NVIDIA showed a simulation of this collision running on their latest Tesla GPGPU based on the "Kepler" architecture

Starts at around 1:00 on this video with a great explanation of the collision itself.
http://www.youtube.com/watch?v=aByz-mxOXJM&feature=relmfu

Sumit
(NVIDIA employee)

Discrete GPU market is growing. See JPR's analyst reports http://jonpeddie.com/press-releases/details/embedded-graphics-processors-killing-off-igps-no-threat-to-discrete-gpus/

here is the full report http://jonpeddie.com/download/media/slides/An_Analysis_of_the_GPU_Market.pdf

There is a new easier way to program GPUs now using Directives-based compilers.

Idea is that you add some high-level pragmas to your C or Fortran code that a parallelizing compiler
uses to map to the GPU accelerator. Of course, you have to expose parallelism in the code for
the compiler to do a decent job. Example, use more data-parallel data structures. But this is a nice
incremental way to take advantage of the GPU.

Check it out at:
http://www.nvidia.com/object/tesla-2x-4weeks-guaranteed.html?cid=dev

Sumit
NVIDIA - Tesla Group

Differences between GeForce and Tesla for compute are at:
http://www.nvidia.com/object/why-choose-tesla.html

Bottom line, GeForce is great for development, but if you want to build a cluster with GPUs,
you are better off using the commercial grade Tesla GPUs.

Sumit
NVIDIA Tesla Group

Overall, I think Google+ looks more like LinkedIn, but for friends rather than professional contacts. Here are some suggestions for LinkedIn and Google+ to improve Lessons for LinkedIn from Google+

I think this is a big misconception about GPUs. They are good at many applications - not just Linpack.

Take a look at the list of applications ranging from video transcoding to weather forecasting to computational chemistry to physics at:
http://www.nvidia.com/cuda

In fact, the researchers at the Chinese Academy of Sciences just ran one of the fastest scientific simulations using their GPU supercomputer (#2 on the Top500 list):
http://blogs.nvidia.com/2011/06/chinas-investment-in-gpu-supercomputing-begins-to-pay-off-big-time/

There are tons of papers at the Supercomputing conference for real "full" applications in a very diverse range of applications that are accelerated using GPUs.

The top500 site has its own take on highlights:
http://www.top500.org/lists/2011/06/press-release

- The two Chinese systems at No. 2 and No. 4 and the Japanese Tsubame 2.0 system at No. 5 are all using NVIDIA GPUs to accelerate computation, and a total of 19 systems on the list are using GPU technology.
- China keeps increasing its number of systems and is now up to 62, making it clearly the No. 2 country as a user of HPC, ahead of Germany, UK, Japan and France.
- Intel continues to provide the processors for the largest share (77.4 percent) of TOP500 systems. Intel’s Westmere processors increased their presence in the list strongly with 169 systems, compared with 56 in the last list.
- Quad-core processors are used in 46.2 percent of the systems, while already 42.4 percent of the systems use processors with six or more cores.
- Cray defended the No. 2 spot in market share by total against Fujitsu, but IBM stays well ahead of either. Cray’s XT system series remains very popular for big research customers, with three systems in the TOP 10 (one new and two previously listed).

In my opinion, the newest & most important trend in high performance computing is the advent of accelerators like GPUs.

This is an awesome development - Microsoft adding support for GPU computing in their mainstream tools and C++.

Today, CUDA C++ already provides a full C++ implementation on NVIDIA's GPUs:
http://developer.nvidia.com/cuda-downloads

And the Thrust template library provides a set of data structures and functions for GPUs (similar in spirit to STL):
http://code.google.com/p/thrust/

- biased NVIDIA employee

NASA is also using GPUs -- looks for climate / atmospheric modeling.

So is the NNSA (National Nuclear Security Administration).

The Gordon Bell Prize give at the Supercomputing Conference is effectively the yearly Nobel Prize for computing.

Wikipedia
http://en.wikipedia.org/wiki/Gordon_Bell_Prize

ACM website:
http://awards.acm.org/bell/

There are tons of other CUDA accelerated numerical packages besides Matlab -- Mathematica, LabView, plugins / wrappers / libraries for Python, R, IDL. Some of these are linked from NVIDIA's website
http://www.nvidia.com/object/numerical-packages.html

Others from
http://www.nvidia.com/object/data_mining_analytics_database.html

3 of the Top 5 supercomputers are already using NVIDIA GPUs:
NVIDIA press release

Bill Dally outlined NVIDIA's plans for Exascale computing at Supercomputing in Nov 2010:
Bill Dally Keynote

5 of Top 10 most green supercomputers use GPUs:
Green 500 List

Each GPU is very high performance and so high power. Performance / watt is what counts and
here GPUs beat CPUs by 4 to 5 times. This is why so many of the new supercomputers are using
GPUs / heterogenous computing.