Performance evaluation of medical imaging algorithms on Intel® MIC platform

Abstract

Heterogeneous computer architectures, where CPUs co-exist with accelerators such as vector coprocessors, GPUs and FPGAs, are rapidly evolving to be powerful platforms for tomorrow's exa-scale computing. The Intel® Many Integrated Core (MIC) architecture is Intel's first step towards heterogeneous computing. This paper investigates the performance of the MIC platform in the context of medical imaging and signal processing. Specifically, we analyze the achieved performance of two popular algorithms: Complex Finite Impulse Response (FIR) filtering which is used in ultrasound signal processing and Simultaneous Algebraic Reconstruction Technique (SART) which is used in 3D Computed tomography (CT) volume reconstruction. These algorithms are evaluated on Intel® Xeon Phi™ using Intel's heterogeneous offload model. Our analysis indicates that execution times of both of these algorithms are dominated by the memory access times and hence effective cache utilization as well as vectorization play a significant role in determining the achieved performance. Overall, we perceive that Intel® MIC is an easy-to-program accelerator of the future that shows good potential in terms of performance.