HPC CONTAINER ADAPTABILITY ANALYSIS
coMtainer: Compilation-assisted HPC Container Images with Enhanced Adaptability
The paper introduces coMtainer, a compilation-assisted image transformation framework addressing the adaptability issue in HPC container images. By embedding build-time information, coMtainer enables remote HPC systems to specialize and rebuild containers using native toolchains and libraries, preserving image neutrality while optimizing performance. This framework also unlocks advanced compiler optimizations like LTO and PGO, demonstrating significant performance recovery and improvements across various real-world HPC applications.
Executive Impact: Bridging the Adaptability Gap
Current HPC container workflows often lead to suboptimal performance (adaptability issue) because images are built generically without knowledge of target HPC system specifics (toolchains, libraries, hardware stacks), leading to a mismatch between the generic image and the specialized HPC environment.
coMtainer is a compilation-assisted framework that embeds build-time data (source, intermediate representations, build configurations) into container images. This allows remote HPC systems to rebuild and optimize applications with native toolchains and libraries, enabling system-specific adaptations and advanced compiler optimizations like LTO and PGO, without user intervention.
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Average Performance Recovery (x86-64)
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Average Performance Recovery (AArch64)
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Extra Performance Gain (Optimized)
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Low Image Overhead
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Introduction
Motivation
Design
Evaluation
Introduction to coMtainer
The increasing interconnectivity and diversity of HPC systems demand efficient application migration and adaptation. While containers simplify deployment, they often fail to deliver optimal performance due to generic builds missing out on system-specific software advantages, a challenge termed the adaptability issue.
coMtainer addresses this by embedding build-time information into images, allowing HPC systems to specialize and rebuild containers with native toolchains and libraries, ensuring optimized execution without user involvement.
Motivation Behind coMtainer
The core problem is the mismatch between generically built container images and specific target HPC systems. Users build images on local machines with default toolchains, leading to performance degradation on remote, highly optimized HPC systems. Creating system-specific images for every target is infeasible and costly.
coMtainer aims to bridge this gap by enabling system-side specialization while preserving image portability. The goal is to allow users to publish generic images, while the system handles the system-specific optimizations.
coMtainer System Design
coMtainer introduces an analysis procedure during the user-side build process to collect intermediate build-time data. This data is then embedded into an extended OCI-compliant image.
Remote HPC systems pull this extended image, then use the embedded data to rebuild and redirect the container using native toolchains and optimized libraries, producing a system-specific, optimized image. This process supports advanced optimizations like LTO and PGO.
Evaluation Results
Experiments on x86-64 and AArch64 HPC systems with various applications (HPL, HPCG, LULESH, LAMMPS, OpenMX) demonstrate coMtainer's effectiveness. It achieved an average performance recovery of 96.3% on x86-64 and 66.5% on AArch64 compared to original images, matching native builds.
Furthermore, LTO and PGO yielded an additional 8% performance boost on x86-64. The cache layer overhead for storing build-time data is minimal, typically less than 7.1% of the original image size.
96.3%
Average Performance Recovery on x86-64
coMtainer recovers nearly all performance lost due to generic image builds on x86-64 HPC systems, matching native build performance.
coMtainer Workflow: User & System Sides
Developer creates Source Code
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User side builds Container (Env/Base Images)
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coMtainer Toolset embeds Build-time Data
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OCI Image Repository stores Extended Image
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Remote HPC System pulls Extended Image
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coMtainer Rebuild with Native Toolchains
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coMtainer Redirect for Optimized Runtime
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Optimized Image deployed on HPC System
| Optimization Level | Performance Improvement (Relative to Original) | Key Actions |
|---|---|---|
| Original | 0% | Generic build with default toolchain. |
| Native | +96.3% | Built and run natively on target HPC system. |
| Adapted (coMtainer) | +96.3% | coMtainer adapts with target system's software stack. |
| Optimized (coMtainer + LTO/PGO) | +105.9% | coMtainer adds LTO and PGO for extra gains. |
coMtainer's adapted images achieve performance comparable to native builds, and further optimization with LTO/PGO provides additional benefits. Data is illustrative based on Figure 3 and 9 of the paper.
Large-Scale Application Performance: LAMMPS & OpenMX
For large applications like LAMMPS and OpenMX, coMtainer demonstrates significant performance gains. On the x86-64 system, LAMMPS showed a 253% improvement and OpenMX achieved a 99.7% improvement compared to original generic images. This highlights coMtainer's ability to unlock substantial benefits for complex HPC workloads by ensuring deep system adaptation and optimization. The gains are attributed to utilizing specialized MPI libraries and native compiler toolchains, resolving issues like communication overheads present in generic builds.
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Estimated Annual Savings
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Your Path to Optimized HPC
A typical coMtainer implementation journey, tailored to your enterprise's unique HPC landscape and specific application needs.
Discovery & Assessment
Initial consultation to understand current HPC container challenges, infrastructure, and performance bottlenecks. Define key objectives and success metrics for coMtainer integration.
Pilot & Customization
Deploy coMtainer framework in a pilot environment, integrate with a subset of key applications, and customize build-time data capture and system adapters for specific HPC systems.
Full-Scale Rollout & Training
Expand coMtainer to all critical HPC applications and user groups. Provide comprehensive training for your team on advanced optimization techniques and monitoring.
Continuous Optimization & Support
Ongoing monitoring, performance analysis, and iterative refinement of coMtainer configurations. Access to expert support for new hardware, compilers, and application updates.
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