Cloud-based serverless computing enables accelerated monte carlo simulations for nuclear medicine imaging

Abstract

Objective.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.Approach.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.Main results.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 10⁹decays 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.Significance.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.

Keywords: AWS lambda; GATE; Monte-Carlo simulation; SimSET; cloud-based computing; computational efficiency; nuclear medicine imaging.

Figure 1:

The computing architecture implements an interface between the remote client, the cloud storage service (AWS S3) and the computing units (AWS lambda) to execute parallel SimSET simulations.

Figure 2:

On client side a Python script is used to access the remote cloud storage service (AWS S3) and to execute GATE on parallel computing units (AWS lambda).

Figure 3:

Comparison of the performance of SimSET simulations using cloud-based computing with AWS lambda (diamonds) to our local multi-core server (circles). The dashed line describes the computation time on the cloud only, and the bottom graph displays the performance improvement factor when using AWS compared to local computations.

Figure 4:

Comparison of the performance of GATE simulations using cloud-based computing with AWS lambda (diamonds) with our local multi-core server (circles). The dashed line describes the computation time on the cloud only, and the bottom graph displays the performance improvement factor when using AWS compared to local computations.