Graphics Cards

Select the Appropriate GPU for Your CAD Workflow — and Your Budget, Part 2

24 Jul, 2014 By: Alex Herrera

Understand how power consumption, support for multiple monitors, and other factors can impact your choice.


Editor’s note: Read Part 1 of this article here.


When you begin shopping for a workstation-caliber graphics processing unit (GPU), having a price range in mind will narrow the options quite a bit. But it still invariably leads to perhaps the most intimidating issue in this whole shopping process: How to assess and compare the performance of the products in that price range.

Spec sheets may seem like the answer, but most overwhelm the reader with floods of numbers, and use metrics that may not provide a good basis for comparison. If the performance metrics you’ll find in marketing brochures are of interest to you, by all means check them out — but remember to take each with a few grains of salt. While they can hint at a rough performance level, they are generally too subjective in definition and too specific in usage to indicate whether the GPU can succeed in making your job more productive.

Ultimately, getting at least a rough handle on how a GPU might perform while running your type of workload requires some type of benchmarking. Although they are often misused and misinterpreted, benchmarks can be useful tools, and should factor into your GPU selection process. (Learn more about GPU benchmarks in “How Fast Is It? Assess Your Graphics Hardware.”) But that’s not all the information you’ll need.

Capabilities and Features That Benchmarks Don't Measure

Benchmarks are inherently limited to yielding approximations of performance for the things they measure — and there are many professional GPU capabilities that they don't measure at all. For example, the extent of a card's support for advanced display functionality, GPGPU (general-purpose computing on graphics processing units), and memory size and speed can make all the difference in productivity, yet won't show up in published results for popular professional benchmarks such as SPECviewperf.

Display real estate. The secret’s long out that increasing screen “real estate” — the size, resolution, and number of displays — is the single best way to improve your productivity. Most users who have switched to two or more displays will tell you they could never go back to one. Today, the typical professional GPU can manage two or more high-resolution displays on its own. However, lower-end cards are more likely to limit the resolution per screen, an extra consideration should multiple high-resolution displays be a concern for you.

High-end and specialty video support. The capabilities of today's entry-class GPU cards are impressive; still, there are limits at the lower end when it comes to video and display support. These limitations will push higher-demand users to consider more capable cards.

Consider 10-bit color, for example. Design and engineering often go hand-in-hand with product styling, making 10-bit support nice to have, if not mandatory, for the desired visual impact and accuracy for client pitches, previews, or commercial advertisement.

GPGPU. We've discussed nothing but rendering, video, and display so far. But remember, there's another use of GPUs that today's professionals should be considering in their shopping criteria: GPGPU. In its evolution from technological curiosity to mature, workstation-caliber tool, GPU computing has quickened its development pace in recent years. Although GPGPU usage for mainstream consumer or corporate applications is still evolving and looking for compelling footholds, the same isn't true for professional computing spaces.

Common tasks such as raytracing, financial instrument modeling, and engineering simulation — for example, finite element analysis (FEA) and computational fluid dynamics (CFD) — exhibit the type of computing characteristics that make them excellent candidates for GPGPU acceleration: lots of repetitive, highly parallelizable floating-point arithmetic. And that opportunity has not been lost on top CAD rendering and engineering simulation software vendors, with top applications including ANSYS (FEA and CFD), Autodesk Moldflow (CFD), Bunkspeed, and Dassault Systèmes CATIA (both photorealistic raytraced rendering) tapping GPUs to reduce wait times considerably.

If GPGPU acceleration is on your radar, make sure your card supports the application(s) in your workflow that could benefit the most. NVIDIA's Quadro and Tesla cards support the CUDA platform as well as OpenCL, while AMD's FirePro line supports OpenCL.

 


GPU memory size and bandwidth. Depending on the benchmark and where your particular system's bottlenecks are, the size and bandwidth of your GPU's local memory may not be reflected in benchmark results. Still, big and fast memories are advantageous. And for professionals engaging in large-dataset applications — such as oil and gas exploration, raytracing, and scientific research — big memory footprints operating at high bandwidth can make or break performance throughput, regardless of whether a particular benchmark's results indicate as much.

Power consumption. Getting maximum performance out of minimum watts is desirable on many levels. More GPU watts will mean higher electric bills — to drive not only the GPU itself, but also the fans that cool the GPU, and the office space's air conditioning, which must compensate for all that extra heat being produced under the desk.

Then there's the issue of noise. More power means more heat produced; for actively cooled solutions, that also means more (and/or faster) fans to move the air and dissipate the heat. For a growing number of users, heat and noise considerations will help decide between two comparably priced and comparably performing GPU cards.

A Complete Enterprise-Caliber Ecosystem

Many IT managers don't even want to consider the merits of the hardware until they're convinced that an adequate, long-term infrastructure is in place to support the GPUs. After all, the benefits of increased performance and productivity won't mean anything if the solution is difficult to deploy, manage, and operate reliably for what could be an extended-duration installation.

In that context, consider a professional GPU product's lifespan, driver stability, and high-priority 24/7 support as base-level requirements. Because if that foundation isn't in place, the rest is pretty much moot. How reliable and stable are the vendor's and the product's drivers? Do you have faith in them on day one? What about day 365?

Capable and comprehensive IT management tools. Deploying, configuring, and maintaining a single workstation isn't particularly burdensome. But now consider ten, a hundred, or even a thousand workstation seats, some of which need to be configured similarly and some differently, but all updated synchronously.

It's no longer enough to have an IT professional manage one machine at a time locally. Does your support allow for monitoring GPUs and making driver updates remotely? On one, a subset, or all machines at once? One mistake or omission in procedure can mean a failure that leaves a user — or even an entire staff — idle for the day, so the need for a robust and effective means to manage a sea of systems is very real.

End-user tools to maximize productivity. Your new GPU will most likely be capable of running more screens of higher resolution than ever before. That promises to be a major boon to your productivity — provided, of course, your GPU is supported by a robust set of display and workspace management tools. You’ll need tools to manage multiple displays in various organizations, spanning toolbars, screen-space division, and virtual desktops. The hardware is only half the equation; the means to use it effectively is what ultimately delivers on that promise of productivity.

Having the Right Tools Matters

Professional users aren't necessarily experts on computing technology, and they shouldn't have to be. They're architects, builders, engineers, and product designers; that's what they compete on, and how they make their money. The ideal GPU for today's computing professional? One that's so reliable and optimally tuned for your application, that you forget it's even there. For that reason alone, the vast majority will want to select a professional-level GPU. Any modest premium on purchase price should be outweighed quickly by the long-term benefits in cost of ownership, not to mention peace of mind.

The right GPU will make a difference to your productivity, and ultimately, your bottom line. But there’s no need to stress over the decision. With these basic guidelines, you'll quickly hone in on the GPU that performs best in the context that matters most: yours.


About the Author: Alex Herrera

Alex Herrera

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