BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Talks.cam//talks.cam.ac.uk//
X-WR-CALNAME:Talks.cam
BEGIN:VEVENT
SUMMARY:Taming GPU threads with F# and Alea.GPU  - Dr Daniel Egloff\, Quan
 tAlea AG
DTSTART:20141103T140000Z
DTEND:20141103T150000Z
UID:TALK55876@talks.cam.ac.uk
CONTACT:Microsoft Research Cambridge Talks Admins
DESCRIPTION:Writing GPU kernel code which optimally exploits parallelism a
 nd the GPU architecture is the most challenging and time-consuming aspect 
 of GPU software development. Programmers have to identify algorithms suita
 ble for parallelization and while implementing them reason about deadlocks
 \, synchronization\, race conditions\, shared memory layout\, plurality of
  state\, granularity\, throughput\, latency and memory bottlenecks.\nThis 
 means that new languages with professional tooling which increase the prod
 uctivity of GPU software development\, whilst retaining the full flexibili
 ty of the underlying GPU programming model CUDA or OpenCL\, are of tremend
 ous value. \nIn 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 ca
 n be leveraged to reduce the development effort for cross platform GPU acc
 elerated applications. Finally we look at our new reactive dataflow model\
 , which simplifies GPU computing further. \n
LOCATION:Auditorium\, Microsoft Research Ltd\, 21 Station Road\, Cambridge
 \, CB1 2FB
END:VEVENT
END:VCALENDAR