Homec4science
Diffusion lammps (lammm-master-deprecated) Browse

lammps/lib/kokkos/README
ed9f13663baclammm-master-deprecated

Last Change
Raw File

README
View Options

Kokkos implements a programming model in C++ for writing performance portable
applications targeting all major HPC platforms. For that purpose it provides
abstractions for both parallel execution of code and data management.
Kokkos is designed to target complex node architectures with N-level memory
hierarchies and multiple types of execution resources. It currently can use
OpenMP, Pthreads and CUDA as backend programming models.
The core developers of Kokkos are Carter Edwards and Christian Trott
at the Computer Science Research Institute of the Sandia National
Laboratories.
The KokkosP interface and associated tools are developed by the Application
Performance Team and Kokkos core developers at Sandia National Laboratories.
To learn more about Kokkos consider watching one of our presentations:
GTC 2015:
http://on-demand.gputechconf.com/gtc/2015/video/S5166.html
http://on-demand.gputechconf.com/gtc/2015/presentation/S5166-H-Carter-Edwards.pdf
A programming guide can be found under doc/Kokkos_PG.pdf. This is an initial version
and feedback is greatly appreciated.
A separate repository with extensive tutorial material can be found under
https://github.com/kokkos/kokkos-tutorials.
If you have a patch to contribute please feel free to issue a pull request against
the develop branch. For major contributions it is better to contact us first
for guidance.
For questions please send an email to
kokkos-users@software.sandia.gov
For non-public questions send an email to
hcedwar(at)sandia.gov and crtrott(at)sandia.gov
============================================================================
====Requirements============================================================
============================================================================
Primary tested compilers on X86 are:
GCC 4.7.2
GCC 4.8.4
GCC 4.9.2
GCC 5.1.0
Intel 14.0.4
Intel 15.0.2
Intel 16.0.1
Clang 3.5.2
Clang 3.6.1
Primary tested compilers on Power 8 are:
IBM XL 13.1.3 (OpenMP,Serial)
GCC 4.9.2 (OpenMP,Serial)
GCC 5.3.0 (OpenMP,Serial)
Secondary tested compilers are:
CUDA 6.5 (with gcc 4.7.2)
CUDA 7.0 (with gcc 4.7.2)
CUDA 7.5 (with gcc 4.8.4)
Other compilers working:
X86:
Intel 17.0.042 (the FENL example causes internal compiler error)
PGI 15.4
Cygwin 2.1.0 64bit with gcc 4.9.3
KNL:
Intel 16.2.181 (the FENL example causes internal compiler error)
Intel 17.0.042 (the FENL example causes internal compiler error)
Known non-working combinations:
Power8:
GCC 6.1.0
Pthreads backend
Primary tested compiler are passing in release mode
with warnings as errors. They also are tested with a comprehensive set of
backend combinations (i.e. OpenMP, Pthreads, Serial, OpenMP+Serial, ...).
We are using the following set of flags:
GCC: -Wall -Wshadow -pedantic -Werror -Wsign-compare -Wtype-limits
-Wignored-qualifiers -Wempty-body -Wclobbered -Wuninitialized
Intel: -Wall -Wshadow -pedantic -Werror -Wsign-compare -Wtype-limits -Wuninitialized
Clang: -Wall -Wshadow -pedantic -Werror -Wsign-compare -Wtype-limits -Wuninitialized
Secondary compilers are passing without -Werror.
Other compilers are tested occasionally, in particular when pushing from develop to
master branch, without -Werror and only for a select set of backends.
============================================================================
====Getting started=========================================================
============================================================================
In the 'example/tutorial' directory you will find step by step tutorial
examples which explain many of the features of Kokkos. They work with
simple Makefiles. To build with g++ and OpenMP simply type 'make openmp'
in the 'example/tutorial' directory. This will build all examples in the
subfolders.
============================================================================
====Running Unit Tests======================================================
============================================================================
To run the unit tests create a build directory and run the following commands
KOKKOS_PATH/generate_makefile.bash
make build-test
make test
Run KOKKOS_PATH/generate_makefile.bash --help for more detailed options such as
changing the device type for which to build.
============================================================================
====Install the library=====================================================
============================================================================
To install Kokkos as a library create a build directory and run the following
KOKKOS_PATH/generate_makefile.bash --prefix=INSTALL_PATH
make lib
make install
KOKKOS_PATH/generate_makefile.bash --help for more detailed options such as
changing the device type for which to build.
============================================================================
====CMakeFiles==============================================================
============================================================================
The CMake files contained in this repository require Tribits and are used
for integration with Trilinos. They do not currently support a standalone
CMake build.
===========================================================================
====Kokkos and CUDA UVM====================================================
===========================================================================
Kokkos does support UVM as a specific memory space called CudaUVMSpace.
Allocations made with that space are accessible from host and device.
You can tell Kokkos to use that as the default space for Cuda allocations.
In either case UVM comes with a number of restrictions:
(i) You can't access allocations on the host while a kernel is potentially
running. This will lead to segfaults. To avoid that you either need to
call Kokkos::Cuda::fence() (or just Kokkos::fence()), after kernels, or
you can set the environment variable CUDA_LAUNCH_BLOCKING=1.
Furthermore in multi socket multi GPU machines, UVM defaults to using
zero copy allocations for technical reasons related to using multiple
GPUs from the same process. If an executable doesn't do that (e.g. each
MPI rank of an application uses a single GPU [can be the same GPU for
multiple MPI ranks]) you can set CUDA_MANAGED_FORCE_DEVICE_ALLOC=1.
This will enforce proper UVM allocations, but can lead to errors if
more than a single GPU is used by a single process.
c4science · Help