Commit 07cba17e authored by Andrew Emerson's avatar Andrew Emerson
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Update README_ACC.md

parent 2fb7ae55
......@@ -19,34 +19,32 @@ completely re-written in CUDA FORTRAN by Filippo Spiga. The version program used
experiments is v6.0, even though further versions becamse available later during the
activity.
## 2. Requirements
## 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
* Quantum ESPRESSO 5.4
* Kepler GPU: (minimum) CUDA SDK 6.5
* Pascal GPU: (minimum) CUDA SDK 8.0
* 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 or Intel MKL. If
none is available on your system then the installation can use a
version supplied with the distribution.
* 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
### QE distribution
Many packages are available from the download page but since you need
only the main base package for the benchmark suite, the
`expresso-5.4.0.tar.gz` file will be sufficient. This can be downloaded
as:
[http://www.quantum-espresso.org/download] (http://www.quantum-espresso.org/download)
### GPU plugin
The GPU source code can be conveniently downloaded from this link:
[https://github.com/QEF/qe-gpu-plugin] (https://github.com/QEF/qe-gpu-plugin)
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 the application
The QE-GPU gives more details but for the benchmark suite we followed
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