64 Cores of Rendering Madness: The AMD Threadripper Pro 3995WX Review

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Knowing your market is a key fundamental of product planning, marketing, and distribution. There’s no point creating a product with no market, or finding you have something amazing but offer it to the wrong sort of customers. When AMD started offering high-core count Threadripper processors, the one market that took as many as they could get was the graphics design business – visual effects companies and those focused on rendering loved the core count, the memory support, all the PCIe lanes, and the price. But if there’s one thing more performance brings, it’s the desire for even more performance. Enter Threadripper Pro.

computational graphics goes brrrrrrr

There are a number of industries that, when looking from the outside, an enthusiast might assume that using a CPU is probably old fashioned – the question is asked as to why hasn’t that industry moved fully to using GPU accelerators? One of the big ones is machine learning – despite the push to dedicated machine learning hardware and lots of big businesses doing ML on GPUs, most machine learning today is still done on CPUs. The same is still true with graphics and visual effects.

The reason behind this typically comes down to the software packages in use, and the programmers in charge.

Developing software for CPUs is easy, because that is what most people are trained on. Optimization packages for CPUs are well established, and even for upcoming specialist instructions, these can be developed in simulated environments. A CPU is designed to handle almost anything thrown at it, even super bad code.

By contrast, GPU compute is harder. It isn’t as difficult as it used to be, as there are wide arrays of libraries that enable GPU compilation without having to know too much about how to program for a GPU, however the difficulty lies in architecting the workload to take advantage of what a GPU has to offer. A GPU is a massive engine that performs the same operation to hundreds of parallel threads at the same time – it also has a very small cache and accesses to GPU memory are long, so that latency is hidden by having even more threads in flight at once. If the compute part of the software isn’t amenable to that sort of workload, such as being structurally more linear, then spending 6 months redeveloping for a GPU is a wasted effort. Or even if the math works out better on GPU, trying to rebuild a 20-year old codebase (or older) for GPUs still requires a substantial undertaking by a group of experts.

GPU compute is coming on leaps and bounds ever since I did it in the late 2000s. But the fact remains is that there are still a number of industries that are a mix of CPU/GPU throughput. These include machine learning, oil and gas, financial, medical, and the one we’re focusing on today is visual effects.

A visual effects design and rendering workload is a complex mix of dedicated software platforms and plugins. Software like Cinema4D, Blender, Maya, and others rely on the GPU to showcase a partially rendered scene for these artists to work on in real time, also relying on strong single core performance, but the bulk of compute for the final render will depend on what plugins are being used for that particular product. Some plugins are GPU accelerated, such as Blender Cycles, and the move to more GPU-accelerated workloads is taking its time – ray tracing accelerated design is an area that is getting a lot of GPU attention, for example.

There are always questions as to which method produces the best image – there’s no point using a GPU to accelerate the rendering time if it adds additional noise or reduces the quality. A film studio is more than likely to prioritize a slow higher-quality render on CPUs than a fast noisy one on GPUs, or alternatively, render a lower resolution image and then upscale with trained AI. Based on our conversations with OEMs that supply the industry, we've been told that a number of studios will outright say that rendering their workflow on a CPU is the only way they do it. The other angle is memory, as the right CPU can have 256 GB to 4 TB of DRAM available, whereas the best GPUs can only supply 80 GB (and those are the super expensive ones).

The point I’m making here is that VFX studios still prefer CPU compute, and the more the better. When AMD launched its new Zen-based processors, particularly the 32 and 64 core count models, these were immediately earmarked as potential replacements for the Xeons being used in these VFX studios.

AMD’s parts prioritized FP compute, a key element in VFX design, and having double the cores per socket was also a winner, combined with the large amount of cache per core. This latter part meant that even though the first high-core count parts had a non-uniform memory architecture, it wasn’t as much of an issue as with some other compute processes.

A number of VFX companies as far as we understand focused on AMD’s Threadripper platform over the corresponding EPYC. When both of these parts first arrived to market, it was very easy for VFX studios to invest in under-the-desk workstations built on Threadripper, while EPYC was more for the server rack installations and not so much for workstations. Roll around to Threadripper 3000, and EPYC 7002, and now there are 64 cores, 64 PCIe 4.0 lanes, and lots of choice. VFX studios still went for Threadripper, mostly due to offering higher power 280 W in something that could easily be sourced by system integrators like Armari that specialize in high-compute under-desk systems.  They also asked AMD for more.

AMD has now rolled out its Threadripper Pro platform, addressing some of these requirements. While VFX is always core compute focused, the TR Pro now gives double the PCIe lanes, double the memory bandwidth, support for up to 2TB of memory, and Pro-level admin support. These PCIe lanes could be extended to local storage (always important in VFX) as well as large RAMDisks, and the admin support through DASH helps keep the company systems managed together appropriately. AMD’s Memory Guard is also in its Pro line of parts, which is designed to enable full memory encryption.

Beyond VFX, AMD has cited world leadership compute with TR Pro for product engineering with Creo, 3D visualization with KeyShot, model design in architecture with Autodesk Revit, and data science, such as oil and gas dataset analysis, where the datasets are growing into the hundreds of GB and require substantial compute support.
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