Monte Carlo based ray tracing in CPU-GPU heterogeneous systems and applications in radiation therapy

Abstract

Monte Carlo based ray tracing (MCBRT) is the foundation of simulating the transport of particles in an inhomogeneous medium, and arises in different applications such as global illumination in graphics rendering and dose calculation in radiation therapy. Due to the computation intensive nature of MCBRT, GPUs have been extensively adopted to accelerate it. However, memory bandwidth becomes a new bottleneck for GPU-based implementations due to the lack of data locality in the MCBRT random memory access patterns. To tackle this issue and consequently improve performance of MCBRT, we present a new locality enhancing method, called LE-MCBRT, on CPU-GPU heterogeneous systems. LE-MCBRT is based on task partitioning and scheduling, which enhances both the spatial and temporal data locality by organizing random rays into coherent groups. We also develop a CPU-GPU pipeline scheme to reduce the overhead in such ray organization process. To show the applicability of our LE-MCBRT method, we apply it to a dose calculation problem in radiation cancer treatment, achieving 6-8X speedup over the best-known GPU solutions on various clinical cases of radiation therapy.