HIGH PERFORMANCE COMPUTING
Tech focus: cloud
ROBERT ROE LOOKS AT USE CASES FOR CLOUD TECHNOLOGY IN HPC
Cloud technologies are now reaching a level of maturity that is making
them appealing to HPC users. Whether using public or hybrid cloud, these technologies offer unprecedented flexibility for users who can create or ‘spin-up’ nodes with specific architectural requirements, use cloud bursting to increase the capacity of their in-house infrastructure – or it can increase the agility of a company that shares data over multiple sites.
In previous years there have
been some concerns around security or the cost of moving data to and from the cloud, but these reservations are slowly being eroded as more users see value in developing a cloud infrastructure as part of their HPC resource.
One aspect of designing
and procuring HPC systems in the past was the need to create a balanced architecture. This means looking at the kind of applications that will be run on a particular cluster to try and match the requirements of applications with the technologies that are needed. For example, some workloads require large memory nodes, high-speed storage or interconnects or high- performance storage.
Advancing the technology In a perfect world, all of these technologies could be
included in a single system but, in reality, this is not feasible for most HPC centres as the cost of such a system would increase drastically. Cloud HPC allows people setting up this infrastructure to make more efficient decisions, particularly if they are cloud bursting or developing a hybrid cloud strategy – as they can build their in-house resources to cater for 80 per cent of the user requirements while using the cloud to provide GPUs or specific node architectures that suit a small number of users. This allows all applications
to benefit from this balanced architectural approach, while still being able to cater to the specialised applications that have more niche requirements.
Matching resources to requirements Univa started off work in HPC as a company that designed scheduling software that helps match jobs to resources to increase the utilisation of HPC resources. By coupling this with cloud technology, the company aims to make using cloud and in-house HPC resources as economical and efficient as possible. Rob Lalonde, VP for cloud at
Univa, said: ‘We started out as a scheduler company and we were working for about eight or nine years in that scheduler/ workload management/cluster optimisation part of the world. We acquired Grid Engine from Oracle, which originally came from Sun Microsystems, and we have spent a lot of time commercialising this software, moving it from its open-source roots to the enterprise.’ ‘That is where we have spent our engineering investments over the last eight years. We have
6 Scientific Computing World August/September 2019 Sponsored by
”That is where we have spent our engineering investments over the last eight years. We have now built up a base of around 250 enterprise customers and we sit on a base of more than 3.3 million cores”
now built up a base of around 250 enterprise customers and we sit on a base of more than 3.3 million cores on-premise and various customers based in Chicago, Toronto and Munich,’ Lalonde added. This combination of
scheduling expertise combined with the growing use of cloud technologies led the company to develop software that could help to drive both activities together creating a more efficient platform to manage workloads both on-premise and in the cloud. ‘Over the last few years we
have seen a migration to the cloud, whereas three years ago we did a survey and didn’t see that cloud activity in the customer base. a little over a year and a half ago we did another survey and we found that 61 per cent of our base was migrating to the cloud,’ said Lalonde.
‘Of those customers, 70 per cent were going hybrid cloud – meaning they want to extend their cluster into the cloud – and 30 per cent wanted to run on dedicated cloud clusters for all or part of their computing requirements,’ Lalonde gave an example of
a large pharma customer that works with Univa to develop its cloud strategy. He explained that they are based on the west coast of the US, with their dataset in the cloud, but the datacentre is based on the east coast. ‘It doesn’t necessarily make sense to be pulling their data down into the on-premise datacentre in the east coast, so why not build a dedicated cloud cluster for that application which reduces latency, reduces data movement and bring it closer to the users?’ commented Lalonde. This is just one example, but it describes the kind of challenge
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