Bulat Ziganshin wrote:
Hello Andrew,
Saturday, April 19, 2008, 7:50:30 PM, you wrote:
The data to be sorted is generated using a trivial LCG PRNG.
if you don't generate new data for each sorting run, this means that
data generation is much slower than sorting. don't forget about ghc
laziness :)
...which is why the unsorted data is written to disk *before* I attempt
to sort it. This is to ensure it's fully evaluated, and give me some
idea how long it takes to write to disk. The [first] sorting stage is
about 10x slower than the initial writing stage - and yet, subsequent
sorting is 10x faster than the initial unsorted write.
Not with -N1.
are you sure? :) afaik, -threaded RTS uses dedicated i/o thread
despite of -N setting (which controls only amount of threads running
*user* code)
Interesting. Without consulting a GHC RTS expert, I guess there's no way
to know.
Certainly I doubt my program is I/O-bounded. It only writes about 11 MB
of data to a text file. I would think converting Word32 -> String is the
slow part.
Again, with -N1, it is *still* only using 1 CPU core.
parallel version is different from sequential one and it process data
in another order.
Yeah, perhaps it improves RAM usage or something...
Fails to explain why the parallel version is faster than the sequential
one (even with no parallelism), or why the sequential algorithm should
slow down with more threads. (Surely the extra threads just sit idle?)
there are management overheads. with multiple worker threads you have
many OS threads which fights for the right to execute single Haskell
thread :))
As I understand it, sparked work is placed into a FIFO queue, and a set
of worker threads poll this queue, remove the first item and begin
executing. There is no "fighting"; once a work item has been picked by a
thread, it runs to completion.
Now, if there are more OS threads than physical CPU cores, they will
fight it out at the OS level who runs first... ;-)
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