Official alpha release of X10/CUDA

Since many X10 community members have been asking whether X10 will run on
the GPU, as part of the 2.1.0 X10 release we are also releasing our CUDA
backend.
This enables fragments of X10 programs to run on CUDA-capable GPUs,
allowing significant speedups.  Here are some supported features:

* Write programs that run on host and GPU using a single language: X10
* Use existing X10 constructs (async, finish, etc) to represent many CUDA
  idioms
* Utilize more than 1 GPU
* Use a network of hosts, each with 0 or more GPUs
* Implicitly copy data from host to GPU
* Static prevention of errors due to accessing GPU memory on the host or
  vice versa
* 3 small programs to try out in samples/CUDA
* Automatic selection of blocks/threads based on particulars of hardware
and kernel

Currently using CUDA is only possible with the C++ backend and has only
been tested on x86/x64 Linux with the latest CUDA drivers.  X10 must be
specially built from source to use CUDA devices.  Please see the X10
2.1.0 CUDA page on the X10 website for instructions on how to try it out.

http://x10.codehaus.org/X10+2.1+CUDA

Questions and comments about X10/CUDA are welcome on the x10-users mailing
list.

Note that development of the CUDA release is ongoing in the svn
repository.  If you are a member of the community and your appetite has
been whet by the CUDA support in the 2.1.0 release, consider going further
and checking out svn.  We have already strengthened the support for CUDA
shared memory, and added access to the CUDA __syncthreads() construct via
the X10 clock syntax.  We intend to add more support for CUDA features
over the coming weeks.

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