HIGH PERFORMANCE COMPUTING
Deluge of data
Hsueh-Li Wang discusses HPC trends that include data analysis, virtualisation and how hardware developers are looking to solve the challenges in HPC performance
What are the primary factors driving change in HPC? If we look at HPC, we can split the market into scientific simulation, scientific research, artificial intelligence (AI), and virtualisation. After that, there will be any other generic general purpose compute, either with CPU or GPU. Current research areas in HPC are
focused on medical research, biosciences, geoscience, weather forecasting, demographic data analysis and so on. There has been a rise in the use of demographic data analysis due to the increasing use of big data, and also the fact that computing technologies are gradually becoming more miniaturised. Today, you can deploy very high
performance, high-power compute nodes at multiple locations and remote locations even where there is no data centre. Now the computing capability of a single device is so strong, just one small device can do almost any kind of analysis if you set it up correctly. For example, if I take a smart, edge
device, the size of a smartphone, it can be installed on any telephone post or power pole, outdoor or indoor, even in a supermarket. That node can be used to collect and analyse data on facial recognition, object detection, object recognition, traffic flow, human traffic flow and so on. The rise in demographic data analysis is due to all these end-use cases, and also the fact that data can now be collected on a more granular level. What I mean is that, in the past, when you collected data, it was straightforward in
8 Scientific Computing World Autumn 2021
terms of data structure. Data collection was focused on just a few simple data points – for example, gender, race and maybe skin colour. Now almost all the end-use cases
are moving to 3D or multi-dimensional structures. This more complex data collection means that for a set of demographic data, you can have facial features, race, ethnicity, skin colour, emotions, attire, age, height, weight and even the speed they are walking. AI and HPC end-use cases are seeing
an increasing trend towards demographic data, because now we can collect more data, data is becoming more complex, and compute nodes are becoming more powerful. The fact that data is becoming more
complex is also applicable to other areas of HPC research. When you look at HPC as a whole, in terms of scientific research, scientific simulation, all the trends have one thing in common: data is
becoming more complex. Now we have the databases; the data systems that can host that data. We have the compute units, such as CPU and GPU, with the right software to analyse it.
How do virtualisation technologies fit into HPC research? Virtualisation is also interrelated with HPC in the sense that HPC used to be based on a monolithic compute architecture, meaning that you deploy heavy analysis machines and use those machines just as compute nodes. You deploy them and then, without much virtualisation, you
“What I can tell you is the next generation of CPU’s will have much faster, much larger cache memory”
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