GPU accelerated molecular dynamicsYASARA's molecular dynamics algorithms[1] can now
be accelerated using GPUs from AMD, nVIDIA and Intel, in Linux,
Windows and MacOS.
The actual performance is shown on the
benchmark page.
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 (or the GPU will run idle) - but having a fast CPU for
everyday work is not really a
disadvantage at all. 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 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. A PC should have minimally 8 GB RAM to run YASARA smoothly.
YASARA uses the industry standard 'open compute language' (OpenCL) to communicate with the GPU. OpenCL is supported by all major GPU vendors. 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. Likewise, a slow GPU as found in notebooks will likely slow down the simulation. 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. To estimate performance, look at the FP32 TFLOPs and be careful since the success of AI has led to the development of very expensive GPUs for AI that have neglegible FP32 and thus MD performance (for example the Geforce RTX 4090 has 82 FP32 TFLOPs, while the NVIDIA A100 delivers only 19.5 TFLOPs, i.e. 24%). Recommended operating systems:
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