The usage of graphics processing units (GPUs) as computing architectures for inherently data parallel signal processing applications in this computing era is very popular. In principle, GPUs in comparison with central processing units (CPUs) could achieve significant speed-up over the latter, especially considering data parallel applications which expect high throughput. The paper investigates the usage of GPUs for running space borne image data compression algorithms, in particular the CCSDS

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... 2.0-B-1 standard as a case study. The paper proposes an architecture to parallelize the Bit-Plane Encoder (BPE) stage of the CCSDS 122.0-B-1 in lossless mode using a GPU to achieve high throughput performance to facilitate real-time compression of satellite image data streams. Experimental results are furnished by comparing the performance in terms of compression time of the GPU implementation versus a state of the art single threaded CPU and an field-programmable gate array (FPGA) implementation. The GPU implementation on a NVIDIA® GeForce® GTX 670 achieves a peak throughput performance of 162.382 Mbyte/s (932.288 Mbit/s) and an average speed-up of at least 15 compared to the software implementation running on a 3.47 GHz single core Intel® XeonTM processor. The high throughput CUDA implementation using GPUs could potentially be suitable for air borne and space borne applications in the future, if the GPU technology evolves to become radiation-tolerant and space-qualified.