Self-adjustable End-to-End Stack Programming Optimization
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Solution Overview
Problem
Current programming model optimization approaches across the entire stack are cumbersome and inefficient, requiring domain-specific expertise and ad hoc implementations that fail to consider interdependencies between layers, leading to suboptimal performance.
Innovation Solution
A self-adjusting, systematic approach for end-to-end programming model optimization that performs individual optimization loops across each layer, using plasticity variables and consolidation optimization functions to achieve near-optimal performance, and continuously monitors the model during runtime for real-time adjustments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If ad hoc optimization is performed for each layer individually, then domain-specific optimization can be achieved, but the process becomes immensely cumbersome and fails to consider interdependencies between layers
Solution Approach 1:
The patent merges individual layer optimizations into a unified end-to-end optimization framework. The system consolidates optimization across algorithm, toolchain, runtime, and hardware layers by establishing a unified objective function and shared parameter space, allowing simultaneous optimization of all layers while considering their interdependencies through the mapping between layer parameters.
2Manufacturing precision
If manual domain expertise is used for each layer optimization, then specialized knowledge can be applied, but the process requires immense manual effort and heuristic approaches
Solution Approach 1:
The system implements self-service optimization by automatically performing end-to-end optimization without requiring manual domain expertise at each layer. The unified framework automatically maps parameters across layers, formulates the optimization problem, and solves it through automated algorithms, eliminating the need for manual heuristics and reducing optimization time significantly.
Solution Approach 2:
The patent incorporates feedback mechanisms where the optimization process continuously monitors performance metrics from the programming model execution and uses this feedback to iteratively improve the unified objective function and parameter mappings. This closed-loop approach allows the system to learn from actual performance and refine optimizations automatically.
3Productivity
If optimizations consider interdependencies across all layers, then near-optimal end-to-end performance can be achieved, but the optimization process becomes systematically complex
Solution Approach 1:
The patent segments the complex end-to-end optimization problem into manageable components by dividing it into distinct layers (algorithm, toolchain, runtime, hardware) while maintaining their interconnections through parameter mappings. This segmentation allows the system to handle complexity systematically by optimizing each layer's contribution to the unified objective while considering cross-layer dependencies.
Data Source
AI summary
Systems and methods are provided for optimizing parameters of a system across an entire stack, including algorithms layer, toolchain layer, execution or runtime layer, and hardware layer. Results from the layer-specific optimization functions of each domain can be consolidated using one or more consolidation optimization functions to consolidate the layer-specific optimization results, capturing the relationship between the different layers of the stack. Continuous monitoring of the programming model during execution may be implemented and can enable the programming model to self-adjust based on real-time performance metrics. In this way, programmers and system administrators are relieved of the need for domain knowledge and are offered a systematic way for continuous optimization (rather than an ad hoc approach).