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Jeffrey Richter has completed CLR via C#, Third Edition and the book is at the printer! We’ll post chapter excerpts when the book is available in a couple of weeks. Here is Jeffrey describing the book in his Introduction:

Just finishing off the chapter on bit syntax and pattern matching over bit strings for our Erlang book. We wanted to put in a realistic example, and chose a TCP segment as described  here. It's amazing how expressive the notation can be, and we get a definition which pretty much mirrors the diagram and explanation in the link above: nothing like doing it for yourself to convince you that it works.

As many long time readers know, I am an enthusiast of the F# programming language.  I make no apologies for the fact that, if you are developing software on the .NET platform, F# is one of the better choices you can make for numerous reasons.  It is one of the reasons I proudly contributed as a co-author to the book, Professional F# 2.0, which is being published by Wrox in October. Some of the oft cited benefits of F# are that, to distill them quickly, it is good at doing intensely mathematical operations, it is built for parallelism, and it is good at helping define domain specific languages.  Those benefits are so often cited by speakers on the F# speaker circuit that they pretty much seem cliche to me at this point (note, yours truly is proud to call himself a member of said circuit, and often gives this talk!)  As great as these features are, there are a couple features, that in my more mundane F# experiences, seem to stand out as things that “save my ass”, for lack of a better phrase, more often than not.

The first textbook of its kind, Programming Massively Parallel Processors: A Hands-on Approach launches today, authored by Dr. David B. Kirk, NVIDIA Fellow and former chief scientist, and Dr. Wen-mei Hwu, who serves at the University of Illinois at Urbana-Champaign as Chair of Electrical and Computer Engineering in the Coordinated Science Laboratory, co-director of the Universal Parallel Computing Research Center and principal investigator of the CUDA Center of Excellence. The textbook, which is 256 pages, is the first aimed at teaching advanced students and professionals the basic concepts of parallel programming and GPU architectures. Published by Morgan Kaufmann, it explores various techniques for constructing parallel programs and reviews numerous case studies. With conventional CPU-based computing no longer scaling in performance and the world’s computational challenges increasing in complexity, the need for massively parallel processing has never been greater. GPUs have hundreds of cores capable of delivering transformative performance increases across a wide range of computational challenges. The rise of these multi-core architectures has raised the need to teach advanced programmers a new and essential skill: how to program massively parallel processors.