From d79e40b3faaf3fe45b2482d3113a5fca20cae5e5 Mon Sep 17 00:00:00 2001
From: Jan Siwiec <jan.siwiec@vsb.cz>
Date: Wed, 17 Jan 2024 13:13:44 +0100
Subject: [PATCH] Update power10.md
---
docs.it4i/cs/guides/power10.md | 71 ++++++++++++++++++++--------------
1 file changed, 41 insertions(+), 30 deletions(-)
diff --git a/docs.it4i/cs/guides/power10.md b/docs.it4i/cs/guides/power10.md
index 265293336..28c55dad8 100644
--- a/docs.it4i/cs/guides/power10.md
+++ b/docs.it4i/cs/guides/power10.md
@@ -25,6 +25,7 @@ The platform offers both `GNU` based and proprietary IBM toolchains for building
## Building Applications
Our sample application depends on `BLAS`, therefore we start by loading following modules (regardless of which toolchain we want to use):
+
```
ml GCC OpenBLAS
```
@@ -32,10 +33,13 @@ ml GCC OpenBLAS
### GCC Toolchain
In the case of GCC toolchain we can go ahead and compile the application as usual using either `g++`
+
```
g++ -lopenblas hello.cpp -o hello
```
+
or `gfortran`
+
```
gfortran -lopenblas hello.f90 -o hello
```
@@ -44,6 +48,7 @@ as usual.
### IBM Toolchain
The IBM toolchain requires additional environment setup as it is installed in `/opt/ibm` and is not exposed as a module
+
```
IBM_ROOT=/opt/ibm
OPENXLC_ROOT=$IBM_ROOT/openxlC/17.1.1
@@ -57,10 +62,12 @@ export LD_LIBRARY_PATH=$OPENXLF_ROOT/lib:$LD_LIBRARY_PATH
```
from there we can use either `ibm-clang++`
+
```
ibm-clang++ -lopenblas hello.cpp -o hello
```
or `xlf`
+
```
xlf -lopenblas hello.f90 -o hello
```
@@ -81,10 +88,12 @@ export LD_LIBRARY_PATH=$ESSL_ROOT/lib64:$LD_LIBRARY_PATH
The simplest way to utilize `ESSL` in application, which already uses `BLAS` or `CBLAS` routines is to link with the provided `libessl.so`. This can be done by replacing `-lopenblas` with `-lessl` or `-lessl -lopenblas` (in case `ESSL` does not provide all required `BLAS` routines).
In practice this can look like
+
```
g++ -L${ESSL_ROOT}/lib64 -lessl -lopenblas hello.cpp -o hello
```
or
+
```
gfortran -L${ESSL_ROOT}/lib64 -lessl -lopenblas hello.f90 -o hello
```
@@ -95,6 +104,7 @@ and similarly for IBM compilers (`ibm-clang++` and `xlf`).
The `hello world` example application (written in `C++` and `Fortran`) uses simple stationary probability vector estimation to illustrate use of GEMM (BLAS 3 routine).
Stationary probability vector estimation in `C++`:
+
```c++
#include <iostream>
#include <vector>
@@ -107,45 +117,45 @@ const size_t MATRIX_SIZE = 1024;
int main(int argc, char *argv[])
{
const size_t matrixElements = MATRIX_SIZE*MATRIX_SIZE;
-
+
std::vector<float> a(matrixElements, 1.0f / float(MATRIX_SIZE));
-
+
for(size_t i = 0; i < MATRIX_SIZE; ++i)
a[i] = 0.5f / (float(MATRIX_SIZE) - 1.0f);
a[0] = 0.5f;
-
+
std::vector<float> w1(matrixElements, 0.0f);
std::vector<float> w2(matrixElements, 0.0f);
-
+
std::copy(a.begin(), a.end(), w1.begin());
-
+
std::vector<float> *t1, *t2;
t1 = &w1;
t2 = &w2;
-
+
auto c1 = std::chrono::steady_clock::now();
-
+
for(size_t i = 0; i < ITERATIONS; ++i)
{
std::fill(t2->begin(), t2->end(), 0.0f);
-
- cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, MATRIX_SIZE, MATRIX_SIZE, MATRIX_SIZE,
+
+ cblas_sgemm(CblasRowMajor, CblasNoTrans, CblasNoTrans, MATRIX_SIZE, MATRIX_SIZE, MATRIX_SIZE,
1.0f, t1->data(), MATRIX_SIZE,
- a.data(), MATRIX_SIZE,
+ a.data(), MATRIX_SIZE,
1.0f, t2->data(), MATRIX_SIZE);
-
+
std::swap(t1, t2);
}
-
+
auto c2 = std::chrono::steady_clock::now();
-
+
for(size_t i = 0; i < MATRIX_SIZE; ++i)
{
std::cout << (*t1)[i*MATRIX_SIZE + i] << " ";
}
-
+
std::cout << std::endl;
-
+
std::cout << "Elapsed Time: " << std::chrono::duration<double>(c2 - c1).count() << std::endl;
return 0;
@@ -153,57 +163,58 @@ int main(int argc, char *argv[])
```
Stationary probability vector estimation in `Fortran`:
+
```fortran
program main
implicit none
-
+
integer :: matrix_size, iterations
integer :: i
real, allocatable, target :: a(:,:), w1(:,:), w2(:,:)
real, dimension(:,:), contiguous, pointer :: t1, t2, tmp
real, pointer :: out_data(:), out_diag(:)
integer :: cr, cm, c1, c2
-
+
iterations = 32
matrix_size = 1024
-
+
call system_clock(count_rate=cr)
call system_clock(count_max=cm)
-
+
allocate(a(matrix_size, matrix_size))
allocate(w1(matrix_size, matrix_size))
allocate(w2(matrix_size, matrix_size))
-
+
a(:,:) = 1.0 / real(matrix_size)
a(:,1) = 0.5 / real(matrix_size - 1)
a(1,1) = 0.5
-
+
w1 = a
w2(:,:) = 0.0
-
+
t1 => w1
t2 => w2
-
+
call system_clock(c1)
-
+
do i = 0, iterations
t2(:,:) = 0.0
-
+
call sgemm('N', 'N', matrix_size, matrix_size, matrix_size, 1.0, t1, matrix_size, a, matrix_size, 1.0, t2, matrix_size)
-
+
tmp => t1
t1 => t2
t2 => tmp
end do
-
+
call system_clock(c2)
-
+
out_data(1:size(t1)) => t1
out_diag => out_data(1::matrix_size+1)
-
+
print *, out_diag
print *, "Elapsed Time: ", (c2 - c1) / real(cr)
-
+
deallocate(a)
deallocate(w1)
deallocate(w2)
--
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