High-performance computing for Monte Carlo radiotherapy calculations

2009 ◽  
Vol 367 (1897) ◽  
pp. 2607-2617 ◽  
Author(s):  
P. Downes ◽  
G. Yaikhom ◽  
J.P. Giddy ◽  
D.W. Walker ◽  
E. Spezi ◽  
...  
Keyword(s):  
Monte Carlo ◽  
High Performance Computing ◽  
High Performance ◽  
Distributed Simulation ◽  
Turnaround Time ◽  
Dose Calculations ◽  
Simulation Based ◽  
Accurate Dose ◽  
Experimental Findings ◽  
Performance Computing

We report on the RTGrid project, which investigates approaches for using high-performance computing infrastructures, such as the grid, in order to reduce the turnaround time of Monte Carlo (MC) simulation-based radiotherapy treatment planning. The main aim of this project is to render accurate dose calculations using MC simulations clinically feasible. To this end, we have successfully implemented and deployed the RTGrid distributed simulation framework for MC dose calculations. In this paper, we present the main experimental findings.

scholarly journals Monte Carlo method and High Performance Computing for solving Fokker–Planck equation of minority plasma particles

2015 ◽  
Vol 81 (3) ◽  
Author(s):  
E. Hirvijoki ◽  
T. Kurki-Suonio ◽  
S. Äkäslompolo ◽  
J. Varje ◽  
T. Koskela ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Method ◽  
High Performance Computing ◽  
High Performance ◽  
Collision Operator ◽  
Planck Equation ◽  
Energetic Ions ◽  
The Monte Carlo Method ◽  
Particle Simulations ◽  
Performance Computing

This paper explains how to obtain the distribution function of minority ions in tokamak plasmas using the Monte Carlo method. Since the emphasis is on energetic ions, the guiding-center transformation is outlined, including also the transformation of the collision operator. Even within the guiding-center formalism, the fast particle simulations can still be very CPU intensive and, therefore, we introduce the reader also to the world of high-performance computing. The paper is concluded with a few examples where the presented method has been applied.

Parallel computing

2018 ◽  
Author(s):  
Joseph F. Boudreau ◽  
Eric S. Swanson
Keyword(s):  
Monte Carlo ◽  
Parallel Computing ◽  
High Performance Computing ◽  
Parallel Programming ◽  
High Performance ◽  
Parallel Application ◽  
Client Server ◽  
Parallel Code ◽  
Performance Computing

This chapter describes various approaches to concurrency, or “parallel programming”. An overview of high performance computing is followed with a review of Flynn’s taxonomy of parallel computing. Three methods for implementing parallel code using the frameworks provided by MPI, openMP, and C++ threads are presented. The use of the C++ constructs mutex and future to resolve issues of synchronization are discussed. All methods are illustrated with an embarrassingly parallel application to a Monte Carlo integral and common pitfalls are presented. The chapter closes with a discussion and example of the utility of forking processes and the use of C++ sockets and their application in a client/server environment.

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