Just like the standard version of VASP, the GPU port is parallelized with MPI and can distribute the computational workload across multiple CPUs, GPUs and nodes. We will use Intel MPI in this guide, but all techniques described herein work with other MPI implementations just as well.

Please refer to the documentation of your concrete MPI implementation to find the equivalent command line options. Eye-fi Not Transferring Raw Files. VASP supports a variety of features and algorithms causing its computational profile to be just as diverse. Therefore, depending on your specific calculations, you might need different parameters to yield the quickest possible execution times.

• Density functional theory • Vienna Ab-initio Simulation Package (VASP) • Setting up a VASP calculation • Tutorial 1: Structure optimization. Overview VASP, the Vienna Ab initio Simulation Package, is one of the materials science packages available on RCC. It is a materials modeling software designed for.

These aspects propagate to the GPU port just as well. In this tutorial, we will provide various techniques that can help speeding up your GPU runs. However, as there is no one optimal setup, you need to benchmark your cases individually to find the settings with the best performance for your cases. First, let’s see how many (and which) GPUs your node offers. Typically, GPUs need to transfer data between their own and the main memory. On multi-socket systems, the transfer performance depends on the path the data needs to move along. In the best case, there is a direct bus between the two separate memory regions.

Archicad 15 X64 Crack. In the worst-case scenario, the CPU process needs to access memory that is physically located in a RAM module associated to the other CPU socket and then copy it to GPU memory that is (yet again) only accessible via a PCI-E lane controlled by the other CPU socket. Information about the bus topology can be displayed with.

Whenever you want to compare execution times of runs in various configurations, it is essential to avoid unforeseen deviations. NVIDIA GPUs feature techniques to allow for temporarily raising and lowering clock-rates based on the current thermal situation and compute load.

While this is good for saving power, for benchmarking it might give misleading numbers caused by a slightly higher variance on execution times between multiple runs. Therefore, to do comparative benchmarking we try to turn it off for all the cards in the system. Rank 0 uses GPU0, but is bound to the more distant CPU cores 16-23. The same problem applies for ranks 2 and 3. Only rank 1 uses GPU1 and is pinned to the cores 24-31, which offer best transfer performance.

Let’s look at some actual performance numbers now. Using all 32 cores of the two Intel ® Xeon ® E5-2698 v3 CPUs present in our system without any GPU acceleration, it took 607.142 s to complete the benchmark. 1 Using 4 GPUs in this default, but suboptimal way, results in an execution time of 273.320 s and a speedup of 2.22x. Use the following metrics included in VASP to quickly find out how long your calculation ran 1 If you have built the CPU-only version of VASP before, you can use the following command to see how long it takes on your system: mpirun -n 32 -env I_MPI_PIN_PROCESSOR_LIST=allcores:map=scatter ~/bin/vasp_std.

This gave us a runtime of 276.299 s and can be especially helpful if some of the CPU cores remain idle. You may want to do so on purpose, if a single process per GPU saturates a GPU resource that is limiting performance. Overloading the GPU even further, would impair performance then. This is given in the siHugeShort benchmark example, so on our system, this is as good as it gets (feel free to try out the coming options here anyway!). However, it’s generally a bad idea to waste available CPU cores as long as you are not overloading the GPUs, so do your own testing!

Date Parameters In Microsoft Query Wizard. After reaching the sweet spot, adding more processes per GPU impairs performance even more. Whenever a GPU needs to switch contexts, i.e., allow another process to take over, it introduces a hard synchronization point.

Consequently, there is no possibility for instructions of different processes to overlap on the GPU and overusing this feature can in fact slow things down again. Please also see the illustration below. In conclusion, it seems to be a good idea to test how much oversubscription is beneficial for your type of calculations. Of course, very large calculations will more easily fill a GPU with a single process than smaller ones, but we can’t encourage you enough to do your own testing! The first command starts the MPS server in the background (daemon mode). When it is running it will intercept instructions issued by processes sharing a GPU and put them into the same context before sending them to the GPU. The difference to the previous section is that from the GPU’s perspective the instructions belong to a single process and context and as such can overlap now, just like if you were using streams within a CUDA application.