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Engineering workstations must keep up with new hardware and software technologies that are becoming essential to computer-aided design (CAD) and computer-aided engineering (CAE) professionals. Many of these emerging technologies would not be possible without the compute power delivered by graphics processing units (GPUs).

These GPU-boosted technologies include high resolution 4K displays, augmented and virtual reality (AR/VR), photorealistic rendering direct from CAD files, artificial intelligence (AI) features that deliver previously unthinkable assistance to engineers, interactive CAE used upfront in the design cycle, and generative design (evolutionary algorithms).

Choosing the right GPU can futureproof your engineering workstation over the expected lifecycle of ownership, allowing you to increase productivity, boost product innovation, and embrace emerging technologies with minimal disruption.

Why GPUs Matter to Engineers

GPUs are driving in-silico simulation and design validation, which is increasingly displacing the need for time consuming and costly physical prototypes. This can result in reduced costs and shortened design cycles while delivering superior products.

Modern CAD and CAE apps require GPUs.

Powerful GPUs are required for product designers using AR, VR, or mixed reality (MR) to conduct design reviews or evaluate ergonomics.

GPUs will continue to play a big role in supporting remote work and work-from-home, which will continue even after the pandemic abates. Reinventing 3D design workflows to be flexible, extensible, and productivity-boosting will require new GPU-powered software technologies. The need for ongoing remote work with geographically dispersed teams requires increasing GPU utilization for video conferencing tools like Zoom or Microsoft Teams all the way to industry leading tools for CAD, CAE, and 3D design visualization.

GPUs can solve time-to-market challenges by enabling powerful technologies like rendering, interactive CAE, and generative design. These GPU-based technologies assist engineering professionals in exploring new manufacturing techniques to produce innovative designs with exciting new features.

Capabilities of Modern GPUs

Modern GPUs need to be part of a scalable accelerated computing infrastructure capable of handling every workload—whether working on data center AI, edge AI, big data analytics, data science simulations, or professional visualization of all kinds.

The best contemporary GPUs deliver real-time ray tracing, which aids in professional visualization, animation, and casino or consumer gaming. GPUs with specialized hardware for machine learning computations can use AI to rapidly denoise renders, provide superior anti-aliasing, or deliver novel features that can’t be achieved without AI. These features empower engineering professionals by transforming their GPUs from a system component to an active assistant.

For instance, six workstation graphics cards within the NVIDIA RTX line (the A6000, A5500, A5000, A4500, A4000 and A2000 12GB) can deliver real-time photorealistic ray tracing with their specialized RT Cores. These cards also feature Tensor Cores for AI acceleration and lots of advanced GDDR6 ECC memory to ensure the GPU has better access to the data it needs.

Basic Building Blocks that Every Engineering Workstation Needs

The following features are basic building blocks that every engineering workstation needs:

Access to massively parallel processing power.
High-performance networking for remote work, design reviews, and large models or designs.
High-capacity, high-performance storage, with a RAID implementation for backing-up critical design data.

The Essential Business Metrics of Engineering Workstations

It is important to consider the following business metrics when selecting an engineering workstation:

Cost of ownership: Does it provide a strong return on investment (ROI) and predictable future costs?
Flexibility: Does it support fa variety of compute, graphics, and AI-intensive workloads?
Ecosystem integration: does it include standards-based equipment and leading software that fully exploits the capabilities of GPUs?
Scalability: Is it easy to upgrade and easily scalable (e.g. with GPU memory pooling) with the ability to integrate into complex workflows?
Productivity: Does it offer fluid, interactive performance that enables creativity and allows additional or new design possibilities to be fully explored?
Creativity: Does it have the performance to “get out of the way” of the creative process and allow users to design or simulate different approaches in-silico before physical prototypes are produced?
ROI of scaling: Can it support GPU workload bandwidth requirements but also scale out in a tiered storage system?

Many engineering tools now require additional processing performance and capabilities that only GPUs can provide. The right GPU futureproofs engineering workstations by letting them accommodate growing and ongoing engineering trends.

For more information on choosing the right GPU for engineering workstations, visit PNY.com.