Computed tomography (CT) image reconstruction is a crucial technique for many imaging applications. Among various reconstruction methods, Model-Based Iterative Reconstruction (MBIR) enables super-resolution with superior image quality. MBIR, however, has a high memory requirement that limits the achievable image resolution, and the parallelization for MBIR suffers from limited scalability. In this paper, we propose Asynchronous Consensus MBIR (AC-MBIR) that uses Consensus Equilibrium (CE) to provide a super-resolution algorithm with a small memory footprint, low communication overhead and a high scalability. Super-resolution experiments show that AC-MBIR has a 6.8 times smaller memory footprint and 16 times more scalability, compared with the state-of-the-art MBIR implementation, and maintains a 100% strong scaling efficiency at 146880 cores. In addition, AC-MBIR achieves an average bandwidth of 3.5 petabytes per second at 587520 cores.
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Wang, X., Sridhar, V., Ronaghi, Z., Thomas, R., Deslippe, J., Parkinson, D., … Warfield, S. K. (2019). Consensus equilibrium framework for super-resolution and extreme-scale CT reconstruction. In International Conference for High Performance Computing, Networking, Storage and Analysis, SC. IEEE Computer Society. https://doi.org/10.1145/3295500.3356142