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Mary Jo Foley, editor of ZDNet’s All About Microsoft blog talks about /Build and the new leadership at the New Microsoft. All About Microsoft MJF’s Interview of Scott Guthrie MJF’s Interview of Terry Myerson Cortana Build Announcements Project N Listen … Continue reading → For the complete article and hyperlinks, please visit my blog at

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Starting with our experiments with integrating General Purpose GPU programming into F# at Microsoft Research, I've been watching the evolution of F# as a GPGPU programming platform. Over time, GPGPU programming with F# has become both more professionalized, and much more broad spectrum (including CUDA, OpenCL and other options). Some latest developments in this area have been: There were two talks on F#/GPU/Finance at the NVIDIA GTC Conference covering both how F#/GPU programming is really used in real, large-scale financial applications, and how F# enables the design of a language-integrated compiler targeting LLVM:
  GPU Applications for Modern Large Scale
Asset Management   (Daniel Egloff)
  How to Design a Language Integrated Compiler with LLVM (Xiang Zhang, Aaron Brewbaker) 
  Alea.cuBase 1.2.680 has been released – congratulations to the team from QuantAlea!  Alea.cuBase is available on NuGet.
  QuantAlea have released a first version of CUDALab, demonstrating live documentation for CUDA kernels written using F# scripts and Alea.cuBase.
  Vulpes is a Deep Belief Net written in F#, and using Alea.cuBase to access the GPU. The source for Vulpes is now available on GitHub.
  FSCL (the F# to OpenCL compiler) has matured considerably.  FSCL increases the abstraction over OpenCL programming on...(Read whole news on source site)

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Corax is a research project that we have, to see how we can build a full text search library on top of Voron. Along the way, we take the chance to find out how Lucene does things, and what we can do better. Pretty much from the get go, Corax is likely to use more disk space than Lucene, probably significantly so. I would be happy if we could get a merely 50% increase over Lucene The reason that this is the case is that Lucene goes to great length to save disk space. From storing all integers in
variable length format, to prefix compression to implicitly referencing data in other files. For example, you can see that when you try reading term positions: TermPositions are ordered by term (the term is implicit, from the .tis file). Positions entries are ordered by increasing document number (the document number is implicit from the .frq file). The downside of saving every little bit is that it is a lot more complex to read the data, requiring multiple caches and complex code path to actually get it properly. It make a lot of sense, when Lucene was created, disk space...(Read whole news on source site)

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Information Sharing code across platforms - Immo Landwerth discusses the two options both dramatically improving following announcements at Build, to producing cross platform applications with shared code Migrating from NHibernate to Entity Framework - Jimmy Bogard discusses a recent migration from NHibernate to Entity Framework, discussing the history of the two projects, the equivalences and equivalents between [...]



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