Greetings from the SC07 Conference in Reno, NV! The show kicks off later today, and is shaping up to be pretty exciting.
One thing we are psyched about is our collaboration with Cray, where we are working together to enable Star-P to run on Cray's XT4 supercomputers. (Cray announced this earlier this morning.) The cool thing here is that it enables a whole new way to run applications on a Cray system, and makes Cray an extension of the scientist's desktop. Imagine coding an algorithm in a desktop tool such as Python, MATLAB®, R, and others, and then running the code - with potentially enormous data sets - on a Cray system, without coding a line of C++, Fortran, and MPI.
We got some work ahead of us to deliver this to early adopters in Q1 of 2008, but already have some pieces of Star-P's port to Cray working. One thing that makes it a pretty reasonable effort is that it's built on standard x86-64 chipset, running Linux.
Initial Porting Results
As a proof of concept, we tweaked a couple things in Star-P and compiled it for the Cray, and ran a couple toy problems on (perhaps the world's smallest) Cray supercomputer :) - a 32-core XT4. The neat thing here is that it worked, and scaled nicely. For this test, we took 64 matrices of 3 different sizes (1000 x 1000, 1500 x 1500, and 2000 x 2000), and found the 2 most correlated vectors in each matrix, all done from Python (one of the very high-level languages Star-P supports).
We ran each problem on 1, 2, 4, 8, 16, and 32 processors of the XT4 system. Looks like pretty good scaling for a 1st run. The timing curve for the smallest problem (1000 x 1000 matrix size) starts to lean over slightly at 32 processors, likely due to the communication overhead associated with distributing the relatively small data set across 32 cores.
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