Competing energy lookup algorithms in Monte Carlo neutron transport calculations and their optimization on CPU and Intel MIC architectures

The Monte Carlo method is a common and accurate way to model neutron transport with minimal approximations. However, such method is rather time-consuming due to its slow convergence rate. More specifically, the energy lookup process for cross sections can take up to 80% of overall computing time and therefore becomes an important performance hot-spot. Several optimization solutions have been already proposed: unionized grid, hashing and fractional cascading methods. In this paper we revisit those algorithms for both CPU and Many Integrated Core (MIC) architectures and introduce vectorized versions. Tests are performed with the PATMOS Monte Carlo prototype, and algorithms are evaluated and compared in terms of time performance and memory usage. Results show that significant speedup can be achieved over the conventional binary search on both CPU and MIC. Using vectorization instructions has been proved efficient on manycore architecture due to its 512-bit Vector Processing Unit (VPU); on CPU this improvement is limited by the smaller VPU width. Further optimization like memory reduction turns out to be very important since it largely improves computing performance.