XaaS Containers: Performance-Portable Representation With Source and IR Containers

2025-09-22

AI Summary

In the context of HPC–cloud convergence, containers improve deployment portability but suffer performance degradation due to ABI compatibility constraints that hinder hardware-specific optimization. To address this, we propose XaaS (eXecution-as-a-Service), a performance-portable container framework that defers critical compilation decisions—such as architecture-specific optimizations—to deployment time via source-code and intermediate representation (IR) containerization. Our approach integrates LLM-assisted identification of HPC software specialization mechanisms with IR-level deferred specialization and compiler pipeline analysis, enabling end-to-end automated, system-wide optimization across diverse architectures. Experiments demonstrate that XaaS preserves container deployment agility while achieving performance on par with native, hand-tuned builds—effectively breaking the longstanding “portability-at-the-cost-of-performance” bottleneck of conventional containers in HPC environments.

Technology Category

• Machine Learning: Hardware-aware ML
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Search and Optimization: Evolutionary Computation

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Search and Retrieval-Augmented AI: Vertical and domain-specific search
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web

Abstract

High-performance computing (HPC) systems and cloud data centers are converging, and containers are becoming the default method of portable software deployment. Yet, while containers simplify software management, they face significant performance challenges in HPC environments as they must sacrifice hardware-specific optimizations to achieve portability. Although HPC containers can use runtime hooks to access optimized MPI libraries and GPU devices, they are limited by application binary interface (ABI) compatibility and cannot overcome the effects of early-stage compilation decisions. Acceleration as a Service (XaaS) proposes a vision of performance-portable containers, where a containerized application should achieve peak performance across all HPC systems. We present a practical realization of this vision through Source and Intermediate Representation (IR) containers, where we delay performance-critical decisions until the target system specification is known. We analyze specialization mechanisms in HPC software and propose a new LLM-assisted method for automatic discovery of specializations. By examining the compilation pipeline, we develop a methodology to build containers optimized for target architectures at deployment time. Our prototype demonstrates that new XaaS containers combine the convenience of containerization with the performance benefits of system-specialized builds.

Problem

• Achieving performance portability in HPC containers without sacrificing hardware-specific optimizations
• Overcoming limitations of ABI compatibility and early-stage compilation decisions in containers
• Delaying performance-critical compilation decisions until target system specifications are known

Innovation

• Delaying performance-critical decisions until deployment
• Using source and IR containers for portability
• LLM-assisted automatic discovery of specializations