Aggregated HPC resources have rigid allocation systems and programming models which struggle to adapt to diverse and changing workloads. Consequently, HPC systems fail to efficiently use the large pools of unused memory and increase the utilization of idle computing resources. Prior work attempted to increase the throughput and efficiency of supercomputing systems through workload co-location and resource disaggregation. However, these methods fall short of providing a solution that can be applied to existing systems without major hardware modifications and performance losses. In this paper, we improve the utilization of supercomputers by employing the new cloud paradigm of serverless computing. We show how serverless functions provide fine-grained access to the resources of batch-managed cluster nodes. We present an HPC-oriented Function-as-a-Service (FaaS) that satisfies the requirements of high-performance applications. We demonstrate a \emph{software resource disaggregation} approach where placing functions on unallocated and underutilized nodes allows idle cores and accelerators to be utilized while retaining near-native performance.

翻译：聚合型高性能计算资源受限于僵化的分配系统和编程模型，难以适应多样化的动态工作负载。因此，高性能计算系统未能有效利用大量闲置内存池，且无法提升空闲计算资源的利用率。此前研究尝试通过工作负载共置与资源解聚来提升超级计算系统的吞吐量与效率，但这些方法缺乏可在无需重大硬件改造及性能损失的情况下应用于现有系统的解决方案。本文通过采用无服务器计算这一新型云范式来提升超级计算机的利用率，论证了无服务器函数如何实现对批处理集群节点资源的细粒度访问，并提出了一种面向高性能计算的函数即服务（FaaS）架构，满足高性能应用需求。我们展示了一种软件资源解聚方法：通过将函数部署到未分配及低利用率节点，可使空闲核心与加速器得到利用，同时保持接近原生性能。