GPU accelerated molecular dynamics

YASARA can accelerate molecular dynamics simulations using GPUs from AMD, nVIDIA and Intel, in Linux and Windows (MacOS currently crashes, the problem has been reported to Apple). The actual performance is shown on the benchmark page, including article references.

YASARA uses the GPU to calculate the non-bonded interactions (Van der Waals and real-space Coulomb forces), all the rest (PME, bonded intraactions, NMR restraints…) is done by the CPU. This approach has a few advantages:

• The CPU's power is not wasted by letting it run idle, especially since some tasks are better handled by the CPU (the GPU architecture is quite different).
• Complicated algorithms developed over the past decades don't have to be rewritten for the GPU and are immediately available (knowledge- based force fields for protein refinement, NMR restraints…)
• Macros that interact with the simulation (steered MD etc.) work unchanged.
  

The only real disadvantage is that you cannot upgrade an old computer by inserting a top graphics card. Instead, the power of the CPU must match the GPU - but having a fast CPU for everyday work is not really a disadvantage at all.

Recommended CPUs:

To keep the GPU busy, a fast CPU is required. Depending on your budget, we recommend most recent CPUs from Intel and especially the new Ryzen 7000 CPUs with Zen 4 architecture released by AMD in 2022. The next CPU we will order is the Ryzen 9 7950X (so we don't have benchmark results yet), which for the first time supports the AVX-512 instruction set without reducing the clock frequency like Intel CPUs do. This is possible thanks to 'double bumping', which looks like a smart compromise to us. We will therefore re-evaluate if it's worthwhile to add AVX-512 support to YASARA (which it wasn't on Intel CPUs).
The only thing to keep in mind is that CPU clock speed tends to be more important than CPU core count. For example Intel's giant Xeons with huge caches and countless cores are less attractive, because they combine a significantly higher price tag with a low clock frequency, that is even throttled further down when using high performance AVX code (like YASARA does). For the same reason, machines with two or more CPU sockets cannot be recommended.
The cores should all have equally fast access to memory, that's why we don't recommend Ryzen Threadripper 2990WX and 2970X, where you have to fiddle with extra tools to tune memory access. Also note that the more threads a CPU can execute, the larger the simulated system must be. The very small DHFR benchmark with 23786 atoms can keep 16 threads busy.

Recommended GPUs:

YASARA uses the industry standard 'open compute language' (OpenCL) to communicate with the GPU. OpenCL is supported by all major GPU vendors, so by using OpenCL, YASARA promotes competition and makes sure that you are not tied to a single company, who can then ask arbitrary prices for their hardware.
To estimate how well a certain card from nVIDIA or AMD is suited for MD simulation with YASARA, please visit the CompuBench 1.5 OpenCL benchmark page and look at "Particle simulation 64k".
Since YASARA uses both CPU and GPU at the same time, their power must match. It doesn't make sense to pair an ultra-high end GPU with a low end CPU, because the GPU would spend most of its time waiting. If you go for a GPU from nVIDIA, pick a Geforce RTX or newer, it has features that are helpful for YASARA's new Vulkan graphics engine.

Recommended operating systems:

• Linux delivers the highest performance, thanks to its frequent use on clusters and the extensive optimization work done to make good use of these expensive resources.
• Windows, being the primary video game platform, has also been well optimized and gets very close to Linux (within a few percent), so the difference is only measurable but not noticeable.
• MacOS has been heavily optimized for power efficiency and long battery life on mobile devices, but not so much for performance. It has difficulties dealing with programs that spawn multiple threads to fully exploit the CPU's potential. As a result, MD simulations of small proteins may run noticeably slower, but the difference becomes smaller with growing protein size. Unfortunately, GPU acceleration in MacOS is somewhat unreliable, depending on your MacOS version and GPU type, it can work fine or crash. Both problems have been reported to Apple and will hopefully be solved soon.