Many-integrated core (MIC) technology for accelerating Monte Carlo simulation of radiation transport: A study based on the code DPM

Abstract Monte Carlo simulation of radiation transport is computationally demanding to obtain reasonably low statistical uncertainties of the estimated quantities. Therefore, it can benefit in a large extent from high-performance computing. This work is aimed at assessing the performance of the first generation of the many-integrated core architecture (MIC) Xeon Phi coprocessor with respect to that of a CPU consisting of a double 12-core Xeon processor in Monte Carlo simulation of coupled electron–photonshowers. The comparison was made twofold, first, through a suite of basic tests including parallel versions of the random number generators Mersenne Twister and a modified implementation of RANECU. These tests were addressed to establish a baseline comparison between both devices. Secondly, through the p DPM code developed in this work. p DPM is a parallel version of the Dose Planning Method (DPM) program for fast Monte Carlo simulation of radiation transport in voxelized geometries. A variety of techniques addressed to obtain a large scalability on the Xeon Phi were implemented in p DPM. Maximum scalabilities of 84 . 2 × and 107 . 5 × were obtained in the Xeon Phi for simulations of electron and photon beams, respectively. Nevertheless, in none of the tests involving radiation transport the Xeon Phi performed better than the CPU. The disadvantage of the Xeon Phi with respect to the CPU owes to the low performance of the single core of the former. A single core of the Xeon Phi was more than 10 times less efficient than a single core of the CPU for all radiation transport simulations.

[1]  Jie Cheng,et al.  Programming Massively Parallel Processors. A Hands-on Approach , 2010, Scalable Comput. Pract. Exp..

[2]  Xun Jia,et al.  GPU technology is the hope for near real-time Monte Carlo dose calculations: Point/Counterpoint , 2015 .

[3]  J. Sempau,et al.  DPM, a fast, accurate Monte Carlo code optimized for photon and electron radiotherapy treatment planning dose calculations , 2000 .

[4]  Lin Su,et al.  ARCHERRT - a GPU-based and photon-electron coupled Monte Carlo dose computing engine for radiation therapy: software development and application to helical tomotherapy. , 2014, Medical physics.

[5]  Peter Ziegenhein,et al.  Fast CPU-based Monte Carlo simulation for radiotherapy dose calculation , 2015, Physics in medicine and biology.

[6]  Steve B. Jiang,et al.  Development of a GPU-based Monte Carlo dose calculation code for coupled electron–photon transport , 2009, Physics in medicine and biology.

[7]  Dirk Schmidl,et al.  Assessing the Performance of OpenMP Programs on the Intel Xeon Phi , 2013, Euro-Par.

[8]  I. Kawrakow Accurate condensed history Monte Carlo simulation of electron transport. I. EGSnrc, the new EGS4 version. , 2000, Medical physics.

[9]  Indrin J Chetty,et al.  Photon beam relative dose validation of the DPM Monte Carlo code in lung-equivalent media. , 2003, Medical physics.

[10]  Indrin J Chetty,et al.  Implementation of the DPM Monte Carlo code on a parallel architecture for treatment planning applications. , 2004, Medical physics.

[11]  Kevin Souris,et al.  Fast multipurpose Monte Carlo simulation for proton therapy using multi- and many-core CPU architectures. , 2016, Medical physics.

[12]  Indrin J Chetty,et al.  Benchmarking of the dose planning method (DPM) Monte Carlo code using electron beams from a racetrack microtron. , 2002, Medical physics.

[13]  J. Sempau,et al.  PENELOPE-2006: A Code System for Monte Carlo Simulation of Electron and Photon Transport , 2009 .

[14]  Qing Zhang,et al.  High-Performance Computing on the Intel® Xeon Phi™ , 2014, Springer International Publishing.

[15]  G. G. Stokes "J." , 1890, The New Yale Book of Quotations.

[16]  Sabri Pllana,et al.  Accelerating DNA Sequence Analysis using Intel Xeon Phi , 2015, ArXiv.

[17]  J. Sempau,et al.  Monte Carlo simulation of electron beams from an accelerator head using PENELOPE , 2001, Physics in Medicine and Biology.

[18]  I. Kawrakow,et al.  History by history statistical estimators in the BEAM code system. , 2002, Medical physics.

[19]  F. James A Review of Pseudorandom Number Generators , 1990 .

[20]  Takuji Nishimura,et al.  Mersenne twister: a 623-dimensionally equidistributed uniform pseudo-random number generator , 1998, TOMC.

[21]  John B. Shoven,et al.  I , Edinburgh Medical and Surgical Journal.

[22]  I. Kawrakow Accurate condensed history Monte Carlo simulation of electron transport. II. Application to ion chamber response simulations. , 2000, Medical physics.

[23]  Andreu Badal,et al.  A package of Linux scripts for the parallelization of Monte Carlo simulations , 2006, Comput. Phys. Commun..

[24]  A. Dell'Acqua,et al.  Geant4 - A simulation toolkit , 2003 .

[25]  Xun Jia,et al.  GPU-based fast Monte Carlo simulation for radiotherapy dose calculation. , 2011, Physics in medicine and biology.

[26]  Benoît Ozell,et al.  GPUMCD: A new GPU-oriented Monte Carlo dose calculation platform. , 2011, Medical physics.