Published on 8th November 2009 by
Originally Posted by mi1ezI'll have to read this without a hangover me thinks!
Originally Posted by Redsnake77What on earth is that red branch tree thing in the Barcelona Supercomputing Centre picture?
Originally Posted by anoC'mon guys. Why didn't this go to an editor before posting? 4 typos in the first 3 paragraphs makes it awkward to read.
threatening to taking over the world
Rather than writing a instructions that are
"thread' - single or double quotes, choose your style and stick to it.
This requires further steps need to be taken
Originally Posted by BrightCandlevery interesting post
Originally Posted by BrightCandle
2) A set of mostly parallel problems where something like 95% of the algorithm will run in parallel, but where the rest must be executed in a lock or atomically. But that means that 5% of the algorithms time has to run in a single thread and assuming an infinite number of cores the best speed up we could thus get is around 20x. 1000 cores wont make any performance difference to these problems, they are dominated by the serial part of the algorithm once you have enough cores. Early on we'll see the problem, 20 CPUs on this problem will only provide around a 10x speedup. Many of the programs we're seeing running on multiple cores today fall into this category,
Originally Posted by wuyanxuwhy is this so hard? all embedded programmers such as all Electronic engineers in ECS, Southampton uni understands and knows how to implement concurrency on a hardware level.
software programmers are too used to sequentially writing their code. emply a couple embedded systems engineer they'd tell you how to take advantage of parallelism available in any embedded chip.
Originally Posted by karx11erxWhat's new about locking critical code areas for concurrent CPU access? OpenMP btw does the same with the omp atomic (single statement) and omp criticial (multiple statement) pragmas. That's parallel programming basics.
BrightCandle, your post sounds all nice and dandy in theory, but there are a lot of real-word problems that lend themselves very well to parallelization, and often they are very simple to implement as parallel code. No big deal. You should be careful not to throw out the baby with the bath water.
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