Computational benchmarking of on-board processing performance for space applications has often been done in a case-to-case basis, taking into account only a small subset of devices and specific, often proprietary, applications, limiting domain coverage and reproducibility. While commercial benchmarks exists for embedded systems, they are usually limited to CPUs and are based on synthetic algorithms non-relevant for space. Consequently, they are not generally suitable for assessing highly parallel processors (GPUs, DSPs, etc.) and/or hardware implementations (i.e. ASICs and FPGAs) which are commonplace in space systems. For on-board processing, there are a number of application types which reoccur over multiple missions. These applications and algorithms are often driving the overall computational requirements of the mission, e.g. in the case of image and radar processing, RF signal processing and compression. In each case, there are certain performance metrics ��� such as the number of pixels processed per second ��� which are well-known and easily understandable by designers and users. Finally, with the rise of machine learning applications in on-board space applications, tasks such as image classification and object detection using SVMs and CNNs are becoming commonly used. OBPMark (On-Board Processing Benchmarks) defines a set of benchmarks covering the typical classes of applications commonly found on-board spacecraft. The benchmark suite is publicly available to enable easy comparison of different systems and to quickly down-select possible processing solutions for a mission. It is open source and includes multiple implementations, while it is easily extensible allowing porting and optimization to target platforms, including heterogeneous ones, for fair comparison. Currently, implementations in standard C, OpenMP, OpenCL and CUDA are included. A technical note, defining the algorithms used is also provided to allow implementers to provide additional dedicated versions, including reference inputs and outputs for correctness verification as well as an optional automated launching framework for reproducibility. This also allows the benchmarks to be implemented in FPGAs, while ensuring equivalence with the reference implementations. Five categories of benchmarks are defined 1) Image Processing Pipelines; 2) Standard Compression Algorithms; 3) Standard Encryption Algorithms; 4) Processing Building Blocks; and 5) Machine Learning Inference. In each category, specific benchmarks are included, e.g. both image and radar image compression. Recommended parameters for the CCSDS compression standards 121.0, 122.0 and 123.0 are provided. The processing building blocks include e.g. FIR filters and FFT processing. Two ML applications have been chosen: cloud screening and ship detection. Both will be provided as standard pre-trained machine learning models, both floating point and quantized integer models ��� to allow support for multiple microarchitectures. The specification of OBPMark has been initiated by ESA together with BSC as an open source project to allow transparent and open performance comparison of devices and systems. The project will also maintain a list of available benchmark results on its open repository. The work has been carried out both internally at ESA, and at BSC through the on-going ESA-funded GPU4S activity, whose optimised versions of algorithmic building blocks implemented in the open source GPU4S Bench benchmarking suite were used as a basis.