technology tools, because that expensive car "better be the car that I ordered". ‘With Intel OSPRay, Bentley has created a full 3D manufacturing pipeline, so the models consumers see are the actual manufacturing models rendered in 3D, so that you can select colours and other options. Then, that model goes to the manufacturing floor all in one workflow.’

Future vision Going forward, visualisation tools must adapt to the growing scale and complexity of data, providing an easy-to-use and unified solution. Harduwar said: ‘As data increases,

so does complexity. So does the importance of interpretation and doing the right thing with the data. MapleSim’s simulation platform can solve various problems. Large amounts of data can be brought in and processed and simulated to produce various results and the optimised simulation can produce one solid answer, or a suite of answers and possible solutions. ‘We also hear customers talking about

predictive maintenance of machines and products out there. Predictive maintenance is a trend we see going forward because MapleSim is backed by the Maple math engine. So, simulations can be performed extremely fast.’ From Chouinard’s perspective, remote

working and advancing digitisation also pose increasingly prevalent problems for visualisation tools. He explains: ‘The challenges associated with the quantity, location, transfer and visualisation of engineering data throughout the product development lifecycle will only increase as organisations continue the adoption of cloud computing models and embrace data-heavy artificial intelligence and IoT technologies.' Chouinard adds: ‘Today’s mobile and distributed workforce will also put the burden on producers of engineering software to meet a high standard of flexibility and compatibility across devices. We expect the trend of the convergence of simulation, artificial intelligence and high- performance computing to continue and even accelerate in coming years.’ The challenge for Intel is to deliver ‘the

promise of in-situ visualisation', according to Jeffers. ‘I/O just won’t keep up. Currently, you take your thousands of nodes, run your simulation – sometimes for weeks or months – save all the data, and then need to allocate time in the future to analyse the data, competing with other important science computations. ‘The issue is, at exascale we have great compute capability and have made great

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across its many partnerships and collaborations, as Jeffers explains: ‘For example, the Aurora Exascale system will allow researchers to do larger, more complex simulations than they have ever done before, and we’re already using the Aurora Early Science program to provide early access to hardware to test new approaches to visualisation.’ For example, via a US Department of

 Rendering of Stellar Radiation, >2TB, 3000+ timesteps (frames). Optimised by Intel Open Volume Kernel Library and Intel OSPRay components of Intel oneAPI Rendering Toolkit. Image courtesy of Lars Bildsten (and others), University of California, Santa Barbara and Joe Insley, Argonne National Laboratory.

strides in I/O, but there is a significant delay between I/O – that’s everything from node to node communications, storing from memory to disk, and reading it back out for visualisation,' says Jeffers. ‘Everyone focuses on the simulation, but it’s just 1’s and 0’s until you visually analyse it. Making it consumable for the scientists for them to do what they want – which is to make a discovery – is going to require a high percentage of codes going to in-situ processing to deliver that goal.’ Intel is making progress in this area

Visualising vascular surgery

Surgeons can now plan, execute and understand surgeries using intelligent augmentation and machine learning technologies. Developed by Cydar Medical, the company’s Cydar Intelligent Maps harness cloud GPU computing, computer vision and machine learning technologies to advance surgical visualisation and improve decision- making in theatre, and across the surgical pathway. Tom Carrell, co-founder at Cydar Medical and vascular surgeon, says: ‘We are all

familiar with consumer intelligent map apps such as Google Maps, Apple Maps, and Waze. The concept of intelligent maps for surgery is similar: every new patient’s care would be informed by the care of all previous similar patients and, in turn, each new patient’s care would inform the care of all future patients.' The key is integrating augmented, intelligence-enabled planning, guidance and outcome analysis of surgery for each patient, connecting that dataset to an anonymised global pool of similar datasets and using augmented intelligence to match cases. In 2021, Cydar EV Maps introduced integrated planning, guidance and outcome analysis that uses augmented intelligence (including 3D and 2D deep learning and computer vision technologies) to connect the data across a patient’s journey together into a dynamic, patient-specific model of endovascular (EV) surgery – an EV Map. Carrell explains: ‘Before surgery, augmented intelligence helps clinical users build a

3D pre-operative map of the surgical plan. Then, during surgery, it automatically fuses the map with the live imaging, continuously checking it and non-rigidly adjusting it to account for real-time postural changes and deformation. ‘After surgery, it helps analyse the post-operative outcome in the context of planned,

pre-operative and actual adjusted maps,’ he adds. ‘The Cydar EV Maps cloud platform connects each patient to an anonymised data pool across the EU, US and UK and is learning from every case. The next step is towards intelligent maps for surgery.’

Energy project called SENSEI, the Argonne scientists are collaborating with other labs and within industry to create a unified approach to in-situ data analysis and visualisation. Jeffers explains: ‘With access to early hardware and the Aurora software development kit, the team has been testing and developing these capabilities in advance of the exascale machine’s arrival. With its in-situ, real-time interactive visualisation capability, researchers will be able to examine the data during the on-going simulation and extract insights as the simulation progresses. Thus, the system will speed the time to analyse results in what I like to call “real-time discovery”,' he concludes. As we move to the exascale, visualisation is on the brink of great things, where real- time discovery could pave the way for a new era of scientific and technological breakthroughs – unlocking the promise of visualisation for all, regardless of your location, industry and end user.

Summer 2021 Scientific Computing World 37

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