Taming GPU threads with F# and Alea.GPU

Writing GPU kernel code which optimally exploits parallelism and the GPU architecture is the most challenging and time-consuming aspect of GPU software development. Programmers have to identify algorithms suitable for parallelization and while implementing them reason about deadlocks, synchronization, race conditions, shared memory layout, plurality of state, granularity, throughput, latency and memory bottlenecks. This means that new languages with professional tooling which increase the productivity of GPU software development, whilst retaining the full flexibility of the underlying GPU programming model CUDA or OpenCL, are of tremendous value. In this talk we introduce the upcoming version 2 of Alea.GPU, a high productivity GPU development tool chain for .NET. We show how GPU scripting, dynamic compilation and unique features of the F# language can be leveraged to reduce the development effort for cross platform GPU accelerated applications. Finally we look at our new reactive dataflow model, which simplifies GPU computing further.

Speaker Details

Daniel Egloff is partner at InCube and founder of QuantAlea, a software company specialized in quantitative methods and high performance computing for the financial industry. Over the past years he has become a well-known expert in GPU computing and parallel algorithms. He successfully applied GPUs in productive systems for derivative pricing, risk calculations and statistical analysis. Before setting up QuantAlea he spent more than fifteen years in the financial service industry, working in derivative pricing, risk management, and high performance computing. He studied mathematics, theoretical physics and computer science at the University of Zurich and the ETH Zurich, and has a PhD in mathematics from the University of Fribourg, Switzerland.

Date:
Speakers:
Daniel Egloff
Affiliation:
QuantAlea AG
    • Portrait of Jeff Running

      Jeff Running