Crashes, errors and performance issues¶
Throughout MRtrix3 we try to provide users with meaningful error messages if something does not work or sensible default behaviour cannot be determined. However in some cases, feedback to the user can be unavoidably difficult to interpret, or non-existent in cases where performance is poor but a command does not fail outright; this page is a collection of information of such cases.
Compiler error during build¶
If you encounter an error during the build process that resembles the following:
ERROR: (#/#) [CC] tmp/cmd/command.o /usr/bin/g++-5.0 -c -std=c++11 -pthread -fPIC -I/home/user/mrtrix3/eigen -Wall -O2 -DNDEBUG -Isrc -Icmd -I./lib -Icmd cmd/command.cpp -o release/cmd/command.o failed with output g++-4.8: internal compiler error: Killed (program cc1plus) Please submit a full bug report, with preprocessed source if appropriate. See for instructions.
ERROR: ( 9/498) [CC] tmp/src/directory/header.o g++ -c -std=c++11 -pthread -fPIC -DMRTRIX_WORD64 -DMRTRIX_TIFF_SUPPORT -isystem /usr/include/x86_64-linux-gnu -DEIGEN_FFTW_DEFAULT -Wall -O3 -DNDEBUG -Isrc -I./core -Icmd -isystem /usr/include/eigen3 -DEIGEN_DONT_PARALLELIZE src/directory/header.cpp -o tmp/src/registration/transform/rigid.o_ failed with output cc1plus: out of memory allocating 4064 bytes after a total of 35667968 bytes
This is most typically caused by the compiler running out of RAM. This can be solved either through installing more RAM into your system, or by restricting the number of threads to be used during compilation:
$ NUMBER_OF_PROCESSORS=1 ./build
Commands crashing due to memory requirements¶
Some commands in MRtrix3 have substantial RAM requirements, and can therefore fail even on a relatively modern machine:
tcksift2must store, for every streamline, a list of every fixel traversed, with an associated streamline length through each voxel.
fixelcfestatsmust store a sparse matrix of fixel-fixel connectivity between fixels in the template image.
In both of these cases, the memory requirements increase in proportion to the number of streamlines (directly proportionally in the case of SIFT/SIFT2, less so in the case of Connectivity-based Fixel Enhancement (CFE)). They also depend on the spatial resolution: If the voxel size is halved, the number of unique fixels traversed by each individual streamline will go up by a factor of around 3, with a corresponding increase in RAM usage in SIFT/SIFT2; but the total number of unique fixels increases by up to 8, and hence the total number of possible fixel-fixel connections goes up by a factor of 64! The RAM usage of CFE therefore increases by a substantial amount as the resolution of the template is increased. Unfortunately in both of these cases it is theoretically impossible to reduce the RAM requirements in software any further than has already been done; the information stored is fundamental to the operation of these algorithms.
In both cases, the memory usage can be reduced somewhat by reducing the number of streamlines; this can however be detrimental to the quality of the analysis. Possibly a better solution is to reduce the spatial resolution of the underlying image, reducing the RAM usage without having too much influence on the outcomes of such algorithms.
For SIFT/SIFT2, the subject FOD image can be down-sampled using e.g.:
$ mrresize in.mif out.mif -scale 0.5
Note that it is not necessary to use this down-sampled image for tractography, nor for any other processing; it is simply used for SIFT/SIFT2 to reduce memory usage. Additionally, by performing this down-sampling using MRtrix3 rather than some other software, it will ensure that the down-sampled image is still properly aligned with the full-resolution image in scanner space, regardless of the image header transformation.
For CFE, it is the resolution of the population template image that affects the memory usage; however using higher-resolution images for registration when generating that population template may still be beneficial. Therefore we advocate downsampling the population template image after its generation, and otherwise proceed with Fixel-Based Analysis (FBA) using this down-sampled template image.
Scripts crashing due to storage requirements¶
The Python scripts provided with MRtrix generate their own temporary directory in which to store various data files and image manipulations generated during their operation. In some cases - typically due to use of a temporary RAM-based file system with limited size, and/or a failure to clean up old temporary files - the location where this temporary directory is created may run out of storage space, resulting in the script crashing out.
A few pointers for anybody who encounters this issue:
When these scripts fail to complete due to an error, they will typically not erase the temporary directory, instead allowing the user to investigate the contents of that directory to see what went wrong, potentially fixing any issues and continuing the script from that point. While this behaviour may be useful in this context by retaining the progress the script had made thus far, it also means that these very scripts may be contributing to filling up your storage and thus creating further issues! We recommend that users manually delete such directories as soon as they are no longer required.
The location where the temporary directory is created for the script will influence the amount of storage space available. For instance, the location
/tmp/is frequently created as a temporary RAM-based file system, such that the script’s temporary files are never actually written to disk and are therefore read & written very quickly; it is however also likely to have a smaller capacity than a physical hard drive.
This location can be set manually in two different ways:
- In the MRtrix _Configuration_file, key “ScriptTmpDir” can be used to set the location where such temporary directories will be created by default.
- When executing the script, command-line option
-tempdircan be used to set the location of the temporary directory for that particular script execution.
In the absence of either of these settings, MRtrix3 will now create this temporary directory in the working directory (i.e. the location the terminal was navigated to when the script was called), in the hope that it will reduce the prevalence of users encountering this issue. This may however cause issues if working across a network, or using a job scheduler.
The storage requirements can vary considerably between different scripts. For instance,
dwibiascorrectonly needs to generate a couple of temporary images per execution; whereas
population_templatemust store non-linear warp fields across many subjects. This may explain why one script crashed when other scripts have completed successfully.
Hanging on network file system when writing images¶
When any MRtrix3 command must read or write image data, there are two primary mechanisms by which this is performed:
1. Memory mapping: The operating system provides access to the contents of the file as though it were simply a block of data in memory, without needing to explicitly load all of the image data into RAM.
2. Preload / delayed write-back: When opening an existing image, the entire image contents are loaded into a block of RAM. If an image is modified, or a new image created, this occurs entirely within RAM, with the image contents written to disk storage only at completion of the command.
This design ensures that loading images for processing is as fast as possible and does not incur unnecessary RAM requirements, and writing files to disk is as efficient as possible as all data is written as a single contiguous block.
Memory mapping will be used wherever possible. However one circumstance where this should not be used is when write access is required for the target file, and it is stored on a network file system: in this case, the command typically slows to a crawl (e.g. progressbar stays at 0% indefinitely), as the memory-mapping implementation repeatedly performs small data writes and attempts to keep the entire image data synchronised.
MRtrix3 will now test the type of file system that a target image is stored on; and if it is a network-based system, it will not use memory-mapping for images that may be written to. However, if you experience the aforementioned slowdown in such a circumstance, it is possible that the particular configuration you are using is not being correctly detected or identified. If you are unfortunate enough to encounter this issue, please report to the developers the hardware configuration and file system type in use.
Linux: very slow performance when writing large images¶
This might be due to the Linux Disk Caching or the kernel’s handling of dirty pages.
On Ubuntu, you can get your current dirty page handling settings with
sysctl -a | grep dirty.
Those settings can be modified in
/etc/sysctl.conf by adding the following
two lines to
vm.dirty_background_ratio = 60 vm.dirty_ratio = 80
vm.dirty_background_ratio is a percentage fraction of your RAM and should
be larger than the image to be written. After changing
sysctl -p to configure the new kernel parameters at runtime.
Depending on your system, these changes might not be persistent after reboot.
mrview unable to open images: “Too many open files”¶
It is possible to encounter this error message particularly if trying to open a large number of DICOM images. In most cases, each slice in a DICOM series is stored in an individual file; all of these files must remain open while the image is loaded. In addition, the maximum number of files open at any time (imposed by the kernel, not MRtrix3) may be relatively small (e.g. 256), such that very few subjects can be opened at once.
There are two ways to solve this issue:
Reduce the number of files opened concurrently: By converting each series of interest to an alternative format (e.g. MRtrix image formats (.mih / .mif)) before opening them in
mrview, the total number of files open at once will be drastically reduced.
Increase the limit on number of files opened: If directly opening DICOM images without first converting them is more convenient, then it is possible to instead increase the kernel’s upper limit on the number of files that can remain open at once. The specific details on how this is done may vary between different OS’s / distributions, but here are a couple of suggestions to try:
The current limit should be reported by:
Try running the following (potentially with the use of
sysctl -w fs.file-max=100000
If this solves the issue, the change can be made permanent by editing file
/etc/sysctl.conf, adding the following line (replacing
<number>with your desired upper limit):
fs.file-max = <number>
On MacOSX, you may instead need to look at the
Set the new upper limit using
ulimit(you can try using a number instead of “unlimited” if you choose to):
ulimit -n unlimited
If this works, you will need to add that line to a file such as
~/.bashrcin order for the change to be applied permanently.