Thanks Frank, that's what I have been doing until now. I was hoping that
GDAL could do some of the bookkeeping for me.
Bu the multithreaded issue remains important. Anything that could speed
up the warping process, for whatever configuration, would be welcome and
I would be happy to assist.
Jan
On 14-1-2013 15:32, Frank Warmerdam wrote:
Jan,
While it is desirable to resolve the multithreaded write issue in gdal
I don't think it is critical for your use case.
I think your case would be handled more appropriately by multiple
*processes*. You would still run into issues with write consistency
but of a different nature. The easiest approach is to assign
different spatial regions to each worker server and run these linearly.
Best regards,
On Jan 12, 2013 8:05 AM, "Jan Hartmann" <j.l.h.hartm...@uva.nl
<mailto:j.l.h.hartm...@uva.nl>> wrote:
I would be interested in an implementation. I'm preparing a
proposal to georeference the complete cadastral map of the
Netherlands in 1832 at a 10 cm/pixel scale with Cloud facilities.
Gdalwarp is the central piece of software, and distributed
processing capabilities would be very important. Could you please
think about the possibilities, and what kind of funding would be
required, so I can take that in during the next few months?
Jan
On 01/12/2013 04:35 PM, Even Rouault wrote:
ex. convert
multiple datasets to different output datasets in a parallel way.
As Frank underlined, there's currently an issue with the global block cache
regarding write support.
Imagine that you have 2 threads A and B.
Thread A deal with dataset A, and thread B deal with dataset .
Thread A is in the middle of writing some tile/line of dataset A.
Thread B is trying to fill a new entry in the block cache (with new read
data,
or new data to write). But the block cache is full. So the last recently
used
entry must be discarded. If that entry is a dirty block of dataset A, then
it
must be flushed to disk, in the context of thread B, but at that time
thread A
is also writing data... Which might be an issue since drivers are re-entrant
(can be invoked by multiple threads, if each thread deal with different
datasets) not thread-safe.
This specific case here could be fixed in different ways :
A) Making drivers thread-safe (or accessing them through a thread-safe
layer),
that is to say add a dataset level mutex
B) or having a per-dataset block cache instead of a global block cache
C) deal differently with dirty blocks. Only flush them if the operation that
need to discard the dirty block is initiated by an operation on the same
dataset as the dirty block.
Would
those parallel operations not be affected by GDAL caching for bot read and
write.Since the cache is set to a limit. Is Accessing the current used
cache value concurrent safeto increase it/decrease it ?
Hum, I see that GDALGetCacheMax() and GDALSetCacheMax() are not thread safe
currently. We would need to protect them by the raster block mutex, with a
leading call to CPLMutexHolderD(&hRBMutex );
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