Nvidia

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Compiling Gromacs-2019.3 with Intel MKL and CUDA

Prerequisites

GCC-6.5 Compilers and associates libraries
m4-1.4.18
mpfr-3.1.4
cmake-3.15.1
gmp-6.1.0
mpc-1.0.3

Intel Compilers and Prerequisites

source /usr/local/intel/2018u3/bin/compilervars.sh intel64
source /usr/local/intel/2018u3/impi/2018.3.222/bin64/mpivars.sh intel64
source /usr/local/intel/2018u3/mkl/bin/mklvars.sh intel64
source /usr/local/intel/2018u3/parallel_studio_xe_2018/bin/psxevars.sh intel64
MKLROOT=/usr/local/intel/2018u3/mkl

Create a setup file

touch gromacs_cpu.sh

Put the following into the gromacs_cpu.sh

CC=mpicc CXX=mpicxx cmake .. -DCMAKE_C_COMPILER=mpicc -DCMAKE_CXX_COMPILER=mpicxx -DGMX_MPI=on -DGMX_FFT_LIBRARY=mkl
-DCMAKE_INSTALL_PREFIX=/usr/local/gromacs-2019.3_intel18_mkl -DREGRESSIONTEST_DOWNLOAD=ON
-DCMAKE_C_FLAGS:STRING="-cc=icc -O3 -xHost -ip"
-DCMAKE_CXX_FLAGS:STRING="-cxx=icpc -O3 -xHost -ip" 
-DGMX_GPU=off -DCMAKE_BUILD_TYPE=Release
./gromacs_cpu.sh
make
make install

Testing and Verification

$ source /your/installation/prefix/here/bin/GMXRC
./gmxtest.pl all -np 2
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Addressing The Challenges In Higher ED and Research

Date: Wednesday, June 17, 2020
Time: 11:00am – 12:00am SGT
Duration: 1 hour

Universities are undergoing an unprecedented challenge to provide staff to work from home, remote teaching and learning , and still provide high value learning to students and cutting edge tools and services to faculty and researchers. While remote learning is not a new phenomenon, providing quality service at scale is now a requirement, along with a new set of challenges that span user experience, mobility, effective management of a distributed deployment.

Solutions that enable remote learning and research, such as NVIDIA virtual GPU (vGPU) technology, enable you to meet these new requirements across various workloads with cost-effective solutions for existing on-premise infrastructure assets and in the cloud.

By attending this webinar, you’ll learn:
How NVIDIA vGPU technology solutions enable remote work and learning
How vGPU solutions are helping universities, across both education and research
How to get started with vGPU and vComputeServer to accelerate VDI and computational workloads in your institution

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Webinar – Build the Most Powerful Data Center with GPU Computing Technology and High-speed Interconnect

Build the Most Powerful Data Center with GPU Computing Technology and High-speed Interconnect

Date: Thursday, June 11, 2020
Time: 11:00am-12:30pm Singapore Time

Register here 

Please join NVIDIA as we discuss how to design a well-balanced system that maximizes performance and scalability of various workloads using NVIDIA GPUs and interconnect

Speakers will provide an overview of the state-of-the-art NVIDIA GPU accelerated compute architecture and In-Network computing fabric and how they come together with one goal: to deliver a solution that democratizes supercomputing power, making it readily accessible, installable, and manageable in a modern business setting. To learn more about this webinar click here

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Getting on board Nvidia GPGPU on CentOS KVM

  1. For vGPU test you’ll need a license, which can be requested here:
    https://www.nvidia.com/object/nvidia-enterprise-account.html
  2. Other documentation for installing vGPU on  Red Hat / CentOS is here:
    https://docs.nvidia.com/grid/latest/grid-vgpu-user-guide/index.html#red-hat-el-kvm-install-configure-vgpu
  3. Virtual GPU Software Quick Start Guide
    https://linuxcluster.wordpress.com/2019/01/28/virtual-gpu-software-quick-start-guide/

In summary the steps are:
– Install a piece of sw in the host/hypervisor to help virtualize GPUs
– Install the GPU drivers inside the guest OS of the VMs
– Install a license server (flex) for the licensing
– Configure license server and settings within the VM to connect to the license server