加速異質架構上蒙地卡羅於光子治療的模擬

Abstract

近年來，在醫療方面特別是癌症治療光子被視為不可或缺的角色，為了增進醫療的品質，我們需要透過電腦精確的運算或模擬，一般來說以蒙地卡羅方法為基礎的模擬被大眾視為比較精準的方法，卻需要龐大的運算資源。身為最普及運算資源的中央運算元(CPU)在這類的應用中扮演重要的角色，但卻很少人有效地使用向量來加速在中央運算元的運算，且在Xeon Phi協同處理器中效能的好壞取決於向量化的程度。在這篇的論文中，我使用OpenMP平行並向量化了MCML程式，而後也進一步討論在這些平行與向量化過後的MCML程式跑在Xeon CPU及Xeon Phi協同處理器上的效能。

Recently, photon is indispensable in many medical applications, such as cancer treatment. After accurate computing or simulating, it improve the quality of medical treatments. Generally, Monte Carlo-based simulations are considered to deliver accurate results, but require intensive computational resources. CPU (Central Processing Unit), the most universal resource, plays an important role in computing the application. However, less researches effectively use vector to accelerate the computations on CPU. Furthermore, on Xeon Phi coprocessor, the performance depends on degree of vectorization. In this paper, we parallelized and vectorized the Monte Carlo modeling of light transport in multi-layered tissues (MCML) program with OpenMP.We then discussed the performance of the parallelized and vectorized MCML kernel program runs on Xeon CPU and Xeon Phi coprocessor.