As we move into the exascale era and beyond, high performance computing systems will become more and more resource constrained, and they will face this problem with a growing number of different resources. To solve this problem we need new and more adaptive resource management approaches that can deal with multi-constraint scenarios and that can adjust themselves to changing conditions in the system. In the first part of the talk, I will discuss these challenges using constraints on power and energy as an example and will show how this, in some cases, can have unexpected consequences on application performance.
To solve these challenges, however, we first need to better understand the exact behavior of our systems, their bottlenecks and the impact our workloads have on them. This requires a system wide monitoring and performance data management - from system level measurements to application feedback - combined with the matching analytics capabilities. In the second part of the talk I will discuss concepts to enable such monitoring, how they can be used to feed user facing tools, as well as can be used for new resource management schemes. This is part of a first step towards a more efficient utilization of the scarce resources and can ultimately lead to new design tradeoffs for future systems.
This talk is about experience implementing machine learning in a fully decentralized way on low cost home devices, which can potentially lead to large improvements in privacy. The two-sided market of Cloud Analytics emerged almost accidentally, initially from click-through associated with users’ response to search results, and then adopted by many other services, whether web mail or social media. The business model seen by the user is of a free service (storage and tools for photos, video, social media etc). The value to the provider is untrammeled access to the users' data over space and time, allowing upfront income from the ability to run recommenders and targeted adverts, to background market research about who is interested in what information, goods and services, when and where. The value to the user is increased personalisation. This all comes at a cost, both of privacy (and the risk of loss of reputation or even money) for the user, and at the price of running highly expensive data centers for the providers, and increased cost in bandwidth and energy consumption (mobile network costs & device battery life). The attack surface of our lives expands to cover just about everything.
This talk will examine several alternative directions that this will evolve in the future.
AV material (video, audio, text, images) forms the backbone of the BBC’s current output, recent storage and historical archives. Our R&D teams work across a number of different domains. One specific problem space involves dealing with huge amounts of data split across sites, spanning many broadcast channels and formats. Our research helps the BBC create, capture, store, and analyse multimedia content.
