CAD Tech News (#96)

17 Oct, 2018 By: Cadalyst Staff

Herrera on Hardware: With New Turing, NVIDIA Doubles Down on the Future of Real-Time Ray-Tracing

And CAD professionals are expected to reap the benefits.

By Alex Herrera

NVIDIA's long-expected successor to its Pascal GPU architecture for gaming and professional graphics is here. In August, the GPU developer pulled the covers off Turing, which one could argue is both a successor to not one but both of its preceding generations of graphics processing units — 2016's Pascal and 2017's Volta. In the process, the company confirmed several of the more expected 3D graphics advancements for its next flagship GPU. But it also revealed a few surprises, representing an aggressive-but-justified departure from past generations' decisions about how it formulates products that today are destined for a far wider spectrum of applications than those of years past.

NVIDIA's new Turing GPU archticture will debut this quarter in its Quadro RTX line, including the highest-end Quadro RTX 8000 shown here. (Source: NVIDIA)
NVIDIA's new Turing GPU archticture will debut this quarter in its Quadro RTX line, including the highest-end Quadro RTX 8000 shown here. (Source: NVIDIA)

Perhaps even more significant is the inflection point in GPU evolution that Turing marks, a unification — if not permanent and all-encompassing, at the very least meaningful — of the previous disparate and often conflicting priorities between the GPU's traditional 3D graphics markets and the hot emerging opportunities attracting the company's attention. With Turing, NVIDIA confirms two realizations very much reflected in the GPU's DNA: one, that machine learning is now a valid and justified tool to enhance 3D visual computing; and two, that the time is ripe to begin the long-awaited transition from 3D raster graphics to the ultimate in rendering, real-time ray-tracing.

Turing, RTX, and NGX

NVIDIA is finding more ways to leverage machine learning to improve performance and quality for traditional 3D graphics. It's been a while since NVIDIA shaped new GPU architectures and technology strictly for the benefit of traditional raster-based 3D graphics that CAD applications and users have primarily relied upon. Over the past decade, NVIDIA GPUs have pushed well beyond that core space and into high-performance computation ("compute"), autonomous vehicles, robotics, supercomputing, and now, front-and-center, machine learning. And each new generation has walked a careful balance, supporting new applications without handicapping the GPU for its bread-and-butter 3D graphics markets.

With Turing, NVIDIA made many of the more conventional improvements to its fundamental 3D graphics programmable shader engine, the Streaming Multiprocessor (SM), especially in terms of critical resources like chip registers and cache, and dialed up supporting infrastructure including external memory bandwidth — all good things that contribute to faster, higher-quality interactive 3D graphics crucial to improving the CAD experience and productivity. But those tweaks represent the more expected steps along the tried-and-true GPU evolution path, taking on cost and complexity for features and performance the company is pretty darn sure ISVs and end users alike will value in the near term, if not immediately. More noteworthy than the more conventional 3D graphics features Turing added is what it didn't subtract from the company's previous compute/artificial intelligence (AI)–focused GPU, Volta. With Turing, NVIDIA architects not only didn't strip out Volta's Tensor Cores, they improved on them — and doubled down on the pursuit of real-time rendering by adding more raytrace-specific acceleration. Read more»

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Cadalyst contributing editor Alex Herrera is a consultant focusing on high-performance graphics and workstations.


Bentley Systems Believes Digital Twins Are 'the Future,' and Invests Accordingly

The company is committed to developing digital twin technologies for infrastructure projects and assets, including an iTwin Services cloud platform and a plant operations solution created in partnership with Siemens.

By Cyrena Respini-Irwin

At its annual Year in Infrastructure conference, under way this week in London, Bentley Systems announced iTwin Services, a digital twin cloud offering for infrastructure projects and assets. Scheduled for release in early 2019, iTwin Services is a testament to Bentley's belief in the promise of the digital twin: "I really do believe it's the future," said CTO Keith Bentley.

A digital twin is a virtual replica of something in the real world, such as a machine, structure, or process. Rather than existing in isolation, the digital and physical counterparts are paired with each other; the former integrates data about the latter, such as its frequency of use or operating conditions, giving its owners insight into how it's functioning in the world.

Adam Klatzkin, senior director of iModel Technologies for Bentley Systems, believes that the time has come for digital twins to be regularly leveraged in the infrastructure arena. The scope and complexity of infrastructure projects and assets, however, pose challenges beyond those inherent in other kinds of digital twins. Collecting up-to-date data about the status of a freeway or building site is a far more arduous and expensive task than keeping tabs on the condition and location of a backhoe, for example. And as-operated engineering information is often inaccessible, due to conflicts in data structure or format. "You can't base a digital twin on dark data," said Klatzkin. Read more»

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Cyrena Respini-Irwin is Cadalyst's editor in chief.


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