Selon Ivan <[email protected]>: I've commited new API that adds ReadRaster() and WriteRaster() methods that use the regular Java arrays (byte[], short[], int[], float[], double[]). See http://gdal.org/java
On my PC, http://trac.osgeo.org/gdal/browser/trunk/gdal/swig/java/apps/GDALTestIO.java runs in about 20.3s for the ReadRaster()/WriteRaster() case, and in about 24.7s for the ReadRaster_Direct()/WriteRaster_Direct() case. Not a big advantage (which tends to not any advantage at all when run with the -server flag, as both run in about 21.3 s !), but regular Java arrays are a bit easier to use than ByteBuffer (especially that with Sun JVM 1.6, the array() method on ByteBuffer is not implemented). > Even, > > You are right. The point is how to take full advantage of the GDAL Java API > choosing the right > approach to deal with the raster buffer on the client side. > > Best regards, > > Ivan > > Even Rouault wrote: > > Selon Ivan <[email protected]>: > > > > Ivan, > > > > I'm not sure what you are really measuring if you compare a C++ code versus > its > > translation to Java code. I think it just reflects the known slowdown of > Java > > when doing intensive computations in comparison to native code. The 0.2 > second > > difference between the regular array version and the ByteBuffer one is the > > interesting result, not the 1.2/1.0 second difference between C++ and Java. > > > >> Caio Simone, > >> > >> I just downloaded imageio-ext to check how it does that but it looks like > I > >> don't need to do that now, I can take you report instead. Thank you very > >> much. I will take a look on array pinning for a start. > >> > >> I translated the GDAL Proximity [1] code to Java and I timed both of then > >> with the same input, a 1024x1024 byte image with just one pixel as feature > at > >> the center of the image. > >> > >> It took 0.3 seconds in C++ and 1.5 seconds in Java! > >> > >> I then translated the buffers to regular arrays and it went down a little > >> bit, 1.3 seconds. > >> > >> It is still a big disadvantage. I believe that the buffer-to-buffer > >> translation is the guilt time waster in that case. > >> > >> [1] http://trac.osgeo.org/gdal/browser/trunk/gdal/alg/gdalproximity.cpp > >> > >> My best regards, > >> > >> Ivan > >> > >>> -------Original Message------- > >>> From: Simone Giannecchini <[email protected]> > >>> Subject: Re: [gdal-dev] Re: JAVA API - Performance > >>> Sent: Nov 10 '09 12:36 > >>> > >>> Ciao Even, > >>> just wanted to add my 2 cents. > >>> > >>> As you know for the imageio-ext project we have been using the > >>> GDAL-JNI bindings (actually a modified version of them) for a while in > >>> order to allow Java users to leverage on GDAL using the ImageIO > >>> framework which standard in Java. > >>> This way we also enabled GeoTools and GeoServer to use GDAL as a > >> datasource. > >>> In the past I have done quite some performance tests to add some > >>> new/different methods to them and I can summarise our findings as > >>> follows: > >>> > >>> - DirectByteBuffer vs regular arrays - > >>> DBB is expensive to allocate but prevent the VM from performing copies > >>> when having to move data to and from java and native code since they > >>> live on the native space not on the java heap; On the other side the > >>> regular arrays are fast to allocate but they are "usually" copied when > >>> moved across from/to java and native code since the JVM cannot leave > >>> the native code mess with the java heap space since the garbage > >>> collector would not be very happy about that. I said "usually" since > >>> there is a technique called array pinning that we can suggest the JVM > >>> to use to avoid the copy of regular array; however this mechanism is > >>> not guaranteed to be implemented and/or to work on each call (same > >>> reason as above, GC is not happy about this technique). > >>> > >>> If you can pool the DBB and/or use a few large DBB, where the cost of > >>> the copy would overcome the cost of its creation then DBB are much > >>> better than regular arrays. As an instance I noticed that using when > >>> reading striped tiff files regular arrays where faster, but as the > >>> tile size increases (and therefore the cost of a copy overcomes the > >>> cost of a DBB creation) the DBB performs much better > >>> > >>> - DirectByteBuffer and the impact on some JVM - > >>> Now in the past we decided to stick with DBB and give > >>> GeoServer/GeoTools users the capability to retile data on the fly. > >>> However lately, during the WMS performance shootout we noticed on some > >>> linux machines JVm soldi crashed, not nice (means restarting the > >>> GeoServer!!!). > >>> We investigated a bit in depth and the problem was that somehow the > >>> JVM was failing to allocate some internal images during the rendering > >>> process and then dying with a NullPointerException (apparently the SUN > >>> Java2D engineers did not use to check for out of memory errors in the > >>> java native space). Well, what happens is that if you use too much of > >>> the Java native space for your own objects, it is likely that the JVM > >>> itself will start to malfunction (you can find articles on the web on > >>> the memory model of a Java process, I don't think I am good enough to > >>> explain it ) since it cannot allocate its own objects. > >>> > >>> In the end we decide to leave DBB and go back to regular arrays with > >>> array pinning. This ensured us robustness and we did not see much > >>> performance degradation (which means that array pinning in the end > >>> works). This has been implemented by modifying the SWIG bindings for > >>> GDAL in order to use a byte array instead of a DBB and then use > >>> ByteArray utils to convert between different native type (short, int, > >>> etc..). > >>> > >>> - Conclusion - > >>> We might want to spend some time in the mid term to contribute some of > >>> this work back (or probably provide funding), but anyway, it would be > >>> great to have the capability to switch between DBB and regular arrays > >>> since both have flaws. > >>> However atm if I were asked I would say to go with regular arrays as > >>> we do in the imageio-ext project. > >>> > >>> Ciao, > >>> Simone. > >>> ------------------------------------------------------- > >>> Ing. Simone Giannecchini > >>> GeoSolutions S.A.S. > >>> Founder - Software Engineer > >>> Via Carignoni 51 > >>> 55041 Camaiore (LU) > >>> Italy > >>> > >>> phone: +39 0584983027 > >>> fax: +39 0584983027 > >>> mob: +39 333 8128928 > >>> > >>> > >>> http://www.geo-solutions.it > >>> http://geo-solutions.blogspot.com/ > >>> http://simboss.blogspot.com/ > >>> http://www.linkedin.com/in/simonegiannecchini > >>> > >>> ------------------------------------------------------- > >>> > >>> > >>> > >>> On Tue, Nov 10, 2009 at 12:00 PM, Even Rouault > >>> <[email protected]> wrote: > >>> > Selon Ivan <[email protected]>: > >>> > > >>> > Ivan, > >>> > > >>> > thanks for your testing (CC'ing the list as it is of general > interest). > >>> > Actually, I also read on some sites that using ByteBuffer object > versus > >> regular > >>> > Java arrays is not always a win. Plus the fact that we must use a > direct > >> buffer > >>> > that has an extra allocation cost according to the Javadoc. So > >> ByteBuffer might > >>> > be interesting if you just want to pass big arrays between native > code, > >> for > >>> > example if you read an array from a dataset and then write it to > another > >> one > >>> > without accessing it from the Java side. When you mention that > accessing > >> through > >>> > the byte[] array was faster, did you get it with the array() method > >> instead ? > >>> > I'm wondering what the performance overhead of this call is. > >>> > > >>> > As ByteBuffer is not at all a requirement for the interface with the > >> native > >>> > code, it would be technically possible to add an alternative API that > >> would use > >>> > the regular Java array types. > >>> > > >>> > Would you mind opening an enhancement ticket about that ? Thanks > >>> > > >>> > Even > >>> > > >>> >> Even, > >>> >> > >>> >> I did some test with the GDAL Java API and some simple raster > >> operations > >>> >> like the GDAL Proximity algorthm and I noticed that the performance > >> while > >>> >> accessing pixels with <type>Buffer.get(i), <type>Buffer.put(i,value) > is > >> not > >>> >> as good as if you copy then to (or from) a "regular" array, like > >> float[], > >>> >> double[], integer[] and byte[]. > >>> >> > >>> >> The reason for that is obvious, get() and put() are funtion calls and > >>> >> contains a lot of code for range check. > >>> >> > >>> >> If I understand it correctly, ByteBuffer is the ideal or maybe the > only > >>> >> way to get access to Buffers from C libraries thought a Java wrapper. > >> But > >>> >> do you it would be possible to incapsulate the buffer conversion at > the > >>> >> wrapper code so that users would be able to read and write direct to > >>> >> regular Java arrays? > >>> >> > >>> >> Just a suggestion, > >>> >> > >>> >> Ivan > >>> >> > >>> > > >>> > > >>> > _______________________________________________ > >>> > gdal-dev mailing list > >>> > [email protected] > >>> > http://lists.osgeo.org/mailman/listinfo/gdal-dev > >>> > > >>> > > > > > > > > _______________________________________________ gdal-dev mailing list [email protected] http://lists.osgeo.org/mailman/listinfo/gdal-dev
