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Merge branch 'master' of https://github.com/paboyle/Grid
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Peter Boyle
commit
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5
AUTHORS
5
AUTHORS
@ -0,0 +1,5 @@
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Peter Boyle
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Azusa Yamaguchi
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Intel Parallel Computing Centre @ Higgs Centre for Theoretical Physics
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University of Edinburgh
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Scotland, UK
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86
INSTALL
86
INSTALL
@ -102,9 +102,13 @@ for details on some of the pertinent environment variables.
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You can give `configure' initial values for configuration parameters
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by setting variables in the command line or in the environment. Here
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is an example:
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is are examples:
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./configure CC=c99 CFLAGS=-g LIBS=-lposix
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./configure CXX=clang++ CXXFLAGS="-std=c++11 -O3 -mavx" --enable-simd=AVX1
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./configure CXX=clang++ CXXFLAGS="-std=c++11 -O3 -mavx2" --enable-simd=AVX2
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./configure CXX=icpc CXXFLAGS="-std=c++11 -O3 -mmic" --enable-simd=AVX512 --host=none
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*Note Defining Variables::, for more details.
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@ -124,19 +128,6 @@ architecture at a time in the source code directory. After you have
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installed the package for one architecture, use `make distclean' before
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reconfiguring for another architecture.
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On MacOS X 10.5 and later systems, you can create libraries and
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executables that work on multiple system types--known as "fat" or
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"universal" binaries--by specifying multiple `-arch' options to the
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compiler but only a single `-arch' option to the preprocessor. Like
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this:
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./configure CC="gcc -arch i386 -arch x86_64 -arch ppc -arch ppc64" \
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CXX="g++ -arch i386 -arch x86_64 -arch ppc -arch ppc64" \
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CPP="gcc -E" CXXCPP="g++ -E"
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This is not guaranteed to produce working output in all cases, you
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may have to build one architecture at a time and combine the results
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using the `lipo' tool if you have problems.
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Installation Names
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==================
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@ -218,70 +209,9 @@ overridden with `make V=0'.
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Particular systems
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==================
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On HP-UX, the default C compiler is not ANSI C compatible. If GNU
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CC is not installed, it is recommended to use the following options in
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order to use an ANSI C compiler:
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Cross compiling for native execution on XeonPhi requires
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./configure CC="cc -Ae -D_XOPEN_SOURCE=500"
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and if that doesn't work, install pre-built binaries of GCC for HP-UX.
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HP-UX `make' updates targets which have the same time stamps as
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their prerequisites, which makes it generally unusable when shipped
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generated files such as `configure' are involved. Use GNU `make'
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instead.
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On OSF/1 a.k.a. Tru64, some versions of the default C compiler cannot
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parse its `<wchar.h>' header file. The option `-nodtk' can be used as
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a workaround. If GNU CC is not installed, it is therefore recommended
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to try
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./configure CC="cc"
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and if that doesn't work, try
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./configure CC="cc -nodtk"
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On Solaris, don't put `/usr/ucb' early in your `PATH'. This
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directory contains several dysfunctional programs; working variants of
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these programs are available in `/usr/bin'. So, if you need `/usr/ucb'
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in your `PATH', put it _after_ `/usr/bin'.
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On Haiku, software installed for all users goes in `/boot/common',
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not `/usr/local'. It is recommended to use the following options:
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./configure --prefix=/boot/common
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Specifying the System Type
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==========================
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There may be some features `configure' cannot figure out
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automatically, but needs to determine by the type of machine the package
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will run on. Usually, assuming the package is built to be run on the
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_same_ architectures, `configure' can figure that out, but if it prints
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a message saying it cannot guess the machine type, give it the
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`--build=TYPE' option. TYPE can either be a short name for the system
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type, such as `sun4', or a canonical name which has the form:
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CPU-COMPANY-SYSTEM
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where SYSTEM can have one of these forms:
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OS
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KERNEL-OS
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See the file `config.sub' for the possible values of each field. If
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`config.sub' isn't included in this package, then this package doesn't
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need to know the machine type.
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If you are _building_ compiler tools for cross-compiling, you should
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use the option `--target=TYPE' to select the type of system they will
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produce code for.
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If you want to _use_ a cross compiler, that generates code for a
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platform different from the build platform, you should specify the
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"host" platform (i.e., that on which the generated programs will
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eventually be run) with `--host=TYPE'.
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--build=none
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Sharing Defaults
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================
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42
README
42
README
@ -0,0 +1,42 @@
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This library provides data parallel C++ container classes with internal memory layout
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that is transformed to map efficiently to SIMD architectures. CSHIFT facilities
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are provided, similar to HPF and cmfortran, and user control is given over the mapping of
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array indices to both MPI tasks and SIMD processing elements.
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* Identically shaped arrays then be processed with perfect data parallelisation.
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* Such identically shapped arrays are called conformable arrays.
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The transformation is based on the observation that Cartesian array processing involves
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identical processing to be performed on different regions of the Cartesian array.
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The library will (eventually) both geometrically decompose into MPI tasks and across SIMD lanes.
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Data parallel array operations can then be specified with a SINGLE data parallel paradigm, but
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optimally use MPI, OpenMP and SIMD parallelism under the hood. This is a significant simplification
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for most programmers.
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The layout transformations are parametrised by the SIMD vector length. This adapts according to the architecture.
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Presently SSE2 (128 bit) AVX, AVX2 (256 bit) and IMCI and AVX512 (512 bit) targets are supported.
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These are presented as
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vRealF, vRealD, vComplexF, vComplexD
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internal vector data types. These may be useful in themselves for other programmers.
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The corresponding scalar types are named
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RealF, RealD, ComplexF, ComplexD
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MPI parallelism is UNIMPLEMENTED and for now only OpenMP and SIMD parallelism is present in the library.
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You can give `configure' initial values for configuration parameters
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by setting variables in the command line or in the environment. Here
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is are examples:
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./configure CXX=clang++ CXXFLAGS="-std=c++11 -O3 -mavx" --enable-simd=AVX1
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./configure CXX=clang++ CXXFLAGS="-std=c++11 -O3 -mavx2" --enable-simd=AVX2
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./configure CXX=icpc CXXFLAGS="-std=c++11 -O3 -mmic" --enable-simd=AVX512 --host=none
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43
README.md
43
README.md
@ -1,2 +1,45 @@
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# Grid
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Data parallel C++ mathematical object library
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This library provides data parallel C++ container classes with internal memory layout
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that is transformed to map efficiently to SIMD architectures. CSHIFT facilities
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are provided, similar to HPF and cmfortran, and user control is given over the mapping of
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array indices to both MPI tasks and SIMD processing elements.
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* Identically shaped arrays then be processed with perfect data parallelisation.
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* Such identically shapped arrays are called conformable arrays.
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The transformation is based on the observation that Cartesian array processing involves
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identical processing to be performed on different regions of the Cartesian array.
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The library will (eventually) both geometrically decompose into MPI tasks and across SIMD lanes.
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Data parallel array operations can then be specified with a SINGLE data parallel paradigm, but
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optimally use MPI, OpenMP and SIMD parallelism under the hood. This is a significant simplification
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for most programmers.
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The layout transformations are parametrised by the SIMD vector length. This adapts according to the architecture.
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Presently SSE2 (128 bit) AVX, AVX2 (256 bit) and IMCI and AVX512 (512 bit) targets are supported.
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These are presented as
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vRealF, vRealD, vComplexF, vComplexD
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internal vector data types. These may be useful in themselves for other programmers.
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The corresponding scalar types are named
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RealF, RealD, ComplexF, ComplexD
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MPI parallelism is UNIMPLEMENTED and for now only OpenMP and SIMD parallelism is present in the library.
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You can give `configure' initial values for configuration parameters
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by setting variables in the command line or in the environment. Here
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is are examples:
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./configure CXX=clang++ CXXFLAGS="-std=c++11 -O3 -mavx" --enable-simd=AVX1
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./configure CXX=clang++ CXXFLAGS="-std=c++11 -O3 -mavx2" --enable-simd=AVX2
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./configure CXX=icpc CXXFLAGS="-std=c++11 -O3 -mmic" --enable-simd=AVX512 --host=none
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