LHC@home

@GreatInca

That's not correct.

Typical (modern) Power Supplies operate at approx. 70-85% efficiency (where >80% is very good). In the past, these efficiently levels were known to be only reached unter ~75-80% load conditions, something which has recently changed to the better.

A 300W PSU can never pull more than ~300W from the Plug (exact specs and safety circuits usually cut in slightly below that, depending on which supply lines hit their individual Ampere limit first).
It is a common error to believe e.g. a 500W PSU would actually draw 500W of power all the time. In fact, a good 500W PSU can operate at a sustained 40W with a power-saving Hardware setup attached to it. The attached Hardware is what defines the power consumption (minus a factor of the PSU's efficiency).

I'm running modern Systems with way overpowered PSU's (300-420W, since it's sheer impossible to get e.g. a 150W ATX 2.0 PSU nowadays), and they're effectively using 75W to 115W in total (depending on Hardware Setup) from the Plug (measured).

A good Laptop with crunching in mind should not use more than 45-50W unter full CPU load.
Something in the figures of 100W or more are power-wasting Desktop replacement Notebooks and terribly inefficient when crunching comes into play; 180W for any Laptop would be completely unacceptable. (A well setup crunching Desktop uses far less than that and offers higher performance at the same time)

A Pentium4 is about the worst-case scenario (highly inefficient and by now comparably slow when compared to other architectures)... The only CPU to be less efficient than the P4 was the P4 based Celeron (which marks the rock bottom of poor efficiency in the whole x86 field).

It is important to MaxCache LHC becuase it takes only 18-30 hours for them to run out of work once they put it up (looks like they put up 80000-150000 WUs at a time).

What's important is getting the work done. As I write this, GreatInca, one of your computers still has eight results left to run, while my computers have been out of work for two or three days. Had you not filled your cache to excess the eight results could have been assigned to other computers and the work completed days ago. Spread this across thousands of users and the tail of unreturned results drags out for days.

Each study generally requires analysis of the previous study to determine appropriate parameters. If each study is completed more quickly the next one can be released sooner. The message, therefore is:

Keep your cache as small as possible.