Online binary continuous optimization system and method supporting multi-instruction-set architecture and application

By supporting an online binary continuous optimization system with multiple instruction set architectures, the problem of modern software systems being unable to maintain performance over long periods of operation has been solved. This system enables adaptive, low-overhead cross-platform optimization, improving program performance and system stability.

CN121478291APending Publication Date: 2026-02-06EAST CHINA NORMAL UNIV
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Patent Information

Application Number
CN202511557024.1
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-10-29
Publication Date
2026-02-06

AI Technical Summary

Technical Problem

Existing technologies cannot effectively combine general online binary optimization capabilities, continuous adaptive optimization, and low runtime overhead, resulting in modern software systems being unable to maintain high performance over long periods of operation.

Method used

Design an online binary continuous optimization system that supports multi-instruction set architecture, including a program behavior information acquisition module, an offline binary optimization module, an online code replacement module, and an old version code recycling module. Continuous optimization is achieved through adaptive information acquisition, cross-platform optimization, and secure code replacement.

Benefits of technology

It enables continuous adaptive optimization in modern software systems, reduces runtime overhead, improves program performance, maintains system stability, and adapts to complex and dynamically changing operating environments.

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Abstract

The invention discloses an online binary continuous optimization system supporting a multi-instruction-set architecture. The online binary continuous optimization system comprises a program behavior information acquisition module, an offline binary optimization module, an online code replacement module, an old version code recovery module and a program stage change detection and decision module, the program behavior information acquisition module realizes information acquisition during general operation through a self-adaptive dual-mode design; the offline binary optimization module converts the collected dynamic information into an intermediate representation, and performs optimization on the intermediate representation; the online code replacement module controls the process to start and stop and replaces the optimized fragment; the old version code recovery module detects and recovers old version codes through a runtime memory management mechanism; the program stage change detection and decision-making module monitors program performance index changes, identifies stage behavior switching and realizes continuous adaptive optimization of a closed loop. The invention also discloses an optimization method realized by using the system, and the method has a wide application prospect.
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