Using the C++ Sender Asynchronous Programming Model in the Implementation of Efficient Epidemiological Simulations

ABSTRACT This paper evaluates the C++ Sender Programming Model for heterogeneous parallel computing, using an Agent‐Based epidemiological simulator as a case study. Conventional simulations rely on proprietary frameworks like CUDA , creating vendor lock‐in . To assess the Sender Model as a portable alternative, we implement an SEIRS dengue simulator that assigns tasks to CPU or GPU . Asynchronous execution is 3.8 times faster than synchronous methods. Our analysis also identified bottlenecks such as GPU stalls from memory operations and stochastic workloads, informing future optimization efforts.

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Publication Details

Journal
Concurrency and Computation Practice and Experience
Published
2026-07-24
DOI
https://doi.org/10.1002/cpe.70864
Primary Topic
Distributed and Parallel Computing Systems
Type
article
Field-Weighted Citation Impact
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article

Using the C++ Sender Asynchronous Programming Model in the Implementation of Efficient Epidemiological Simulations

Rogério Luís Rizzi, Guilherme Galante, Pablo Alessandro Santos Hugen
Concurrency and Computation Practice and Experience
Distributed and Parallel Computing Systems
article

Using the C++ Sender Asynchronous Programming Model in the Implementation of Efficient Epidemiological Simulations

Rogério Luís Rizzi, Guilherme Galante, Pablo Alessandro Santos Hugen
article en

Abstract

ABSTRACT This paper evaluates the C++ Sender Programming Model for heterogeneous parallel computing, using an Agent‐Based epidemiological simulator as a case study. Conventional simulations rely on proprietary frameworks like CUDA , creating vendor lock‐in . To assess the Sender Model as a portable alternative, we implement an SEIRS dengue simulator that assigns tasks to CPU or GPU . Asynchronous execution is 3.8 times faster than synchronous methods. Our analysis also identified bottlenecks such as GPU stalls from memory operations and stochastic workloads, informing future optimization efforts.

Concurrency and Computation Practice and ExperienceVol. 38(15)
Universidade Federal do Rio Grande do Sul (BR), Universidade Estadual do Oeste do Paraná (BR)
Good health and well-being
Openalex Percentile: Top 6%
Distributed and Parallel Computing Systems
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