Yes, this is exactly what I meant. If the data are amenable (which was 
addressed in the previous discussion with reference to diffraction images) and 
there is a suitable lossless compression/expansion algorithm, then on most 
modern computers it is faster to read the compressed data from disk and expand 
it in RAM, rather than directly read the uncompressed image from a magnetic 
plate. Of course this depends on all sorts of factors such as the speed of the 
disk, the compression ratio, the CPU(s) clock speed, if the decompression can 
be done in parallel, how much calculation the decompression requires, and so on.

Bill's example is nice because the compression is transparent, so no extra work 
needs to be done by developers. However, this is one for Macs only. I'd like to 
know whether integration runs faster using CBF images with the decompression 
overhead of CBFlib compared with reading the same data in uncompressed form on 
"standard" hardware (whatever that means).

Cheers
David

-----Original Message-----
From: CCP4 bulletin board [mailto:ccp...@jiscmail.ac.uk] On Behalf Of Andrew 
Purkiss-Trew
Sent: 18 September 2009 21:52
To: CCP4BB@JISCMAIL.AC.UK
Subject: Re: [ccp4bb] I compressed my images by ~ a factor of two, and they 
load and process in mosflm faster

The current bottleneck with file systems is the speed of getting data on or off 
the magnetic surface. So filesystem compression helps, as less data needs to be 
physically written or read per image. The CPU time spent compressing the data 
is less than the time saved in writing less data to the surface.

I would be interested to see if the speed up is the same with a solid state 
drive, as there is near 'random access' here, unlike with a magnetic drive 
where the seek time is one of the bottlenecks. For example, mechanical hard 
drives are limited to about 130MB/s, whereas SSDs can already manage 200MB/s 
(faster than a first generation SATA interface at 150MB/s can cope with and one 
of the drivers behind the 2nd (300MB/s) and 3rd generation (600MB/s) SATA 
intefaces). The large size of our image files should make them ideal for use 
with SSDs.


Quoting "James Holton" <jmhol...@lbl.gov>:

> I think it important to point out that despite the subject line, Dr.  
> Scott's statement was:
> "I think they process a bit faster too"
> Strangely enough, this has not convinced me to re-format my RAID array 
> with an new file system nor re-write all my software to support yet 
> another new file format.  I guess I am just lazy that way.  Has anyone 
> measured the speed increase?  Have macs become I/O-bound again? In any 
> case, I think it is important to remember that there are good reasons 
> for leaving image file formats uncompressed.  Probably the most 
> important is the activation barrier to new authors writing new 
> programs that read them.  "fread()" is one thing, but finding the 
> third-party code for a particular compression algorithm, navigating a 
> CVS repository and linking to a library are quite another!  This is 
> actually quite a leap for those
> of us who never had any formal training in computer science.   
> Personally, I still haven't figured out how to read pck images, as  
> it is much easier to write "jiffy" programs for uncompressed data.   
> For example, if all you want to do is extract a group of pixels (such 
> as a spot), then you have to decompress the whole image!  In computer 
> speak: fseek() is rendered useless by compression.  This could be why 
> Mar opted not to use the pck compression for their newer CCD-based 
> detectors?
>
> That said, compressed file systems do appear particularly attractive 
> if space is limiting.  Apparently HFS can do it, but what about other 
> operating systems?  Does anyone have experience with a Linux file 
> system that both supports compression and doesn't get corrupted 
> easily?
>
> -James Holton
> MAD Scientist
>
>
> Graeme Winter wrote:
>> Hi David,
>>
>> If the data compression is carefully chosen you are right: lossless 
>> jpeg2000 compression on diffraction images works very well, but is a 
>> spot slow. The CBF compression using the byte offset method is a 
>> little less good at compression put massively faster... as you point 
>> out, this is the one used in the pilatus images. I recall that the 
>> .pck format used for the MAR image plates had the same property - it 
>> was quicker to read in a compressed image that the raw equivalent.
>>
>> So... once everyone is using the CBF standard for their images, with 
>> native lossless compression, it'll save a fair amount in disk space 
>> (=£/$), make life easier for people and - perhaps most importantly - 
>> save a lot of data transfer time.
>>
>> Now the funny thing with this is that if we compress the images 
>> before we store them, the compression implemented in the file system 
>> will be less effective... oh well, can't win em all...
>>
>> Cheers,
>>
>> Graeme
>>
>>
>>
>> 2009/9/18 Waterman, David (DLSLtd,RAL,DIA) <david.water...@diamond.ac.uk>:
>>
>>> Just to comment on this, my friend in the computer game industry 
>>> insists that compression begets speed in almost all data handling 
>>> situations.
>>> This will be worth bearing in mind as we start to have more 
>>> fine-sliced Pilatus 6M (or similar) datasets to deal with.
>>>
>>> Cheers,
>>> David.
>>>
>>> -----Original Message-----
>>> From: CCP4 bulletin board [mailto:ccp...@jiscmail.ac.uk] On Behalf 
>>> Of William G. Scott
>>> Sent: 17 September 2009 22:48
>>> To: CCP4BB@JISCMAIL.AC.UK
>>> Subject: [ccp4bb] I compressed my images by ~ a factor of two, and 
>>> they load and process in mosflm faster
>>>
>>> If you have OS X 10.6, this will impress your friends and save you 
>>> some disk space:
>>>
>>> % du -h -d 1 mydata
>>> 3.5G    mydata
>>>
>>> mv mydata mydata.1
>>>
>>> sudo ditto --hfsCompression mydata.1  mydata rm -rf mydata.1
>>>
>>> % du -h -d 1 mydata
>>> 1.8G    mydata
>>>
>>> This does hfs filesystem compression, so the images are still 
>>> recognized by mosflm, et al.  I think they process a bit faster too, 
>>> because half the information is packed into the resource fork.
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