Dr. Daniel Egloff will hold a talk at the Code Mesh London on 5 November 2014.
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.
New languages and tools to increase the productivity in GPU software development, whilst at the same time retaining the full flexibility of the underlying programming models such as CUDA or OpenCL, are thus of tremendous value.
In this talk we give an introduction to our high productivity GPU development tool chain Alea based on F#. We show how GPU scripting, dynamic compilation and unique features of the F# language can be leveraged to reduce the development time of reusable libraries of parallel primitives and core numerical algorithms.
For more information visit:
Code Mesh London 2014