@@ -249,7 +249,7 @@ The application codes that constitute the UEABS are:
-[QCD](#qcd)
-[Quantum Espresso](#espresso)
-[SPECFEM3D](#specfem3d)
-[TensorFlow](#tensorflow)
# ALYA <a name="alya"></a>
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@@ -482,17 +482,4 @@ QUANTUM ESPRESSO is written mostly in Fortran90, and parallelised using MPI and
| [- Website](https://geodynamics.org/cig/software/specfem3d_globe/)<br>[- Source](https://github.com/geodynamics/specfem3d_globe.git)<br>[- Bench](https://repository.prace-ri.eu/git/UEABS/ueabs/tree/r2.1-dev/specfem3d)<br>[- Summary](https://repository.prace-ri.eu/git/UEABS/ueabs/blob/r2.1-dev/specfem3d/PRACE_UEABS_Specfem3D_summary.pdf) | Geodynamics | Fortran & C | yes | yes | Yes (CUDA) | 100k Fortran & 20k C | The software package SPECFEM3D simulates three-dimensional global and regional seismic wave propagation based upon the spectral-element method (SEM). |
# TensorFlow <a name="tensorflow"></a>
TensorFlow (https://www.tensorflow.org) is a popular open-source library for symbolic math and linear algebra, with particular optimization for neural-networks-based machine learning workflow. Maintained by Google, it is widely used for research and production in both the academia and the industry.
TensorFlow supports a wide variety of hardware platforms (CPUs, GPUs, TPUs), and can be scaled up to utilize multiple compute devices on a single or multiple compute nodes. The main objective of this benchmark is to profile the scaling behavior of TensorFlow on different hardware, and thereby provide a reference baseline of its performance for different sizes of applications.
There are many open-source datasets available for benchmarking TensorFlow, such as `mnist`, `fashion_mnist`, `cifar`, `imagenet`, and so on. This benchmark suite, however, would like to focus on a scientific research use case. `DeepGalaxy` is a code built with TensorFlow, which uses deep neural network to classify galaxy mergers in the Universe, observed by the Hubble Space Telescope and the Sloan Digital Sky Survey.
