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International Journal of Advance Scientific Research SCIENTIFIC IDEAS. INTERNATIONAL PERSPECTIVES.
ISSN 2750-1396 12 issues per year · Monthly
Crossref DOI: 10.37547/ijasr
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Articles Published:

GPU-Accelerated Monte Carlo Methods for Proton Therapy: Integrating Heterogeneous Runtime Systems and Unified Memory Strategies for Clinical-Grade Dose Calculation

Vol. 5 No. 07 (2025): Volume 05 Issue 07 Pages 89-100
Authors
  • Dr. Marcellus V. Carter
    Department of Computational Medicine, University of Edinburgh
RESEARCH OVERVIEW

Abstract

This article presents a comprehensive, publication-ready investigation into the integration of GPU-accelerated Monte Carlo (MC) dose calculation methods for proton therapy with modern heterogeneous runtime systems and unified memory strategies. The work synthesizes principles from GPU programming, runtime compilation, managed runtime systems, and medical physics Monte Carlo simulations to propose a cohesive framework for clinical-grade, high-throughput proton dose calculation. The abstract outlines the motivation: proton therapy demands highly accurate dose calculations that account for complex particle transport physics while delivering results within clinical time constraints. Traditional CPU-based MC codes offer high fidelity but are limited by throughput; GPU implementations have demonstrated orders-of-magnitude speedups, yet bring challenges in memory management, precision, platform heterogeneity, and security. This paper reviews the computational and physical foundations of MC simulation in proton therapy, examines GPU programming models and thread hierarchies, surveys existing GPU-based MC systems and verification/validation efforts, analyzes unified memory and its performance implications, and proposes a methodology that couples just-in-time (JIT) GPU compilation, managed runtimes, and careful algorithmic restructuring to reconcile precision, performance, and maintainability. The results section provides a descriptive analysis of expected performance gains, trade-offs in memory strategies, and implications for clinical deployment. In the discussion, limitations, potential failure modes, and avenues for future work—including validation workflows, regulatory considerations, and hybrid CPU–GPU orchestration—are explored in depth. The conclusion synthesizes these threads into actionable recommendations for researchers and system designers seeking to produce clinically viable, reproducible, and secure GPU-accelerated Monte Carlo proton therapy dose engines. Keywords: proton therapy, Monte Carlo simulation, GPU acceleration, unified memory, just-in-time compilation, heterogeneous runtime, clinical dose calculation.

Keywords

  • Proton therapy
  • Monte Carlo simulation
  • GPU acceleration
  • unified memory
SOURCES & CITATIONS

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