Towards Online Code Specialization of Systems
Vaastav Anand, Deepak Garg, Antoine Kaufmann

TL;DR
This paper proposes a novel approach to optimize low-level systems through online code specialization using JIT compilation, enabling performance improvements with minimal developer effort.
Contribution
It introduces a new method for online system code specialization with JIT, demonstrating feasibility and performance gains for low-level systems like network stacks.
Findings
Online specialization improves system performance.
JIT-based approach reduces need for complex cost models.
Low developer effort required for effective specialization.
Abstract
Specializing low-level systems to specifics of the workload they serve and platform they are running on often significantly improves performance. However, specializing systems is difficult because of three compounding challenges: i) specialization for optimal performance requires in-depth compile-time changes; ii) the right combination of specialization choices for optimal performance is hard to predict a priori; and iii) workloads and platform details often change online. In practice, benefits of specialization are thus not attainable for many low-level systems. To address this, we advocate for a radically different approach for performance-critical low-level systems: designing and implementing systems with and for runtime code specialization. We leverage just-in-time compilation to change systems code based on developer-specified specialization points as the system runs. The JIT…
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Taxonomy
TopicsModel-Driven Software Engineering Techniques · Advanced Software Engineering Methodologies · Software Engineering Research
