Cloud Services Enable Efficient AI-Guided Simulation Workflows across Heterogeneous Resources
This work addresses the problem of efficiently utilizing diverse computing resources in computational science, though it is incremental as it builds on existing methods for cross-resource workflows.
The paper tackled the challenge of deploying AI-guided simulation workflows across heterogeneous computing resources by using cloud-hosted management services for authentication, function invocation, and data transfer, achieving performance parity with direct-connection systems.
Applications that fuse machine learning and simulation can benefit from the use of multiple computing resources, with, for example, simulation codes running on highly parallel supercomputers and AI training and inference tasks on specialized accelerators. Here, we present our experiences deploying two AI-guided simulation workflows across such heterogeneous systems. A unique aspect of our approach is our use of cloud-hosted management services to manage challenging aspects of cross-resource authentication and authorization, function-as-a-service (FaaS) function invocation, and data transfer. We show that these methods can achieve performance parity with systems that rely on direct connection between resources. We achieve parity by integrating the FaaS system and data transfer capabilities with a system that passes data by reference among managers and workers, and a user-configurable steering algorithm to hide data transfer latencies. We anticipate that this ease of use can enable routine use of heterogeneous resources in computational science.