A memory management library for CT-reconstruction on GPUs

Abstract

Driven by improved computational throughput, multi- and many-core processors have been increasingly used in medical image processing. As these systems contain a discrete memory node, programmers have to manually manage the data transfer. To improve throughput by overlapping data transfers and task execution, special hardware details have to be known and should be considered with care. Data management could be even more tedious when the data size exceeds the GPU memory. In this work, we present a library that provides a convenient interface for CT reconstruction. Further, it contains a transparent data management, automatic data partitioning in case the GPU memory is insufficient, and overlapping techniques for improved performance. Our evaluations reveal that the library is able to reduce the amount of necessary code lines by ≈ 63% with respect to a comparable manual implementation. Additionally, a speedup of 38.1% for a volume size of 256 (10.7% for a volume size of 512) could be achieved by the library’s overlapping technique.