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Add the FMV tutorial content.
The Function Multi-versioning tutorial content was provided by Victor Rodriguez. This version has been edited and the markup fixed to comply with the documentation repo's guidelines. Signed-off-by: Rodrigo Caballero <rodrigo.caballero.abraham@intel.com>
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Rodrigo Caballero
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.. _fmv:
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Use the Function Multi Version patch generator
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##############################################
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CPU architectures often gain interesting new instructions as they evolve but
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application developers find it difficult to take advantage of those
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instructions. The reluctance to lose backward-compatibility is one of the
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main roadblocks slowing developers from using advancements in newer computing
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architectures. :abbr:`FMV (Function multi-versioning)`, which first appeared
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in GCC 4.8, is a way to have multiple implementations of a function, each
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using a different architecture's specialized instruction-set extensions. GCC
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6 introduces changes to FMV to make it even easier to bring architecture-
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based optimizations to the application code.
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In this tutorial we will use FMV on general code and on :abbr:`FFT Fast
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Fourier Transform` library code. Upon completing the tutorial, you will be
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able to use this technology on your code and use the libraries to deploy
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architecture-based optimizations to your application code.
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Install and configure a Clear Linux host on bare metal
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******************************************************
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First, follow our guide to :ref:`bare-metal-install`.
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Once the bare metal installation and initial configuration are complete,
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add the `desktop-dev` bundle to the system.
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desktop-dev: contains the necessary development tools like GCC\* and Perl\*.
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To install the bundles, run the following command in the :file:`$HOME`
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directory:
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.. code-block:: bash
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sudo swupd bundle-add desktop-dev
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Detect loop vectorization candidates
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************************************
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Now, we need to detect the loop vectorization candidates to be cloned for
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multiple platforms with FMV. As an example, we will use the following
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simple C code:
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.. code-block:: c
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:linenos:
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#include <stdio.h>
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#include <stdlib.h>
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#include <sys/time.h>
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#define MAX 1000000
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int a[256], b[256], c[256];
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void foo(){
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int i,x;
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for (x=0; x<MAX; x++){
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for (i=0; i<256; i++){
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a[i] = b[i] + c[i];
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}
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}
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}
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int main(){
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foo();
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return 0;
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}
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Save the example code as :file:`example.c` and build with the
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following flags:
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.. code-block:: bash
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gcc -O3 -fopt-info-vec example.c -o example
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The build generates the following output:
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.. code-block:: console
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example.c:11:9: note: loop vectorized
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example.c:11:9: note: loop vectorized
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The output shows that line 11 is a good candidate for vectorization:
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.. code-block:: c
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for (i=0; i<256; i++){
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a[i] = b[i] + c[i];
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Generate the FMV patch
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**********************
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To generate the FMV patch with the `make-fmv-patch`_ project, we
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must clone the project and generate a log file with the loop vectorized
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information:
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.. code-block:: bash
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git clone https://github.com/clearlinux/make-fmv-patch.git
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gcc -O3 -fopt-info-vec example.c -o example &> log
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To generate the patch files, execute:
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.. code-block:: bash
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perl ./make-fmv-patch/make-fmv-patch.pl log .
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The make-fmv-patch.pl take two arguments: <buildlog> and <sourcecode>. Replace with the proper values and execute:
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.. code-block:: bash
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perl make-fmv-patch.pl <buildlog> <sourcecode>
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The command generates the following :file:`example.c.patch` patch:
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.. code-block:: console
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--- ./example.c 2017-09-27 16:05:42.279505430 +0000
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+++ ./example.c~ 2017-09-27 16:19:11.691544026 +0000
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@@ -5,6 +5,7 @@
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int a[256], b[256], c[256];
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+__attribute__((target_clones("avx2","arch=atom","default")))
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void foo(){
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int i,x;
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for (x=0; x<MAX; x++){
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The `make-fmv-patch` is recommended to add the attribute generating the
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target clones on the function foo. Thus, we can have the following code:
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.. code-block:: c
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#include <stdio.h>
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#include <stdlib.h>
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#include <sys/time.h>
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#define MAX 1000000
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int a[256], b[256], c[256];
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__attribute__((target_clones("avx2","arch=atom","default")))
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void foo(){
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int i,x;
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for (x=0; x<MAX; x++){
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for (i=0; i<256; i++){
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a[i] = b[i] + c[i];
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}
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}
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}
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int main(){
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foo();
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return 0;
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}
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By changing the value of the `$avx2` variable, we can change the target clones when adding the patches or in the make-fmv-patch.pl script:
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.. code-block:: perl
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my $avx2 = '__attribute__((target_clones("avx2","arch=atom","default")))'."\n";
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Compile the code again with FMV and add the option to analyze the `objdump`:
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.. code-block:: bash
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gcc -O3 example.c -o example -g
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objdump -S example | less
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You can see the multiple clones of the foo function:
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.. code-block:: console
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foo
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foo.avx2.0
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foo.arch_atom.1
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The cloned functions use AVX2 registers and vectorized instructions. You can verify this with:
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.. code-block:: assembly
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vpaddd (%r8,%rax,1),%ymm0,%ymm0
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vmovdqu %ymm0,(%rcx,%rax,1)
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FTT project example
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*******************
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To follow the same approach with a package like FFT, we must get the build log file with the `-fopt-info-vec` flag:
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::
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~/make-fmv-patch/make-fmv-patch.pl results/build.log fftw-3.3.6-pl2/
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patching fftw-3.3.6-pl2/libbench2/verify-lib.c @ lines (36 114 151 162 173 195 215 284)
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patching fftw-3.3.6-pl2/tools/fftw-wisdom.c @ lines (150)
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patching fftw-3.3.6-pl2/libbench2/speed.c @ lines (26)
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patching fftw-3.3.6-pl2/tests/bench.c @ lines (27)
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patching fftw-3.3.6-pl2/libbench2/util.c @ lines (181)
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patching fftw-3.3.6-pl2/libbench2/problem.c @ lines (229)
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patching fftw-3.3.6-pl2/tests/fftw-bench.c @ lines (101 147 162 249)
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patching fftw-3.3.6-pl2/libbench2/mp.c @ lines (79 190 215)
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patching fftw-3.3.6-pl2/libbench2/caset.c @ lines (5)
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patching fftw-3.3.6-pl2/libbench2/verify-r2r.c @ lines (44 187 197 207 316 333 723)
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Thus, files like :file:`fftw-3.3.6-pl2/tools/fftw-wisdom.c.patch` generate
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patches like:
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.. code-block:: git
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1 --- fftw-3.3.6-pl2/libbench2/verify-lib.c 2017-01-27 21:08:13.000000000 +0000
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2 +++ fftw-3.3.6-pl2/libbench2/verify-lib.c~ 2017-09-27 17:49:21.913802006 +0000
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3 @@ -33,6 +33,7 @@
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4
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5 double dmax(double x, double y) { return (x > y) ? x : y; }
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6
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7 +__attribute__((target_clones("avx2","arch=atom","default")))
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8 static double aerror(C *a, C *b, int n)
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9 {
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10 if (n > 0) {
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11 @@ -111,6 +112,7 @@
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12 }
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13
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14 /* make array hermitian */
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15 +__attribute__((target_clones("avx2","arch=atom","default")))
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16 void mkhermitian(C *A, int rank, const bench_iodim *dim, int stride)
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17 {
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18 if (rank == 0)
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19 @@ -148,6 +150,7 @@
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20 }
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21
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22 /* C = A + B */
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23 +__attribute__((target_clones("avx2","arch=atom","default")))
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24 void aadd(C *c, C *a, C *b, int n)
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25 {
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26 int i;
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27 @@ -159,6 +162,7 @@
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28 }
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29
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30 /* C = A - B */
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31 +__attribute__((target_clones("avx2","arch=atom","default")))
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32 void asub(C *c, C *a, C *b, int n)
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33 {
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34 int i;
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35 @@ -170,6 +174,7 @@
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36 }
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37
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38 /* B = rotate left A (complex) */
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39 +__attribute__((target_clones("avx2","arch=atom","default")))
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40 void arol(C *b, C *a, int n, int nb, int na)
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41 {
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42 int i, ib, ia;
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43 @@ -192,6 +197,7 @@
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44 }
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45 }
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With these patches, we can select where to apply the FMV technology making
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bringing architecture-based optimizations to application code even easier.
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**Congratulations! **
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You have successfully installed an FMV development environment on Clear
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Linux. Furthermore, you used cutting edge compiler technology to improve the
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performance of your application based on Intel Architecture technology and
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profiling of the specific execution of your application.
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.. _make-fmv-patch: https://github.com/clearlinux/make-fmv-patch
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@@ -14,3 +14,4 @@ specific |CLOSIA| use cases.
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machine-learning/machine-learning
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azure
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multi-boot/multi-boot
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fmv
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