HTTomo stands for High Throughput Tomography pipeline for processing and reconstruction of parallel-beam tomography data in Python. The HTTomo project was initiated in 2022 at Diamond Light source by Tomography Team. With the Diamond-II upgrade approaching, there is a need to be able to process bigger data in larger quantities and with high fidelity. With the support of modern developments in the field of High Performance Computing and multi-GPU processing, it is possible to enable faster data streaming and higher throughput for big data.

The main concept of HTTomo is to split the data stored as HDF5 files into three-dimensional (3D) chunks that would fit into a GPU memory available and process them in parallel on one or multiple devices.

As a UI, HTTomo exploits YAML for pipelines or process lists construction.

HTTomo fully relies on the backends for data processing and does not contain any algorithms.

Installation

Please see the detailed instructions here.

Usage

Please see the examples how to run HTTomo from the command line inside the shell.

Benchmarks

To be added…