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 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

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:

PGI 15.4
IBM XL 13.1.2
Cygwin 2.1.0 64bit with gcc 4.9.3

Primary tested compiler are passing in release mode with warnings as errors. 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.

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.