Daniel Egloff overviews Alea, an F# alternatives to CUDA C/C++ and OpenCL C++, showing how to write GPU scripts and perform dynamic compilation in F#.
Aish Fenton discusses Netflix' machine learning algorithms, including distributed Neural Networks on AWS GPUs, providing insight into offline experimentation and online AB testing.
Olivier Chafik discusses how to make a practical use of reified trees in Scala, with two applications: run-time (re)compilation for extreme speed, and conversion to another language (OpenCL).
Matthew Moloney discusses using F# and .NET inside Excel, demonstrating doing big data, cloud computing, using GPGPU and compiling F# Excel UDFs.
Jarred Nicholls explains how browsers leverage the GPU to speed up complex web pages by primitive drawing, composing layers and using tiles backing stores.
Graham Brooks discusses using GPU for application development, explaining how GPUs can be used for general purpose programming and how continuous integration can be applied.
Andrew Sheppard overviews the driving forces behind GPU’s adoption by the financial industry, and explains the use of the Monte Carlo technique on GPUs.
Cyril Zeller introduces NVIDIA CUDA development, showing how to write and execute C programs on the GPU, how to manage GPU memory and communication with the CPU.