# Quantum Espresso in the Accelerated Benchmark Suite
## Document Author: A. Emerson (a.emerson@cineca.it) , Cineca.


## Contents

1.	Introduction	
2.	Requirements	
3.	Downloading the software	
4.	Compiling the application	
5.	Running the program	
6.	Example	
7.	References	


## 1. Introduction

### GPU version
The GPU port of Quantum Espresso is a version of the program which has been 
completely re-written in CUDA FORTRAN by Filippo Spiga. The version program used in these
experiments is v6.0, even though further versions becamse available later during the 
activity. 

### 2. Build Requirements

For complete build requirements and information see the following GitHub site:
[QE-GPU](https://github.com/fspiga/qe-gpu)
A short summary is given below:

Essential

 * The PGI compiler version 17.4 or above.
 * You need NVIDIA TESLA GPUS such as Kepler (K20, K40, K80) or Pascal (P100) or Volta (V100).
   No other cards are supported. NVIDIA TESLA P100 and V100 are strongly recommend
   for their on-board memory capacity and double precision performance.

Optional
* A parallel linear algebra library such as Scalapack, Intel MKL or IBM ESSL. If
  none is available  on your system then the installation can use a version supplied
  with the distribution.  


### 3. Downloading the software

Available from the web site given above. You can use, for example, ``git clone``
to download the software:
```bash
git clone https://github.com/fspiga/qe-gpu.git
```

### 4. Compiling and installing the application

Check the __README.md__ file in the downloaded files since the
procedure varies from distribution to distribution.
Most distributions do not have a ```configure``` command. Instead you copy a __make.inc__
file from the __install__ directory, and modify that directly before running make.
A number of templates are available in the distribution:
- make.inc_x86-64
- make.inc_CRAY_PizDaint
- make.inc_POWER_DAVIDE
- make.inc_POWER_SUMMITDEV

The second and third are particularly relevant in the PRACE infrastructure (ie. for CSCS
PizDaint and CINECA DAVIDE).
Run __make__ to see the options available. For the UEABS you should select the
pw program (the only module currently available)

```
make pw
```
 
The QE-GPU executable will appear in the directory `GPU/PW` and is called `pw-gpu.x`.

## 5. Running the program

Of course you need some input before you can run calculations. The
input files are of two types: 

1. A control file usually called `pw.in`

2. One or more pseudopotential files with extension `.UPF`
The pseudopotential files are placed in a directory specified in the
control file with the tag pseudo\_dir.  Thus if we have

```shell
pseudo_dir=./
```
then QE-GPU will look for the pseudopotential
files in the current directory. 

If using the PRACE benchmark suite the data files can be
downloaded from the QE website or the PRACE respository. For example, 
```shell
wget http://www.prace-ri.eu/UEABS/Quantum\_Espresso/QuantumEspresso_TestCaseA.tar.gz
```
Once uncompressed you can then run the program like this (e.g. using
MPI over 16 cores): 

```shell
mpirun -n 16 pw-gpu.x -input pw.in 
```

but check your system documentation since mpirun may be replaced by
`mpiexec, runjob, aprun, srun,` etc. Note also that normally you are not
allowed to run MPI programs interactively without using the
batch system. 

A couple of examples for PRACE systems are given in the next section.

### Hints for running the GPU version
The GPU port of Quantum Espresso runs almost entirely in the GPU memory. This means that jobs are restricted
by the memory of the GPU device, normally 16-32 GB, regardless of the main node memory. Thus, unless many nodes are used the user is likely to see job failures due to lack of memory, even for small datasets.
For example, on the CSCS Piz Daint supercomputer each node has only 1 NVIDIA Tesla P100 (16GB) which means that you will need at least 4 nodes to run even the smallest dataset (AUSURF in the UEABS).

## 6. Examples
Example job scripts for various supercomputer systems in PRACE are available in the repository.


### Computer System: DAVIDE P100 cluster, cineca


#### Running

Quantum Espresso has already been installed for the KNL nodes of
Marconi and can be accessed via a specific module:

``` shell
module load profile/knl
module load autoload qe/6.0_knl
```

On Marconi the default is to use the MCDRAM as cache, and have the
cache mode set as quadrant. Other settings for the KNLs on Marconi
haven't been substantailly tested for Quantum Espresso (e.g. flat
mode) but significant differences in performance for most inputs are
not expected.

An example SLURM batch script for the A2 partition is given below:

``` shell
#!/bin/bash
#SBATCH -N2
#SBATCH --tasks-per-node=34
#SBATCH -A <accountno>
#SBATCH -t 1:00:00


module purge
module load profile/knl
module load autoload qe/6.0_knl


export OMP_NUM_THREADS=4
export MKL_NUM_THREADS=${OMP_NUM_THREADS}

mpirun pw.x -npool 4 -input file.in > file.out

```

In the above with the SLURM directives we have asked for 2 KNL nodes (each with 68 cores) in
cache/quadrant mode and 93 Gb main memory each. We are running QE in
hybrid mode using 34 MPI processes/node, each with 4 OpenMP
threads/process and distributing the k-points in 4 pools; the Intel
MKl library will also use 4 OpenMP threads/process. 

Note that this script needs to be submitted using the KNL scheduler as follows:

``` shell
module load env-knl
sbatch myjob

```

Please check the Cineca documentation for information on using the
[Marconi KNL partition]
(https://wiki.u-gov.it/confluence/display/SCAIUS/UG3.1%3A+MARCONI+UserGuide#UG3.1:MARCONIUserGuide-SystemArchitecture).


## 7. References
1. QE-GPU build and download instructions, https://github.com/QEF/qe-gpu-plugin.

Last updated: 7-April-2017