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@ -22,6 +22,37 @@ Last update June 2017.
_Please do not send pull requests to the `master` branch which is reserved for releases._
### Description
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 shaped 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, ARM NEON (128 bits) AVX, AVX2, QPX (256 bits), IMCI and AVX512 (512 bits) targets are supported.
These are presented as `vRealF`, `vRealD`, `vComplexF`, and `vComplexD` internal vector data types.
The corresponding scalar types are named `RealF`, `RealD`, `ComplexF` and `ComplexD`.
MPI, OpenMP, and SIMD parallelism are present in the library.
Please see [this paper](https://arxiv.org/abs/1512.03487) for more detail.
### Compilers
Intel ICPC v16.0.3 and later
@ -56,38 +87,19 @@ When you file an issue, please go though the following checklist:
6. Attach the output of `make V=1`.
7. Describe the issue and any previous attempt to solve it. If relevant, show how to reproduce the issue using a minimal working example.
### Description
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 shaped 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, ARM NEON (128 bits) AVX, AVX2, QPX (256 bits), IMCI and AVX512 (512 bits) targets are supported.
These are presented as `vRealF`, `vRealD`, `vComplexF`, and `vComplexD` internal vector data types.
The corresponding scalar types are named `RealF`, `RealD`, `ComplexF` and `ComplexD`.
MPI, OpenMP, and SIMD parallelism are present in the library.
Please see [this paper](https://arxiv.org/abs/1512.03487) for more detail.
### Required libraries
Grid requires [GMP](https://gmplib.org/), [MPFR](http://www.mpfr.org/) and optionally [HDF5](https://support.hdfgroup.org/HDF5/) and [LIME](http://usqcd-software.github.io/c-lime/) (for ILDG file format support) to be installed.
Grid requires:
[GMP](https://gmplib.org/),
[MPFR](http://www.mpfr.org/)
Bootstrapping grid downloads and uses for internal dense matrix (non-QCD operations) the Eigen library.
Grid optionally uses:
[HDF5](https://support.hdfgroup.org/HDF5/)
[LIME](http://usqcd-software.github.io/c-lime/) (for ILDG file format support)
[FFTW](http://www.fftw.org) (Either generic or via the Intel MKL library)
[LAPACK]( either generic or Intel MKL library)
### Quick start
First, start by cloning the repository: