Added project on GPU sub-gridding, and added John as mentor to some projects.

gsoc/project-ideas-2021.md

@@ -7,11 +7,11 @@ gprMax is planning to participate in the [Google Summer of Code](https://summero
 
 The aim of this project is to develop user-friendly tools with which the user will be able to effectively parametrise the investigated problem and define the expected range of the parameters. Subsequently, big data will be generated in an automatic manner to be subsequently used for training a deep learning scheme to predict the electromagnetic (EM) response subject to the parameters of the models. An AI-based real-time EM solver will be orders of magnitude faster than conventional FDTD and it will facilitate electromagnetic imaging, microwave tomography, antenna design etc.  
 
-**Skills required:** Python, Machine Learning, Keras, Tensoflow etc.
+**Skills required:** Python, Machine Learning, Keras, Tensorflow etc.
 
 **Difficulty:** Hard
 
-**Mentor(s):** Dr Iraklis Giannakis (), Dr Craig Warren (craig@gprmax.com), and Dr Antonis Giannopoulos (antonis@gprmax.com) 
+**Mentor(s):** Dr Iraklis Giannakis (iraklis.giannakis@abdn.ac.uk), Dr Craig Warren (craig@gprmax.com), Dr Antonis Giannopoulos (antonis@gprmax.com), and Dr John Hartley (johnmatthewhartley@gmail.com)
 
 
 ## 2. An Automatic Framework for Generating EM Big Data
@@ -24,7 +24,7 @@ Machine learning relies on coherent, equally distributed, and well-labelled data
 
 **Difficulty:** Easy
 
-**Mentor(s):** Dr Iraklis Giannakis (), Dr Craig Warren (craig@gprmax.com), and Dr Antonis Giannopoulos (antonis@gprmax.com)
+**Mentor(s):** Dr Iraklis Giannakis (iraklis.giannakis@abdn.ac.uk), Dr Craig Warren (craig@gprmax.com), Dr Antonis Giannopoulos (antonis@gprmax.com), and Dr John Hartley (johnmatthewhartley@gmail.com)
 
 
 ## 3. Web-based framework for model building
@@ -37,10 +37,23 @@ Many models, especially for Ground Penetrating Radar (GPR), can be easily specif
 
 **Difficulty:** Medium
 
-**Mentor(s):** Dr Antonis Giannopoulos (antonis@gprmax.com) and Dr Craig Warren (craig@gprmax.com)
+**Mentor(s):** Dr John Hartley (johnmatthewhartley@gmail.com), Dr Antonis Giannopoulos (antonis@gprmax.com) and Dr Craig Warren (craig@gprmax.com)
 
 
-## 4. Improved installation tools
+## 5. GPU based sub-gridding
+
+The aim of the project is to create use NVIDIA CUDA to accelerate our code for sub-gridded meshes to enable running on GPU. The performance (speed) of the solver is a critical feature as simulations become ever larger and more complex.
+
+The solver is based on the [Finite-Difference Time-Domain (FDTD)](https://en.wikipedia.org/wiki/Finite-difference_time-domain_method) method, which has shown significant performance benefits when parallelised – particularly on GPU. Currently we have parallelised using NVIDIA CUDA (with PyCUDA) the main FDTD mesh. The speed-up on GPU is significant (x30 compared to parallelised CPU), therefore we would like to expand this capability to include our code that handles the sub-gridded FDTD meshes.
+
+**Skills required:** Python, CUDA, some knowledge of GPU programming would be beneficial.
+
+**Difficulty:** Medium
+
+**Mentor(s):** Dr John Hartley (johnmatthewhartley@gmail.com) and Dr Craig Warren (craig@gprmax.com)
+
+
+## 6. Improved installation tools
 
 The aim of this project is to create a simplified and more user-friendly installation workflow for the software.
 
@@ -50,10 +63,10 @@ gprMax is predominately written in Python, but some of the performance-critical
 
 **Difficulty:** Medium
 
-**Mentor(s):** Dr Craig Warren (craig@gprmax.com)
+**Mentor(s):** Dr John Hartley (johnmatthewhartley@gmail.com) and Dr Craig Warren (craig@gprmax.com)
 
 
-## 5. Comprehensive test and benchmarking suite
+## 7. Comprehensive test and benchmarking suite
 
 The aim of this project is to develop a comprehensive test suite and benchmarking toolset.
 
@@ -63,14 +76,14 @@ Currently gprMax includes a series of tests that verify specific simulation resu
 
 **Difficulty:** Medium
 
-**Mentor(s):** Dr Antonis Giannopoulos (antonis@gprmax.com) and Dr Craig Warren (craig@gprmax.com)
+**Mentor(s):** Dr Craig Warren (craig@gprmax.com) and Dr Antonis Giannopoulos (antonis@gprmax.com) and 
 
 
-## 6. Geometry visualisation
+## 8. Geometry visualisation
 
 The aim of this project is to improve the handling and visualisation of model geometries.
 
-Being able to visualise and check the geometry and materials of models before running simulations is vital. It minimises the risk of wasting computational resources by running incorrect models. Currently gprMax uses the [Visualization Toolkit (VTK)](https://vtk.org) format to store geometry information to file, and [Paraview](https://www.paraview.org) for visualisation. This allows the geometrical information to be viewed but does not easily permit material information from the model to be checked. A tighter integration is required.
+Being able to visualise and check the geometry and materials of models before running simulations is vital. It minimises the risk of wasting computational resources by running incorrect models. Currently gprMax uses the [Visualization Toolkit (VTK)](https://vtk.org) format to store geometry information to file, and [Paraview](https://www.paraview.org) for visualisation. This allows the geometrical information to be viewed but does not easily permit material information from the model to be checked. A tighter integration is required, possibly considering a browser-based solution.
 
 **Skills required:** Python, familiarity with VTK and Paraview would beneficial.
 
@@ -79,7 +92,7 @@ Being able to visualise and check the geometry and materials of models before ru
 **Mentor(s):** Dr Craig Warren (craig@gprmax.com)
 
 
-## 7. Modelling complex materials
+## 9. Modelling complex materials
 
 The aim of this project is to couple and enhance a series of scripts that have been developed to allow materials with complex (frequency dependent) properties to be modelled.
 
@@ -89,10 +102,10 @@ Often materials that required to be simulated have complex electromagnetic prope
 
 **Difficulty:** Medium
 
-**Mentor(s):** Dr Iraklis Giannakis () and Dr Antonis Giannopoulos (antonis@gprmax.com)
+**Mentor(s):** Dr Iraklis Giannakis (iraklis.giannakis@abdn.ac.uk) and Dr Antonis Giannopoulos (antonis@gprmax.com)
 
 
-## 8. Importing geometrical information from laser scanners
+## 10. Importing geometrical information from laser scanners
 
 The aim of this project is to import geometric data acquired from terrestrial laser scanners. The ability to directly model real objects and topographies without entering their geometries manually would be very useful development.
 
@@ -105,7 +118,7 @@ A laser scanner takes distance measurements in every direction to rapidly captur
 **Mentor(s):** Dr Craig Warren (craig@gprmax.com) and Dr Iraklis Giannakis ()
 
 
-## 9. MPI domain decomposition
+## 11. MPI domain decomposition
 
 The aim of the project is to create version of the solver engine that uses domain decomposition based on the [Message Passing Interface (MPI)](https://en.wikipedia.org/wiki/Message_Passing_Interface) model.