forked from lijiext/lammps
correctly use the term CUDA (instead of Cuda) when not referring to the KOKKOS device flag (which is Cuda)
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@ -909,7 +909,7 @@ src/MAKE/OPTIONS, which include the settings. Note that the
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USER-INTEL and KOKKOS packages can use settings that build LAMMPS for
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different hardware. The USER-INTEL package can be compiled for Intel
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CPUs and KNLs; the KOKKOS package builds for CPUs (OpenMP), GPUs
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(Cuda), and Intel KNLs.
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(CUDA), and Intel KNLs.
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Makefile.intel_cpu
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Makefile.intel_phi
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@ -62,7 +62,7 @@ respectively to your input script.
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[Required hardware/software:]
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To use this package, you currently need to have an NVIDIA GPU and
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install the NVIDIA Cuda software on your system:
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install the NVIDIA CUDA software on your system:
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Check if you have an NVIDIA GPU: cat /proc/driver/nvidia/gpus/0/information
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Go to http://www.nvidia.com/object/cuda_get.html
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@ -85,7 +85,7 @@ The GPU library is in lammps/lib/gpu. Select a Makefile.machine (in
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lib/gpu) appropriate for your system. You should pay special
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attention to 3 settings in this makefile.
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CUDA_HOME = needs to be where NVIDIA Cuda software is installed on your system
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CUDA_HOME = needs to be where NVIDIA CUDA software is installed on your system
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CUDA_ARCH = needs to be appropriate to your GPUs
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CUDA_PREC = precision (double, mixed, single) you desire :ul
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@ -113,7 +113,7 @@ To build with Kokkos support for CPUs, your compiler must support the
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OpenMP interface. You should have one or more multi-core CPUs so that
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multiple threads can be launched by each MPI task running on a CPU.
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To build with Kokkos support for NVIDIA GPUs, NVIDIA Cuda software
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To build with Kokkos support for NVIDIA GPUs, NVIDIA CUDA software
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version 7.5 or later must be installed on your system. See the
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discussion for the "GPU"_accelerate_gpu.html package for details of
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how to check and do this.
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@ -50,7 +50,7 @@ Intel(R) Xeon Phi(TM) coprocessors. :l
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KOKKOS = a collection of atom, pair, and fix styles optimized to run
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using the Kokkos library on various kinds of hardware, including GPUs
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via Cuda and many-core chips via OpenMP or threading. :l
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via CUDA and many-core chips via OpenMP or threading. :l
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USER-OMP = a collection of pair, bond, angle, dihedral, improper,
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kspace, compute, and fix styles with support for OpenMP
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