HP4S: High Performance Parallel Payload Processing for Space

Next generation space missions will require more capable computers in order to implement either advanced navigation and control algorithms needed to increase the spacecraft autonomy and agility or on the payload side with complex scientific payload data pre-processing algorithms.
There is therefore a high interest for building efficient and disruptive on-board computing for future applications, based on the exploitation of multicore and manycores to increase on-board processing capability while sustaining flexibility through the use of software.
The purpose of this study was to demonstrate the benefits of using one of the most well-known parallel OpenMP programming model for the development of parallel space applications, in terms of performance, programmability and portability.
Two main goals were identified:
• Improve overall system performance by exploiting the most advanced parallel embedded architectures targeting the space domain
• Improve the parallel programming productivity by reducing the initial development efforts of systems based on parallel architectures,
During the project two representative image processing use cases were selected and successfully ported to OpenMP parallel programming model with very limited effort and no modification of actual algorithmic legacy code.
Two promising high-end computing devices were selected targeting rad-hard family with the GR740 and COTS family with latest Kalray Massively Parallel Processing Architecture device, Coolidge, released end of Q1 2020.
OpenMP runtime and Open Source observability tools provided by Barcelona Supercomputing Center were ported from HPC mainstream to the selected hardware targets, and exercised through the selected software use cases.
The project demonstrated that usage of OpenMP parallel programming could facilitate the development, and analysis of parallel real-time space applications. Development benefits from embedded runtime thus alleviating the programmer of fine parallelization orchestration burden thanks to non-intrusive and portable source code annotations.
The evaluated OpenMP parallel programming model ported to relevant hardware targets accelerates the development, profiling, analysis and execution of parallel real-time space applications, while providing significant performance and portability benefits.
Some questions remain open for future work amongst which:
• Evaluation of state-of-the-art compiler techniques to guarantee that parallel OpenMP applications are functionally correct and safe, as an example with the absence of pathological race conditions or deadlocks.
• Adaptation of the OpenMP runtime libraries to ensure that the timing guarantees devised at analysis time can be guaranteed at deployment time and continue maturing the prototyped instrumentation and observation tools.
• Exploration of complementary OpenMP features such as offloading to FPGA or other remote computing devices such as neighbor clusters on a ManyCore or specialized accelerators.