Funding NASA helps fund the research and development that allows for the possibility of creating that infrastructure we so desperately need up in space in order to do any of it.
I'd argue funding NASA prevents the creation of infrastructure (gun / laser launch, systems of tethers / rotovators) because NASA can afford one-off rocket shots which result in no residual infrastructure, whereas private industry would have to be smarter (to be affordable).
No more external fuel tanks are being manufactured, the rest of the parts chain is shutting down. When the shuttle is gone, America loses manned access to space
There are still enough residual ETs to do some interesting prototyping things, such as a shuttle derived heavy.
The Constellation program sounded like a real soup sandwich. Cancelling it would be a good thing if it paved the way for something done right. But that's not happening
The shuttle was just farting around in LEO.
See SpaceX's first Falcon 9 in the hangar at the cape, at least it's got the possibility of farting around in LEO more cheaply. Diverting money from Constellation to COTS is paving the way.
Here's a round-up of the recent news with links describing that stuff.
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I haven't the foggiest idea how a temperature gradient can cause matter to climb out of a gravity well.
Thermophoresis causes particles in a fluid to move because of a temperature gradient. The similarity parameters (Reynolds / Mach / Knudsen) for a planetesimal in an accretion disk are probably similar to the aerosal particles in air that the wiki article talks about.
To make any statements, you would first need to make some observations
Or you could be a Bayesian, make some assumptions, include a priori info in the analysis (which you should probably do anyway even if you have data); before you get up to check if there is an elephant in the kitchen assigning equal priors to the two hypothesis is a sound maximum entropy sort of method. You can then update your 50/50 state of knowledge after observing zero or many elephants in your kitchen.
"the data comes from an underlying normal distribution"
You mean we often assume the residuals are normal; the data could be any distribution at all, that's why we fit models. There's plenty of parametric stuff you can do with different distributions on the residuals too; Google "R glm Poisson", "R glm binomial", or "R glm family".
You might already know all this, but ever since that black swan book came out there's a bunch of statistical-illiterates running around saying, "the whole world's not normal", without understanding that everybody who understands the world and statistics understands that already too.
Take a problem that is all 64-bit integer math and has a branch every couple hundred instructions and GPUs will do for crap on it.
So would a Cray; supercomputers and GPUs are made for the same sorts of problems (exploiting data parallelism). Now if by 'supercomputer' you mean 'a cluster of commodity hardware', then ok, you've got a point, that heap of cpus will handle branches plenty fast.
That would make the performance the same as for the GPU system.
Really? Care to share any results that support that? I'm quite sure the peak flops you can achieve on the GPU are much higher than the limited SIMD capability of the CPU.
Note that I am being generous here and actually ignoring the program setup time when they need to copy the data to the GPU.
Sure there's communications overhead, but that's true of any parallel processing problem, the trick is to find problems that have a big computation to communication ratio (which happens to be most of computational physics and these tomographic reconstruction problems that TFA mentions as well).
you can get absolutely incredible performance out of off-of-the-shelf GPUs these days.
I had heard this from folks, but didn't really buy it until I read this paper today. They get a speed-up (wall clock) using the GPU even though they have to go to a worse algorithm (Jacobi instead of SSOR). Pretty amazing.
Intel's compiler is actually one of the best optimizing compilers out there (when it doesn't detect an AMD processor and not bother doing the optimizations...).
I'll second that, here's an example from the Octave mailing lists:
To conclude, on my computer, for this test, Octave is approximately as fast as C, gfortran is a little bit faster and ifort is 10 times as fast.
For scientific computing it's tough to beat ifort on intel iron.
It's that to say that some random blogger likely doesn't have the tools to correctly analyze the data
Most of the palaeoclimatology stuff is just curve fitting, they use Matlab (Octave) and R (read the emails), so yeah, some random blogger actually would have the tools that the 'pros' use quite readily available. The silly thing with the paleo stuff is that it is so easily reproducible if you have access to the data, that's why there's a bit of empire building by data hoarding in this community.
What was the the design failure rate for the shuttle when it was in development? How did that turn out?
The costs are not even in the same order of magnitude, you really think SpaceX's Dragon will add additional recurring costs of $950M? The performance improvement from Falcon 9/Dragon to Ares I/Orion is incremental, certainly not enough to justify the price difference (unless you happen to work at MSFC / Boeing / Lock-Mart).
"Engineering without management is art." -- Jeff Johnson
