"Garbage Collection: Algorithms for Automatic Dynamic Memory Management" (https://www.amazon.com/Garbage-Collection-Algorithms-Automat...) seems to cover GC algorithms up to the CMS.
"The Art of Multiprocessor Programming" (https://www.amazon.com/Art-Multiprocessor-Programming-Mauric...) is for those bored by JCiP. But it's not about threads per se. I imagine nobody cares about the original green threads and I wouldn't expect quality literature on the subject until project Loom goes to prod.
http://www.amazon.com/The-Multiprocessor-Programming-Maurice...
The book is in Java, but C++11 has the required primitives (cross platform compare and set for integral types, a defined memory model) so you could follow allong in C++11.
You're also completely correct about placement new: I am working on a cleaned up version of this class, this was essentially a first pass to get myself more familiar with concurrency in c++0x. What complicates things a bit is that allocators are (much like all else in STL) meant to be used as class template arguments, which makes main separate compilation impossible -- hence the need of an adapter from an STL-style allocator to a pure virtual class. Separate compilation is why I also made a void* version of this initially.
I have a much cleaned up version in the work that will handle more than void . There's an implementation I call it ConcurrentLinkedQueueImpl that handles just void , that is compiled separately -- and there is generic version ConcurrentLinkedQueue that is specialized for void * (ends up just proxying the calls to ConcurrentLinkedQueueImpl), with the generic version (in turn) using ConcurrentLinkedQueue<void *> and placement new to hold any type.
Once again, the version posted there was a rough pass to get myself familiar with 0x concurrency constructs and hazard pointers -- the code is fairly messy.
[1] http://www.amazon.com/Art-Multiprocessor-Programming-Maurice... [2] Everyone should read this book cover to cover -- http://jcip.net/
An example of mutual exclusion, without any atomic operations, taken from the book "The art of multiprocessor programming"[1] is (paraphrased) as follows:
Two threads, A and B, want to access some memory. Each thread has a flag.
When thread A wants to access the shared memory:
Set flag A
Wait for flag B to become unset
Access memory
Unset flag A
Set flag B
While flag A is set {
Unset flag B
Wait for flag A to become unset
Set flag B
}
Access memory
Unset flag B
[1] http://www.amazon.com/Art-Multiprocessor-Programming-Maurice...
For example, In my four year computer science degree, a lot of time was spent on high level languages like Java, C, C++, Haskell, even prolog. A good bit of time was spent on low level details (two computer architecture modules, an "advanced" computer architecture module, an assembly programming module). Some time was spent on computability, complexity and parsing. Of course, we had a number of math modules too, including probability, statistics and linear algebra. A lot of time was spent on object oriented programming, data structures and algorithms. A little bit of time was spent on other topics like network programming, databases, operating systems (we did cover deadlock and some concurrent programming stuff in great detail here) and AI. The rest was split between single (optional!) modules on niche areas: digital signal processing, cryptography, compression, 3d graphics and so on.. including concurrent programming.
We spent so much time learning how to program sequentially, why would anyone even suggest that we should be able to program for multicore systems? Were we given years of training in parallel programming languages, algorithms, data structures and libraries? Nope. Instead the time was spent on sequential programming. Of course its going to be hard to program for multicore!
Heres a car analogy:
Its like spending years learning to drive tractors, expecting that you can then drive formula one cars.
Some problems simply don't make much sense to try and solve with parallel programming. Some problems do, but we're not properly educated to spot these effectively, to know what data structures are appropriate and what algorithms we should use, nevermind things like testing and debugging parallel software.
As an aside, if performance is what we're trying to achieve with multicore programming, then we need to be taught about efficient cache usage too! Misuse of the processor cache kills multicore performance and popular software development principles, like object-oriented programming actually works against the processor cache! Ugh.
There is plenty to help us effectively program for multicore. A short (and very incomplete) list:
- monitor based concurrency (mutexes, semaphores, condition variables + threads); usually not the best option
- Software Transactional Memory; replace manual synchronisation with transactional access to shared memory
- Message passing; multiuple threads/tasks communicating through immutable messages (being immutable means that synchronization concerns are minimized)
- Dataflow languages/libraries where data "flows" through computational nodes and many streams of data can flow through in parallel (each node can be processing at the same time)
- Tools and libraries such as OpenMP, OpenCL and Intel Threading Building Blocks
- Languages with a focus on parallel programming like Erlang and Clojure (I especially like Clojures view on *time*)
- Languages with built-in immutable data structures help make synchronisation less painful. Same goes for atomic data structures.
- Entity Systems[1] are actually surprisingly suited for multicore programming, IMHO
[1] http://t-machine.org/index.php/2007/09/03/entity-systems-are...
[2] http://www.amazon.com/Art-Multiprocessor-Programming-Maurice...
[3] http://www.amazon.com/Intel-Threading-Building-Blocks-Parall...
http://www.amazon.com/Art-Multiprocessor-Programming-Maurice...
Not only does it provide a well-debugged, well-written collection of wait-free data structures; it teaches you to think about concurrency in a structured way. The mutual exclusion chapter doesn't teach you which pthread routines to use, but instead tells you how to implement mutual exclusion, and the trade-offs inherent in, e.g., spin locks vs. queueing locks. A significant bonus is Maurice Herlihy's distinctive prose voice: correct, concise, and funny in that order. It is the best computer book I've read recently, and the one I recommend to all colleagues who are trying to take their concurrency chops up to the next level.
(I took a course from Herlihy in '99, but am otherwise unaffiliated.)
http://book.realworldhaskell.org/
http://www.amazon.com/Programming-in-Haskell-ebook/dp/B001FS...
also: Herlihy , Shavit , Multiprocessor Programming
http://www.amazon.com/Art-Multiprocessor-Programming-Maurice...
http://www.tbray.org/ongoing/What/Technology/Concurrency/
http://www.sauria.com/blog/2009/10/05/the-cambrian-period-of...
http://groups.google.com/group/clojure/browse_thread/thread/...
http://blogs.azulsystems.com/cliff/2008/09/jvm-language-su.h...
http://wiki.jvmlangsummit.com/pdf/36_Click_fastbcs.pdf
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also recommended: Herlihy/Shavit book, blogs by Sutter and Duffy,
http://www.amazon.com/Art-Multiprocessor-Programming-Maurice...
The thing though is most of the thing we (at least I) learned are outdated by at least 10 years. So if you want to learn how things work out in the field I suggest reading about actual implementation. For instance this book is terrific about Linux OS [1], and this is a classic on concurrent programming [2], this one about MySQL [3] and so on.
[1] https://www.amazon.com/Linux-Kernel-Development-Robert-Love/...
[2] https://www.amazon.com/Art-Multiprocessor-Programming-Mauric...
[3] https://www.amazon.in/Understanding-MySQL-Internals-Discover...