I suspect this issue has much more to do with the OS, the local file
system, and the network file system and how the 3 interact. It can get
I have written several scrips that use spm_read_vols (including time
series analysis) over network file systems. Work great on a 64bit
linux server reading files using NFSv4 and with the server using XFS
for the local file system.
On Wed, Jul 27, 2011 at 5:08 PM, John Ashburner <[log in to unmask]> wrote:
> I never use spm_read_vols myself. Are the nifti routines any faster?
> tic = X=N.dat(:,:,:,:); toc
> It's unlikely that we're going to re-code this stuff to make it work
> faster on the kind of device we don't have access too.
> Best regards,
> On 27 July 2011 22:00, Welsh, Robert <[log in to unmask]> wrote:
>> Hi authors of SPM8,
>> I've noticed a horrendous performance hit when accessing time-series data
>> when that data sits on a NAS device.
>> Let's call it an SPM8 x NAS interaction.
>> If I read the 4D dataset with spm_read_vols from our 48TB NAS it takes
>> about 250 seconds, however if I read that 4D dataset with some homegrown
>> matlab code to just open the file, skip over the header and read the data
>> in, then it only takes about 2.5 seconds, or 100 times shorter! This is on
>> the exact same file on the exact same NAS device. When I run spm_read_vols
>> through the system profiler of course the time is pretty much evenly split
>> between spm_matrix and spm_slice_vol. The impact on our throughput is
>> high, that is processes are being IO limited when indeed they should not
>> be. In fact for some custom functional connectivity processing I forego
>> the use of the spm_read_vols to get my time-series data.
>> Can I ask the authors to examine writing a different piece of code for
>> reading time-series data that could jump around the spm_slice_vol and
>> spm_matrix given that the data has to already be resliced on disk and thus
>> the application of spm_matrix maybe is redundant, if having statistics
>> performed? Given that NAS devices are becoming more popular it seems that
>> this will be having an impact for others eventually.
>> I've seen this on both linux and mac os x systems running both matlab 2007
>> and matlab 2009.
>> Have others noticed this issue?
>> -Robert Welsh
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