Adding patterns

docs/source/accelerators.rst

@@ -1,8 +1,8 @@
 .. _accelerators:
 
-*********************************
-Accelerators - OpenMP/CUDA/OpenCL
-*********************************
+******************
+OpenMP/CUDA/OpenCL
+******************
 
 The most computationally intensive parts of gprMax, which are the FDTD solver loops, have been parallelised using different CPU and GPU accelerators to offer performance and flexibility.
 
@@ -17,7 +17,7 @@ Some of these accelerators and frameworks require additional software to be inst
 .. note::
 
     You can use the ``get_host_spec.py`` module (in ``toolboxes/Utilities``) to help you understand what hardware (CPU/GPU) you have and how gprMax can use it with the aforementioned accelerators.
-    
+
 
 OpenMP
 ======
@@ -58,13 +58,13 @@ Software required
 The following steps provide guidance on how to install the extra components to allow gprMax to run on your NVIDIA GPU:
 
 1. Install the `NVIDIA CUDA Toolkit <https://developer.nvidia.com/cuda-toolkit>`_. You can follow the Installation Guides in the `NVIDIA CUDA Toolkit Documentation <http://docs.nvidia.com/cuda/index.html#installation-guides>`_ You must ensure the version of CUDA you install is compatible with the compiler you are using. This information can usually be found in a table in the CUDA Installation Guide under System Requirements.
-2. You may need to add the location of the CUDA compiler (:code:`nvcc`) to your user path environment variable, e.g. for Windows :code:`C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v10.0\bin` or Linux/macOS :code:`/Developer/NVIDIA/CUDA-10.0/bin`.
-3. Install the pycuda Python module. Open a Terminal (Linux/macOS) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`conda activate gprMax`. Run :code:`pip install pycuda`
+2. You may need to add the location of the CUDA compiler (``nvcc``) to your user path environment variable, e.g. for Windows ``C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\vX.X\bin`` or Linux/macOS ``/Developer/NVIDIA/CUDA-X.X/bin``.
+3. Install the pycuda Python module. Open a Terminal (Linux/macOS) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment ``conda activate gprMax``. Run ``pip install pycuda``
 
 Example
 -------
 
-Open a Terminal (Linux/macOS) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment :code:`conda activate gprMax`
+Open a Terminal (Linux/macOS) or Command Prompt (Windows), navigate into the top-level gprMax directory, and if it is not already active, activate the gprMax conda environment ``conda activate gprMax``
 
 Run one of the test models:
 
@@ -74,7 +74,7 @@ Run one of the test models:
 
 .. note::
 
-    If you want to select a specific GPU card on your system, you can specify an integer after the :code:`-gpu` flag. The integer should be the NVIDIA CUDA device ID for a specific GPU card. If it is not specified it defaults to device ID 0.
+    If you want to select a specific GPU card on your system, you can specify an integer after the ``-gpu`` flag. The integer should be the NVIDIA CUDA device ID for a specific GPU card. If it is not specified it defaults to device ID 0.
 
 
 OpenCL
@@ -111,4 +111,4 @@ For example, to run a B-scan that contains 60 A-scans (traces) on a system with
 
 .. note::
 
-    The argument given with `-mpi` is number of MPI tasks, i.e. master + workers, for MPI task farm. So in this case, 1 master (CPU) and 4 workers (GPU cards). The integers given with the `-gpu` argument are the NVIDIA CUDA device IDs for the specific GPU cards to be used.
+    The argument given with ``-mpi`` is number of MPI tasks, i.e. master + workers, for MPI task farm. So in this case, 1 master (CPU) and 4 workers (GPU cards). The integers given with the ``-gpu`` argument are the NVIDIA CUDA device IDs for the specific GPU cards to be used.

docs/source/hpc.rst

@@ -1,8 +1,8 @@
 .. _hpc:
 
-**************************
-High-performance computing
-**************************
+***
+HPC
+***
 
 High-performance computing (HPC) environments usually require jobs to be submitted to a queue using a job script. The following are examples of job scripts for a HPC environment that uses `Open Grid Scheduler/Grid Engine <http://gridscheduler.sourceforge.net/index.html>`_, and are intended as general guidance to help you get started. Using gprMax in an HPC environment is heavily dependent on the configuration of your specific HPC/cluster, e.g. the names of parallel environments (``-pe``) and compiler modules will depend on how they were defined by your system administrator.