From 4372f13dd72303b63e71850493246227dac177fd Mon Sep 17 00:00:00 2001 From: Mark Abraham Date: Tue, 21 Apr 2015 00:13:17 +0200 Subject: [PATCH] Document how to add and use NVML support Change-Id: I8ca7c5d1b163a78559a048ca6cc5b099f34c6cd6 --- docs/install-guide/index.rst | 17 ++++++++++++++++- 1 file changed, 16 insertions(+), 1 deletion(-) diff --git a/docs/install-guide/index.rst b/docs/install-guide/index.rst index 24ef80f4e3..3a842e97db 100644 --- a/docs/install-guide/index.rst +++ b/docs/install-guide/index.rst @@ -578,7 +578,22 @@ If you have the CUDA_ Toolkit installed, you can use ``cmake`` with: (or whichever path has your installation). In some cases, you might need to specify manually which of your C++ compilers should be used, -e.g. with the advanced option ``CUDA_HOST_COMPILER``. +e.g. with the advanced option ``CUDA_HOST_COMPILER``. To make it +possible to get best performance from NVIDIA Tesla and Quadro GPUs, +you should install the `GPU Deployment Kit +`_ and configure +|Gromacs| to use it by setting the CMake variable +``-DGPU_DEPLOYMENT_KIT_ROOT_DIR=/path/to/your/kit``. The NVML support +is most useful if +``nvidia-smi --applications-clocks-permission=UNRESTRICTED`` is run +(as root). When application clocks permissions are unrestricted, the +GPU clock speed can be increased automatically, which increases the +GPU kernel performance roughly proportional to the clock +increase. When using |Gromacs| on suitable GPUs under restricted +permissions, clocks cannot be changed, and in that case informative +log file messages will be produced. Background details can be found at +this `NVIDIA blog post +`_. By default, optimized code will be generated for CUDA architectures supported by the nvcc compiler (and the |Gromacs| build system). -- 2.22.0