Added missing updates.

Author: tbetcke

src/SUMMARY.md

@@ -6,4 +6,6 @@
 
 [Presentations on Programming Models](programming_models_talks.md)
 
+[Presentations on Mathematical Libraries](mathematical_libraries.md)
+
 [Setting up your environment](environment.md)

src/mathematical_libraries.md

@@ -0,0 +1,20 @@
+# Presentations on mathematical libraries
+
+The morning session on Thursday focuses on mathematical libraries.
+We will hear from a range of projects spanning core linear algebra
+up to complex multiphysics environments.
+
+Each presentation should be at most 15 minutes in length, talk about the software and perform a small demonstration of the software.
+The order of talks will be randomly decided on the day.
+
+- **[Blas](https://www.netlib.org/blas/)/[Lapack](https://www.netlib.org/lapack/lug)** are the core of almost any modern dense linear algebra package. **Blas** is a definition of basic matrix operations, implemented through fast low-level libraries (e.g. [Openblas](http://www.openmathlib.org/OpenBLAS/), [Blis](https://github.com/flame/blis), [Apple Accelerate](https://developer.apple.com/documentation/accelerate)). **Lapack** implements many standard matrix decompositions (e.g. LU, SVD, QR, etc.) based upon efficient Blas calls. **(Maciej, Bryce)**
+- **[PETSc](https://petsc.org/release/)** is widely used package on HPC systems for linear and nonlinear solvers, supported by huge community of users and developers. **(Shany, Anees)**
+- **[Suitesparse](https://people.engr.tamu.edu/davis/suitesparse.html)** is a widely used package specialising on sparse matrix solvers. The solvers built into Matlab, Julia, and many other mathematical environments are built around Suitesparse. **(Ross, Callum, Jose Miguel)**
+- **[Gmsh](https://gmsh.info/)** is a powerful package for mesh discretisation and visualization. **(Lewis, Louis)**
+- **[Firedrake](https://www.firedrakeproject.org/)** is a popular
+  PDE solver package based on just-in-time code generation.
+**(Anastasia, Kelan)**
+- **[VTK](https://vtk.org/)** is an essential visualization library and also data-exchange format for grid based data in scientific computing. [Paraview](https://www.paraview.org/) and other visualization tools are built around VTK. **(Divij, Ammar)**
+- **[FMM3D](https://fmm3d.readthedocs.io/en/latest/)** is an outstanding library for the fast solution of n-body interactions.
+- **[Chebfun](https://www.chebfun.org/)** allows the solution of many ODEs and related problems to almost machine precision accuracy. **(Benjamin, Lucas)**
+- **[scikit-learn](https://scikit-learn.org/stable/)** delivers a range of simple to use algorithms for machine learning. **(Skye, Oliver, Advaith)**

src/programming_models_talks.md

@@ -5,62 +5,26 @@ HPC Programming Models. You are asked to prepare slides
 demonstrating the topic together with some simple example live
 example codes.
 
-## Distributed programming with MPI
+Each presentation should be at most 15 minutes in length, talk about the software and perform a small demonstration of the software.
+The order of talks will be randomly decided on the day.
 
-MPI is a distributed programming model going back to
-1991. Even more than 30 years later it is still the dominant
-model for parallelisation on large-scale HPC clusters.
 
-## Shared memory paralellisation with OpenMP
-
-OpenMP goes back to 1997 and is a leading model for
-shared memory parallelisation on CPUs. In recent versions it
-even supports some offloading to GPUs.
-
-## CUDA for software development on NVIDIA GPUs
-
-CUDA is the dominant programming model for NVIDIA GPUs. It is
+- **Distributed programming with MPI.**
+  MPI is a distributed programming model going back to 1991. Even more than 30 years later it is still the dominant model for parallelisation on large-scale HPC clusters. **(Maciej, Bryce)**
+- **Shared memory paralellisation with OpenMP.**   OpenMP goes back to 1997 and is a leading model for shared memory parallelisation on CPUs. In recent versions it even supports some offloading to GPUs. **(Shany, Anees)**
+- **CUDA for software development on NVIDIA GPUs.** CUDA is the dominant programming model for NVIDIA GPUs. It is
 a large ecosystem consisting of CUDA compiler , development
-environments and scientific libraries.
-
-## AMD HIP
-
-HIP is AMD's programming model for AMD GPUs. It is fairly similar
-to CUDA and allows for easy portability between CUDA and HIP code.
-
-## OpenCL - Vendor independent GPU programming
-
-OpenCL was originally developed as an open standard to compute
+environments and scientific libraries. **(Ross, Callum, Jose Miguel)**
+- **OpenCL - Vendor independent GPU programming.** OpenCL was originally developed as an open standard to compute
 with the vendor dependent CUDA ecosystem. While it is less
 used directly in scientific computing it is still very important
 for development on embedded devices. And even for Scientific
 Computing applications it still provides a powerful way to
-write device independent GPU kernels.
-
-## SYCL - A growing heterogeneous computing standard
-
-SYCL has evolved from OpenCL to provide a modern heterogeneous
-computing environment in C++. It is the programming standard
-underlying modern Intel GPU accelerators but also supports AMD
-and NVIDIA.
-
-## Metal - Apple's take on GPU computing
-
-Apple Metal is rarely used for scientific computing. But it is
-a powerful model and the only way to access GPU cores on Apple
-Silicon.
-
-## SIMD - Low-level vector registers
-
-SIMD is not really a programming model. It describes the set
-of instructions found on most modern CPUs to efficiently execute operations on vector registers. Any CPU code targeted for
-performance is optimised to make use of SIMD instructions as much
-as possible.
-
-## Parallel Programming with Jax
-
-Jax is a library for efficient parallel array computations.
-It is mostly used for Machine Learning but can also be used
-to efficiently implement many other algorithms in scientific
-computing.
+write device independent GPU kernels. **(Lewis, Louis)**
+- **SYCL - A growing heterogeneous computing standard.** SYCL has evolved from OpenCL to provide a modern heterogeneous computing environment in C++. It is the programming standard underlying modern Intel GPU accelerators but also supports AMD and NVIDIA. **(Anastasia, Kelan)**
+- **Metal - Apple's take on GPU computing.**
+  Apple Metal is rarely used for scientific computing. But it is a powerful model and the only way to access GPU cores on Apple Silicon. **(Divij, Ammar)**
+- **SIMD - Low-level vector registers.**
+  SIMD is not really a programming model. It describes the set of instructions found on most modern CPUs to efficiently execute operations on vector registers. Any CPU code targeted for performance is optimised to make use of SIMD instructions as much as possible. **(Benjamin, Lucas)**
+- **Parallel Programming with Jax.** Jax is a library for efficient parallel array computations. It is mostly used for Machine Learning but can also be used to efficiently implement many other algorithms in scientific computing. **(Skye, Oliver, Advaith)**
 

src/schedule.md

@@ -1,7 +1,6 @@
 # Schedule
 
-Each day consists of a morning session from 10am to 12:30 and an afternoon
-session from 2pm to 4pm.
+Each day consists of a morning session from 10am to 12:30 and an afternoon session from 2pm to 4pm.
 
 ## Monday
 - Morning Session
@@ -21,13 +20,13 @@ session from 2pm to 4pm.
 - Morning Session
   - Introduction to dense and sparse linear algebra software
 - Afternoon Session
-  - The UCL HPC Facilities
+  - Preparation time for Thursday presentations
 
 ## Thursday
 - Morning Session
   - Presentations on mathematical software libraries
 - Afternoon session
-  - Open-source Hackathon
+  - Hackathon on mathematical software
 
 ## Friday
 - Morning Session