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of the 5D cayley form chiral fermions for the 5d matrix. With Ls entirely in the vector direction, s-hopping terms involve rotations. The serial dependence of the LDU inversion for Mobius and 4d even odd checkerboarding is removed by simply applying Ls^2 operations (vectorised many ways) as a dense matrix operation. This should give similar throughput but high flops (non-compulsory flops) but enable use of the KNL cache friendly kernels throughout the code. Ls is still constrained to be a multiple of Nsimd, which is as much as 8 for AVX512 with single precision.
Grid 
Data parallel C++ mathematical object library
Last update 2015/7/30
This library provides data parallel C++ container classes with internal memory layout that is transformed to map efficiently to SIMD architectures. CSHIFT facilities are provided, similar to HPF and cmfortran, and user control is given over the mapping of array indices to both MPI tasks and SIMD processing elements.
- Identically shaped arrays then be processed with perfect data parallelisation.
- Such identically shapped arrays are called conformable arrays.
The transformation is based on the observation that Cartesian array processing involves identical processing to be performed on different regions of the Cartesian array.
The library will both geometrically decompose into MPI tasks and across SIMD lanes. Local vector loops are parallelised with OpenMP pragmas.
Data parallel array operations can then be specified with a SINGLE data parallel paradigm, but optimally use MPI, OpenMP and SIMD parallelism under the hood. This is a significant simplification for most programmers.
The layout transformations are parametrised by the SIMD vector length. This adapts according to the architecture. Presently SSE4 (128 bit) AVX, AVX2 (256 bit) and IMCI and AVX512 (512 bit) targets are supported (ARM NEON on the way).
These are presented as
vRealF, vRealD, vComplexF, vComplexD
internal vector data types. These may be useful in themselves for other programmers. The corresponding scalar types are named
RealF, RealD, ComplexF, ComplexD
MPI, OpenMP, and SIMD parallelism are present in the library.
You can give `configure' initial values for configuration parameters by setting variables in the command line or in the environment. Here are examples:
./configure CXX=clang++ CXXFLAGS="-std=c++11 -O3 -msse4" --enable-simd=SSE4
./configure CXX=clang++ CXXFLAGS="-std=c++11 -O3 -mavx" --enable-simd=AVX
./configure CXX=clang++ CXXFLAGS="-std=c++11 -O3 -mavx2" --enable-simd=AVX2
./configure CXX=icpc CXXFLAGS="-std=c++11 -O3 -mmic" --enable-simd=AVX512 --host=none
Note: Before running configure it could be necessary to execute the script
script/filelist
For developers: Use reconfigure_script in the scripts/ directory to create the autotools environment