Keywords: Motion Correction, Breast, MRI Hardware, CPU-GPU
Motivation: MRI motion-compensated reconstruction programs rely on several computationally intensive algorithms. For clinical use, they need to use efficiently the computational resources of compute nodes to achieve a good performance.
Goal(s): Evaluate the performance of a program across architectures and optimize its execution without any code modification.
Approach: We investigate the different parallelization paradigms. We use the roofline model, and the performance profiling tools to derive the architectural efficiency on CPU. A Matlab GPU implementation of the reconstruction kernel is used to draw comparisons.
Results: The optimal parallel mapping reduces considerably the reconstruction time. Moreover, GPUs don’t outperform CPUs in every reconstruction problem.
Impact: We demonstrate the importance of micro-architectural study in the overall behavior of MRI motion-compensated reconstruction codes. This study provides insights about how to select the most suitable architecture to a given code, depending on its hardware limitations.
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