Department of Biomedical Engineering, University of California Davis, Davis, CA, United States of America.
Department of Radiology, University of California Davis, Davis, CA, United States of America.
Biomed Phys Eng Express. 2024 Jun 25;10(4). doi: 10.1088/2057-1976/ad5847.
This study investigates the potential of cloud-based serverless computing to accelerate Monte Carlo (MC) simulations for nuclear medicine imaging tasks. MC simulations can pose a high computational burden-even when executed on modern multi-core computing servers. Cloud computing allows simulation tasks to be highly parallelized and considerably accelerated.We investigate the computational performance of a cloud-based serverless MC simulation of radioactive decays for positron emission tomography imaging using Amazon Web Service (AWS) Lambda serverless computing platform for the first time in scientific literature. We provide a comparison of the computational performance of AWS to a modern on-premises multi-thread reconstruction server by measuring the execution times of the processes using between105and2·1010simulated decays. We deployed two popular MC simulation frameworks-SimSET and GATE-within the AWS computing environment. Containerized application images were used as a basis for an AWS Lambda function, and local (non-cloud) scripts were used to orchestrate the deployment of simulations. The task was broken down into smaller parallel runs, and launched on concurrently running AWS Lambda instances, and the results were postprocessed and downloaded via the Simple Storage Service.Our implementation of cloud-based MC simulations with SimSET outperforms local server-based computations by more than an order of magnitude. However, the GATE implementation creates more and larger output file sizes and reveals that the internet connection speed can become the primary bottleneck for data transfers. Simulating 10decays using SimSET is possible within 5 min and accrues computation costs of about $10 on AWS, whereas GATE would have to run in batches for more than 100 min at considerably higher costs.Adopting cloud-based serverless computing architecture in medical imaging research facilities can considerably improve processing times and overall workflow efficiency, with future research exploring additional enhancements through optimized configurations and computational methods.
本研究探讨了基于云的无服务器计算在加速核医学成像任务中的蒙特卡罗（MC）模拟的潜力。即使在现代多核计算服务器上执行，MC 模拟也可能带来很高的计算负担。云计算允许模拟任务高度并行化并大大加速。我们首次在科学文献中研究了基于云的无服务器 MC 放射性衰变模拟在正电子发射断层扫描成像中的计算性能，使用的是亚马逊网络服务（AWS）Lambda 无服务器计算平台。我们通过使用 10^5 到 2·10^10 个模拟衰变来测量过程的执行时间，比较了 AWS 的计算性能与现代内部部署多线程重建服务器的计算性能。我们在 AWS 计算环境中部署了两个流行的 MC 模拟框架-SimSET 和 GATE。使用容器化应用程序映像作为 AWS Lambda 函数的基础，并使用本地（非云）脚本来协调模拟的部署。任务被分解为更小的并行运行，并在并发运行的 AWS Lambda 实例上启动，结果通过简单存储服务进行后处理和下载。我们使用 SimSET 实现的基于云的 MC 模拟的性能比本地服务器计算高出一个数量级以上。然而，GATE 实现会创建更多和更大的输出文件大小，并表明互联网连接速度可能成为数据传输的主要瓶颈。使用 SimSET 模拟 10 个衰变可以在 5 分钟内完成，并且在 AWS 上产生约 10 美元的计算成本，而 GATE 则需要以更高的成本分批运行超过 100 分钟。在医学成像研究设施中采用基于云的无服务器计算架构可以大大提高处理时间和整体工作流程效率，未来的研究将通过优化配置和计算方法来探索额外的增强功能。
