Towards an Adaptive Runtime System for Cloud-Native HPC

2026-03-15

AI Summary

This work addresses the challenges traditional high-performance computing (HPC) applications face in adapting to the dynamic, heterogeneous, and resource-volatile nature of cloud environments, particularly in leveraging elastic resources and mitigating performance instability. Building upon the Charm++ asynchronous message-driven runtime system, the authors propose a cloud-native adaptive runtime framework that introduces a novel rate-aware load balancing mechanism to optimize performance across heterogeneous CPU/GPU resources. The framework further extends its resource management module to support low-overhead scheduling of preemptible instances. By effectively alleviating network contention and processor performance fluctuations, the proposed approach significantly enhances the execution efficiency, stability, and cost-effectiveness of HPC applications in cloud settings.

Technology Category

• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Virtualization and resource management in Web systems and infrastructures
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

The ongoing convergence of HPC and cloud computing presents a fundamental challenge: HPC applications, designed for static and homogeneous supercomputers, are ill-suited for the dynamic, heterogeneous, and volatile nature of the cloud. Traditional parallel programming models like MPI struggle to leverage key cloud advantages, such as resource elasticity and low-cost spot instances, while also failing to address challenges like performance variability and processor heterogeneity. This paper demonstrates how the asynchronous, message-driven paradigm of the Charm++ parallel runtime system can bridge this gap. We present a set of tools and strategies that enable HPC applications to run efficiently and resiliently on dynamic cloud infrastructure across both CPU and GPU resources. Our work makes two key contributions. First, we demonstrate that rate-aware load balancing in Charm++ improves performance for applications running on heterogeneous CPU and GPU instances on the cloud. We further demonstrate how core Charm++ principles mitigate performance degradation from common cloud challenges like network contention and processor performance variability, which are exacerbated by the tightly coupled, globally synchronized nature of many science and engineering applications. Second, we extend an existing resource management framework to support GPU and CPU spot instances with minimal interruption overhead. Together, these contributions provide a robust framework for adapting HPC applications to achieve efficient, resilient, and cost-effective performance on the cloud.

Problem

• HPC
• cloud computing
• heterogeneity
• performance variability
• resource elasticity

Innovation

• adaptive runtime
• heterogeneous cloud
• rate-aware load balancing
• spot instances
• Charm++