System was built in 2003 - 8 years ago.
Assume moore's law of doubles every 2 years - expectation of 16x more powerful
Actual increase in performance - 22x in 1/4th the size
Given that it's then a quarter of the size of system X, that's an amazing increase in peak performance.
There's only one problem - that speed increase appears to owe a lot to the use of GPGPU. As I understand it, whilst research into GPGPU for HPC* is a hot area at the moment, the scale of the actual benefits it offers is still a matter of debate (especially when considering costs and power consumption).
From my perspective, the biggest limitation in using GPUs for more general purpose computations is the communication latency. I published a paper that came to that conclusion: http://people.cs.vt.edu/~scschnei/papers/debs2010.pdf
In short, parallelism is not enough to get benefit from using GPUs. You need parallelism and data reuse.
System was built in 2003 - 8 years ago. Assume moore's law of doubles every 2 years - expectation of 16x more powerful Actual increase in performance - 22x in 1/4th the size
Given that it's then a quarter of the size of system X, that's an amazing increase in peak performance.
There's only one problem - that speed increase appears to owe a lot to the use of GPGPU. As I understand it, whilst research into GPGPU for HPC* is a hot area at the moment, the scale of the actual benefits it offers is still a matter of debate (especially when considering costs and power consumption).