Serverless computing has seen a myriad of work exploring its potential. Some systems tackle Function-as-a-Service (FaaS) properties on automatic elasticity and scale to run highly-parallel computing jobs. However, they focus on specific platforms and convey that their ideas can be extrapolated to any FaaS runtime.

An important question arises: do all FaaS platforms fit parallel computations? In this paper, we argue that not all of them provide the necessary means to host highly-parallel applications. To validate our hypothesis, we create a comparative framework and categorize the architectures of four cloud FaaS offerings, emphasizing parallel performance. We attest and extend this description with an empirical experiment that consists in plotting in deep detail the evolution of a parallel computing job on each service.

The analysis of our results evinces that FaaS is not inherently good for parallel computations and architectural differences across platforms are decisive to categorize their performance. A key insight is the importance of virtualization technologies and the scheduling approach of FaaS platforms. Parallelism improves with lighter virtualization and proactive scheduling due to finer resource allocation and faster elasticity. This causes some platforms like AWS and IBM to perform well for highly-parallel computations, while others such as Azure present difficulties to achieve the required parallelism degree. Consequently, the information in this paper becomes of special interest to help users choose the most adequate infrastructure for their parallel applications.

无服务器计算已经看到了无数探索其潜力的工作。一些系统在自动弹性和规模上处理功能即服务 (FaaS) 属性，以运行高度并行的计算作业。然而，他们专注于特定平台，并表示他们的想法可以推广到任何 FaaS 运行时。

一个重要的问题出现了：所有 FaaS 平台都适合并行计算吗？在本文中，我们认为并非所有这些都提供了托管高度并行应用程序的必要手段。为了验证我们的假设，我们创建了一个比较框架并对四种云 FaaS 产品的架构进行了分类，强调了并行性能。我们通过一个实证实验证明并扩展了这一描述，该实验包括深入详细地绘制每个服务上并行计算作业的演变。

对我们结果的分析表明，FaaS 本身并不适合并行计算，跨平台的架构差异对于对其性能进行分类是决定性的。一个关键洞察是虚拟化技术的重要性和 FaaS 平台的调度方法。由于更精细的资源分配和更快的弹性，并行性通过更轻的虚拟化和主动调度得到改善。这导致一些平台（如 AWS 和 IBM）在高度并行计算方面表现良好，而其他平台（如 Azure）难以实现所需的并行度。因此，本文中的信息对于帮助用户为其并行应用程序选择最合适的基础架构变得特别有意义。