 UK academic supercomputer that has a conventional architecture, thanks to […]

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antimicrobial resistance clinical microbiology computing distributed computing GPUs

Accelerating Oxford Nanopore basecalling

It looks innocuous sitting on the desk, an Oxford Nanopore MinION, but it can produce a huge data of data from a single sequencing run. Since the nanowire works by inferring which base is in the pore by how much it reduces the flow of ions (and hence current) through the pore, the raw data […]

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computing distributed computing GPUs molecular dynamics

GROMACS on AWS: compiling against CUDA

If you want to compile GROMACS to run on a GPU Amazon Web Services EC2 instance, please first read these instructions on how to compile GROMACS on an AMI without CUDA. These instructions then explain how to install the CUDA toolkit and compile GROMACS against it. The first few steps are loosely based on these […]

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computing distributed computing GPUs molecular dynamics

GROMACS on AWS: Performance and Cost

So we have created an Amazon Machine Image (AMI) with GROMACS installed. In this post I will examine the sort of single core performance you can expect and much this is likely to cost compared to other compute options you might have. Benchmark To test the different types of instances you can deploy our GROMACS […]

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computing GPUs molecular dynamics

Running GROMACS on an AMD GPU using OpenCL

I first used an Apple Mac when I was eight. Apart from a brief period in the 1990s when I had a PC laptop I’ve used them ever since. Until last year I had an old MacPro which had four PCI slots so you could add a GPU-capable NVIDIA card, although you were limited by […]

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GPUs molecular dynamics

GROMACS 4.6: Running on GPUs

I mentioned before that I would write something on running GROMACS on GPUs. Let’s imagine we want to simulate a solvated lipid bilayer containing 6,000 lipids for 5 µs. The total number of MARTINI coarse-grained beads is around 137,000 and the box dimensions are roughly 42x42x11 nm. Although this is smaller than the benchmark we […]

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GPUs molecular dynamics

GROMACS 4.6: Scaling of a very large coarse-grained system

So if I have a particular system I want to simulate, how many processing cores can I harness to run a single GROMACS version 4.6 job? If I only use a few then the simulation will take a long time to finish, if I use too many the cores will end up waiting for communications […]