Posted: 8 Nov 2017 | 10:13
Are you interested in using machine learning for something big enough to need supercomputing resources?
Have you worked on, or with, one of the Deep Learning frameworks, like TensorFlow or Caffe?
Are you just curious about the state-of-the-art at the crossover between AI and HPC?
Posted: 16 Aug 2017 | 15:59
I recently attended the 2017 Flash Memory Summit, a conference primarily aimed at storage technology and originally based around flash memory, although it has expanded to cover all forms of non-volatile storage technology.
Non-volatile memory is a big deal nowadays. It is memory that stores data even when it has no power (unlike the volatile memory in computers that lose data when power is switched off). Flash memory is a particular form for non-volatile memory, it's been used for a long time, and has had a massive impact on consumer technology, from the storage in your cameras and phones, to SSD hard drives routinely installed in laptop and desktop systems.
Posted: 15 Jun 2017 | 13:41
We are entering the fourth year of the Intel Parallel Computing Centre (IPCC). This collaboration on code porting and optimisation has focussed on improving the performance of scientific applications on Intel hardware, specifically its Xeon and Xeon Phi processors.
Posted: 15 Jun 2017 | 11:59
Posted: 15 Jun 2017 | 11:49
Reaching the exascale has been a focus of the HPC community for several years, and EPCC has been a key player from the beginning.
Posted: 24 May 2017 | 19:30
When we parallelise and optimise computational simulation codes we always have choices to make. Choices about the type of parallel model to use (distributed memory, shared memory, PGAS, single sided, etc), whether the algorithm used needs to be changed, what parallel functionality to use (loop parallelisation, blocking or non-blocking communications, collective or point-to-point messages, etc).
Posted: 11 May 2017 | 00:06
As part of the ARCHER Knights Landing (KNL) processor testbed, we have produced and collected a set of benchmark reports on the performance of various scientific applications on the system. This has involved the ARCHER CSE team, EPCC's Intel Parallel Computing Center (IPCC) team, and various users of the system all benchmarking and documenting the performance they have experienced.
Posted: 11 Apr 2017 | 17:59
Shall I compare thee...
Performance comparisons are always tricky to get exactly right. They are needed to ensure that we can demonstrate the performance improvements that optimisations, new hardware, new algorithms, etc... have had on an application or benchmark, but there is a lot of latitude in what can be compared, which makes it easy to get a performance comparison wrong and not properly demonstrate whatever it is you're trying to show.
Posted: 4 Apr 2017 | 14:51
Posted: 10 Mar 2017 | 15:39
Measuring performance is a key part of any code optimisation or parallelisation process. Without knowing the baseline performance, and what has been achieved after the work, it's impossible to judge how successful any intervention has been. However, it's something that we, as a community, get wrong all the time, at least when we present our results in papers, presentation, blog posts, etc... I'm not suggesting that people aren't measuring performance correctly, or are deliberately falsifying performance improvements, but the incentives to make your work look as impressive as possible causes people to present results in a way that really isn't justified.