Online shopping is nothing if not efficient. Walmart together with new Jersey-startup Jet take things a step further, using AI and Deep Learning to optimize their entire E-Commerce business. The first AI application we discuss is Jet’s unique smart merchant selection: the platform finds the best merchant and warehouse combination in real time so that the total order cost is as low as possible. Then we show how to efficiently pack fresh and frozen orders with Deep Reinforcement Learning. The value of this approach is not just to find the best boxes and the tightest packing, but also the least amount of coolant and its placement so that the temperature of all items stays within the required limits during shipment.
CUDA and F# are two trailblazing yet unrelated technologies. F# is a uniquely productive language to solve complex problems in a clear and concise way. On the other hand CUDA is a platform for parallel high performance computing on GPUs. Our presentation shows how to wed the two technologies F# and GPUs with the help of Alea. CUDA, a new framework and compiler infrastructure to develop GPU accelerated applications with F# on .NET. Alea.cuBase builds upon the LLVM compiler toolkit and the new NVIDIA PTX backend of CUDA 5. With Alea.cuBase you effectively write F# code, which generates CUDA programs dynamically at runtime, fully integrated into .NET. This approach opens up new dimensions for GPU programming, for example to develop non-trivial domain specific languages. Another interesting application is server side compilation of GPU kernels and GPU cloud computing. To give you a feeling of the new capabilities, we complement our presentation with several live coding examples.
Software companies use frameworks such as .NET to target multiple platforms from desktops to mobile phones with a single code base in order to reduce costs by leveraging existing libraries and to cope with changing trends. While developers can easily write scalable parallel code for multi-core CPUs on .NET, they face a bigger challenge using GPUs to tackle compute intensive tasks. Alea GPU closes this gap by bringing GPU computing directly into the .NET ecosystem. In this hands-on webinar we show how you can write cross platform GPU accelerated .NET applications in any .NET language much easier than ever before. To follow the examples during the webinar, prepare your computer with Alea GPU and a free community license. Setup details can be found at http://bit.ly/1HL35a3. For more information, read Daniel's blog on Parallel Forall http://bit.ly/1Gu62Q3.
Learn from industry experts how new generation GPU accelerated solutions for derivative pricing, hedging, and risk management can be built more efficiently with modern technology and functional programming languages like F# on .NET or Scala on the Java VM. As a concrete example we report from a large derivative pricing project developed in F# on .NET. We will introduce the key design concepts and parallelization strategies, which lead to an efficient and transparent GPU acceleration. Several examples will illustrate the benefit of the functional as compared to the classical object oriented approach.