Introduction

Monte Carlo simulations play a crucial role in healthcare, particularly in radiation therapy planning, where they model particle interactions to optimize dose delivery. Despite their computational efficiency using GPU frameworks like CUDA, performance bottlenecks due to frequent data transfers between CPU and GPU hinder scalability. This project focuses on overcoming this challenge by leveraging advanced GPU programming techniques to optimize data transfers, enabling faster and more efficient simulations.

Problem Statement

Monte Carlo simulations in radiation therapy rely heavily on GPU acceleration. However, frequent data movement between the CPU and GPU introduces significant overhead, leading to slower runtimes and limited scalability. This project aims to reduce these latencies, enabling better resource utilization and faster computational workflows for healthcare simulations.

Techniques Used

The following techniques were implemented to address data transfer overhead:

CUDA Unified Memory: Simplified memory management to reduce the complexity and latency of host-device transfers.

Data Batching: Optimized data movement by processing transfers in larger, efficient batches.

Tools Used

Programming Languages: Python, C++

Frameworks: CUDA, NVIDIA Nsight Systems

Infrastructure: HPC systems with GPU clusters

Visualization: Matplotlib for performance analysis

Conclusion and Future Outlook

This project significantly mitigates the critical bottleneck related to data transfer latency inherent in GPU-accelerated Monte Carlo simulations. The efficiency was achieved by incorporating CUDA Unified Memory and batching techniques for data, leading to dramatic reductions in transfer overhead that increased overall simulation runtime and scalability. Optimizations such as these are critical for health-care applications, notably those involving radiation therapy planning, where efficiency directly determines patient outcome.

For Future Work, continuation of the methodology of the project to other parallel computing challenges of healthcare simulations. Potential future directions include:

Balancing Dynamic Workload: Potential Improvement in GPU utilization across different data sizes.
Real-World Integration: Partnering with healthcare institutions to validate performance gains on clinical datasets.
Advanced Profiling Tools: Utilizing AI-driven insights to further tune memory access patterns and optimize data movement.

These advancements promise to change the regime of healthcare simulations, making them more efficient, scalable, and applicable to diverse computational challenges.

Optimizing GPU Data Transfers for Monte Carlo Simulations in Healthcare

Introduction

Problem Statement

Techniques Used

Tools Used

Expected Outcomes

Conclusion and Future Outlook

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Joydeep Das

HPC & Physics Researcher | Plasma Simulations | Machine Learning