Build Stuff'16 Ukraine

When we talk about scaling, we usually mean up (bigger machine) or out (more machines). However, there is another alternative, which is changing our workload in a way that makes it inherently more parallelizable and then taking advantage of specialized hardware that's very good at handling that sort of thing. Most of us have exactly this type of hardware just sitting in our computers, doing very little most of the time. I'm talking, of course, about the GPU. General-purpose computing on the GPU (GPGPU) is no longer the domain of pure academic research. It is being used in real-world applications such as image processing and face recognition, cryptography, big data analysis, and Bitcoin mining. In this session we will examine the available GPGPU frameworks, learn how to integrate C++ AMP and OpenCL into regular .NET and Java applications, how to debug mixed .NET and GPGPU code in Visual Studio, and how to use the Aparapi framework to seamlessly mix regular CPU and GPGPU code in Java. We’ll even look at using GPGPU from server and client-side JavaScript with WebCL. We will discuss the kinds of tasks that can benefit from graphics card processing, how best to parallelize workloads, and what some of the performance trade-offs are.