Skip to content

GitLab

elpa
elpa
Unverified Commit 88a458c5 authored by Andreas Marek's avatar Andreas Marek
Browse files
Options

Remove obsolete README_elpa2_kernels.txt file

parent fe500b4c
Hide whitespace changes
Inline Side-by-side
Showing with 0 additions and 156 deletions
src/elpa2_kernels/README_elpa2_kernels.txt deleted 100644 → 0
View file @ fe500b4c
This file is intended as guideline for choosing one appropiate
ELPA2-kernel for your installation.
ELPA generally uses BLAS-Routines for all compute intensive work
so that the performance of ELPA mainly depends on the quality of
the BLAS implementation used when linking.
The only exception is the backtransformation of the eigenvectors
for the 2-stage solver (ELPA2). In this case BLAS routines cannot
be used effectively due to the nature of the problem.
The compute intensive part of the backtransformation of ELPA2
has been put to a file of its own (elpa2_kernels.f90) so that
this can be replaced by hand tailored, optimized code for
specific platforms.
However, we cannot choose for you the best kernels, you should read
these hints, and maybe try which kernel works best for you.
Currently we offer the following alternatives for the ELPA2 kernels:
* elpa2_kernels_{real|complex}.f90
- The generic FORTRAN version of the ELPA2 kernels
which should be useable on every platform.
It contains some hand optimizations (loop unrolling)
in the hope to get optimal code from most FORTRAN
compilers. The configure option "--with-generic"
uses these kernels. They are propably a good
default if you do not know which kernel
to use. Note that in the real version,
there is used a complex variable in
order to enforce better compiler
optimizations. This produces correct
code, however, some compilers might
produce a warning.
* elpa2_kernels_{real|complex}_simple.f90
- Plain and simple version of elpa2_kernels.f90.
Please note that we observed that some compilers get
get confused by the hand optimizations done in
elpa2_kernels_{real|complex}.f90 and
give better performance with this
version - so it is worth to try both!
The configure option "--with-generic-simple"
uses these kernels.
* elpa2_kernels_real_bgp.f90
- Fortran code enhanced with assembler calls
for the IBM BlueGene/P. For the complex
eigenvalue problem the "elpa2_kernels_complex.f90"
is recommended. The configure option
"--with-generic-bgp" uses these
kernels. Note that the OpenMP functionality of
this kernel is not yet tested and thus an
preprocessor error is thrown in the combination
of this kernel with OpenMP. By manually editing
the file src/elpa2.F90 one can avoid this and
test the OpenMP functionality. The ELPA
developers would welcome every feedback to this
subject.
* elpa2_kernels_real_bgq.f90
- Fortran code enhanced with assembler calls
for the IBM BlueGene/Q. For the complex
eigenvalue problem the "elpa2_kernels_complex.f90"
is recommended. The configure option
"--with-generic-bgq" uses these
kernels. Note that the OpenMP functionality of
this kernel is not yet tested and thus an
preprocessor error is thrown in the combination
of this kernel with OpenMP. By manually editing
the file src/elpa2.F90 one can avoid this and
test the OpenMP functionality. The ELPA
developers would welcome every feedback
to this subject.
* elpa2_kernels_asm_x86_64.s
- Fortran code enhanced with assembler
for the SSE vectorization. The configure option
"--with-sse-assembler" uses these kernels.
They are worth trying on x86_64 without AVX,
e.g. Intel Nehalem.
Several
* elpa2_kernels_{real|complex}_sse-avx_*.c(pp)
- Optimized intrinisic code for x86_64
systems (i.e. Intel/AMD architecture)
using SSE2/SSE3 operations.
(Use gcc for compiling as Intel
compiler generates slower code!)
Note that you have to specify with
configure the flags
CFLAGS="-O3 -mavx -funsafe-loop-optimizations \
-funsafe-math-optimizations -ftree-vect-loop-version \
-ftree-vectorize"
and
CXXFLAGS="-O3 -mavx -funsafe-loop-optimizations \
-funsafe-math-optimizations -ftree-vect-loop-version \
-ftree-vectorize"
for best performace results.
For convenience the flag
"--with-avx-optimization" sets these
CFLAGS and CXXFLAGS automatically.
On Intel Sandybridge architectures the
configure option "--with-avx-sandybride"
uses the best combination, which is a
combination of block2 for real matrices
and block1 for complex matrices.
On AMD Bulldozer architectures the
configure option "--with-amd-bulldozer"
uses the best combination, which is a
combination of block4 for real matrices
and block1 for complex matrices.
Otherwise, you can try out your own
combinations with the configure options
"--with-avx-complex-block{1|2}" and
"--with-avx-real-block{2|4|6}".
So which version should be used?
================================
* On the IBM BlueGene/P, BlueGene/Q,
you should get the optimal performance using the optimized intrinsics/assembler versions
elpa2_kernels_{real|complex}_bg{p|q}.f90, respectively.
* On x86_64 systems (i.e. almost all Intel/AMD systems) you should get
the optimal performance using the optimized intrinsics/assembler versions
in elpa2_kernels_*.c or elpa2_kernels_{real|complex}_bg{p|q}.f90
respectively. However, here you have quite some choice to find your
optimal kernel.
* If you don't compile for one of these systems or you don't like to use assembler
for any reason, it is likely that you are best off using elpa2_kernels.f90.
Make a perfomance test with elpa2_kernels_simple.f90, however, to check if
your compiler doesn't get confused by the hand optimizations.
* If you want to develop your own optimized kernels for you platform, it is
easier to start with elpa2_kernels_simple.f90.
Don't let you confuse from the huge code in elpa2_kernels.f90, the mathemathics
done in the kernels is relatively trivial.
Markdown is supported
0% or .
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment