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 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 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 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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