The Spring 2015 15-minute student presentations were as follows.
        1. 6:00 Concurrent Map / Reduce in Java
        2. 6:20 Java ReentrantReadWriteLock class
        3. 6:40 Multithreading on Android
        4. 7:00 C++0X Multithreading in Assignment 1
        10-minute BREAK 7:20
        5. 7:30 C++0X atomics
        6. 7:50 Search algorithm on CUDA GPU
        7. 8:10 Concurrency in CPython versus Jython
        8. 8:30 Python asyncio package

        1. 6:00 java.nio Channels ("new I/O") & multithreading
        2. 6:20 Java serialization and compression for distributed communication
        3. 6:40 Introducing Scala, with emphasis on functional programming constructs and why they are useful to concurrent programming
        4. 7:00  Actor-based concurrency in Scala
        10-minute BREAK 7:20
        5. 7:30 OpenMP concurrent programming API
        6. 7:50 WordCA Cellular Automaton on GPU/CUDA
        7. 8:10 Neural networks on the Web using parallel processing
        8. 8:30 Hash cracking on GPUs or distributed systems
 
Here is a draft list of topics and some associated resources. All of them require writing at least some amount of extension code of your own. Check with me on how much. Also, *feel free to propose your own topic!*

Benchmarking fair versus unfair queuing in ReentrantLock, ReadWriteLock, and Semaphore on Linux & Solaris for a thread mix that shows the difference. NEW

Benchmarking the effects of thread priorities on Linux & Solaris for a thread mix that shows the difference. NEW

NVIDIA Graphical Processing Units (GPAs) / CUDA software support and applications. WHO?
    There could be several GPU projects. Dumbledore houses a NVIDIA C2070 card as its second graphics card, Luna houses a Tesla K20C that has not yet been used. Note that there is information on the C2070 at that latter link.
    A. I wrote some research grade bidirectional search code on the C2070 in summer 2012, see parson/multip/multip/pennydime/*Cuda* on acad. I have a draft write-up on that work. I would love to see my code verified to work with the current version of CUDA on Dumbledore's C2070, and then benchmarked on Luna's K20C, and then perhaps enhanced to get additional benefits out of the K20C. My Cuda code includes all-GPU solutions that communicate only text IO to the Linux process, and also a few hybrid solutions that split work between the Linux CPUs and the GPU. I also have some partially outdated Cuda books.
    B. A port of the WordCA engine to one of the GPU cards would be very interesting. We'd have to figure out how to refactor the application architecture.
    C. There are many possible projects at the General Purpose Processing on a GPU site and the NVIDIA site, and undoubtedly others.

C++0X / C++11 added support for a memory model, thread-safe library classes and atomics that are mostly a subset of what Java provides.
    My draft paper cited above for Cuda also discusses some C++0X code I wrote and benchmarked with Java in summer 2012. There are subdirectories CPPCoinPuzzle/, ClikeCoinPuzzle/ and GPPCPUEmulatesGPU/ under ~parson/multip/multip/pennydime with C++0X code that may need to be mildly ported to C++11. Rohrbach library has a hard copy of C++ Concurrency in Action, which is the book I used. I tried my benchmarks in January 2015 with the new Linux csit/acad system, and found that C++11 revised and broke the C++0X libraries for atomics. They changed names to make sure no one assumes backward compatibility. The book will be out of date for those issues, but it is largely useful. This talk would include discussion of the C++0X and C++11 enhancements for concurrency, porting my code as necessary to work on C++11, and making extensions that we agree on.

Networked communications & concurrency.
    A. This would basically be taking the socket code that I have supplied for assignments 3 & 4, and replacing it with library classes from java.nio.channels and associated packages in the library. Take a look at:
    http://www.ibm.com/developerworks/java/tutorials/j-nio/j-nio.html
    https://www3.ntu.edu.sg/home/ehchua/programming/java/J5b_IO_advanced.html
    The java.nio packages in http://docs.oracle.com/javase/6/docs/api/index.html
    NIO is supposed to be more efficient for certain classes of IO. Two reasons that it interests me are:
        The IO libraries we are currently using for Socket IO do not support interrupts(). A blocking read() or write() on a Socket will not sense an interrupt. It is necessary for another thread to close()the Socket to get the thread that is blocked to unblock. That is fine if the purpose of the interrupt() is shutdown (our assignment 3), but if an application is using interrupt() for other purposes, then we wouldn't want to close() the Socket in order to get read() or write() to throw an exception. I am pretty certain that java.nio.channels support interruption from another thread. Your code would be to rewrite the basic Socket code in assignments 3&4 to use java.nio.channels  to support interruptible Socket IO, and then give a talk on that to the class. It also appears to support the Select capability for allowing a single thread to block on multiple incoming & outgoing Sockets until one of them is ready to read() or write() as required by the application.
    B. MPI /  on several of our Linux machines (or possibly several Windows machines):
        http://mpitutorial.com/tutorials/mpi-hello-world/
        It may be possible to do this in Java without requiring KUIT to install software. I don't know. I have never used MPI.
    C.  OpenMP is a pragma-based approach for letting the compiler parallelize inner loops in C/C++, similar to the inner loop parallelization we did by hand in assignment 2. It is not intrinsically about networking. I have never used it.
        http://openmp.org/wp/
    D. There could be additional networking and distributed application topics if there is interest.

The Scala language and its library support for Actor concurrency and alternatives. Scala runs on the JVM. In addition to on-line resources, Rohrbach library has some books. I have some loaner books. Scala is installed on one or more of our machines.
 
Other uses of Functional Programming Techniques as they apply to multiprocessing & concurrent programming would be good.

Concurrency in some class of applications or application framework.

Please suggest your own topic.