Frequently asked questions¶
Here’s a list of questions.
- How can I report bugs?
- Are there any video tutorials?
- Can I run this on a HPC cluster?
- Are there any segmentation routines?
- Are there any tools for aligning projections?
- What is ASTRA toolbox?
- Why TomoPy and ASTRA were integrated?
- Which platforms are supported?
- Does ASTRA support all GPUs?
- What is UFO?
We currently do not have specific plans in this direction, but we agree that it would be very helpful.
In their default installation packages, TomoPy and the ASTRA toolbox are limited to running on a multicore single machine. The ASTRA toolbox, and TomoPy through the presented ASTRA integration, are able to use multiple GPUs that are installed in a single machine. Both toolboxes can be run on a HPC cluster through parallelization using MPI, but since installation and running on a HPC cluster is often cluster specific, the default installation packages do not include these capabilities.
As such, the integrated packages that is presented in the manuscript currently does not support running on a HPC cluster. Note that the ASTRA toolbox provides a separate MPI enabled package for use on a HPC cluster. We refer to [C22] for more details about TomoPy’s planned HPC implementation. It is a MapReduce type MPI implementation layer, which was succesfully used on many clusters, i.e. Stampede, Cori, Mira. There are plans to allow user access to TomoPy on a HPC cluster (e.g. through a client or webportal), but these projects will take some time before they are being matured for user’s use.
Some data processing operations can be applied after reconstruction. Examples of these type of operations are image based ring removal methods, and gaussian filtering or median filtering the reconstructed image. Typicaly, these methods are called “postprocessing algorithms, since they occur after the reconstruction.
The package does not include segmentation algorithms, since we are currently focused on tomography, while we feel that segmentation are more part of the application specific data analysis that occurs after tomographic processing. An important exception is when segmentation steps are used as part of the tomographic reconstruction algorithm, such as in the DART algorithm.
Yes we have. Please check the Examples section for details.
The ASTRA toolbox provides highly efficient tomographic reconstruction methods by implementing them on graphic processing units (GPUs). It includes advanced iterative methods and allows for very flexible scanning geometries. The ASTRA toolbox also includes building blocks which can be used to develop new reconstruction methods, allowing for easy and efficient implementation and modification of advanced reconstruction methods. However, the toolbox is only focused on reconstruction, and does not include pre-processing or post-processing methods that are typically required for correctly processing synchrotron data. Furthermore, no routines to read data from disk are provided by the toolbox.
The TomoPy toolbox is specifically designed to be easy to use and deploy at a synchrotron facility beamline. It supports reading many common synchrotron data formats from disk [C12], and includes several other processing algorithms commonly used for synchrotron data. TomoPy also includes several reconstruction algorithms, which can be run on multi-core workstations and large-scale computing facilities. The algorithms in TomoPy are all CPU-based, however, which can make them prohibitively slow in the case of iterative methods, which are often required for advanced tomographic experiments.
By integrating the ASTRA toolbox in the TomoPy framework, the optimized GPU-based reconstruction methods become easily available for synchrotron beamline users, and users of the ASTRA toolbox can more easily read data and use TomoPy’s other functionality for data filtering and cleaning.
TomoPy supports Linux and Mac OS X, and the ASTRA toolbox supports Linux and Windows. As such, the combined package currently supports only Linux, but we are working on supporting more operating systems.
The GPU algorithms are all implemented used nVidia CUDA. As a result, only nVidia CUDA enabled video cards can be used to run them.
UFO is a general purpose image processing framework, optimized for heterogeneous compute systems and streams of data. Arbitrary data processing tasks are plugged together to form larger processing pipelines. These pipelines are then mapped to the hardware resources available at run-time, i.e. both multiple GPUs and CPUs.
One specific use case that has been integrated into the TomoPy is fast reconstruction using the filtered backprojection and direct Fourier inversion methods although others for pre- and post-processing might be added in the future.