Type Inference Optimization via Selective Bounds Propagation

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Solution Overview

Problem

The conventional process of propagating bounds for inference variables from nested method invocation contexts to outer method invocation contexts is computationally expensive and inefficient, leading to increased time and resource consumption during the compilation process.

Innovation Solution

Optimizing type inference by selectively propagating bounds for a first subset of inference variables while avoiding the propagation of bounds for a second subset, based on specific criteria such as resolution dependency and applicability, to reduce the computational burden and improve efficiency.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Reliability

If bounds for all inference variables are propagated from nested method invocation contexts to outer method invocation contexts, then type inference consistency is maintained, but compilation time and computational resources increase significantly

Engineering Contradiction:
Improvetype inference consistencyVSAvoidcompilation time
Core Design Contradiction:
ReliabilityVSLoss of time

Solution Approach 1:

The patent segments the set of inference variables into two distinct subsets: those whose bounds should be propagated and those that should not. This segmentation allows the compiler to selectively propagate bounds only for variables that contribute to type inference consistency, while excluding variables that would unnecessarily increase compilation time. The segmentation is based on analyzing the dependency relationships and usage patterns of inference variables within nested method invocations.

Inventive Principle:
Principle #1Segmentation

Solution Approach 2:

The patent applies local quality by treating different inference variables differently based on their specific characteristics and roles in the type inference process. Rather than applying a uniform propagation rule to all inference variables, the compiler evaluates each variable's bounds propagation needs individually or in groups, applying propagation only where it serves a purpose for type consistency. This localized approach optimizes the balance between consistency and efficiency.

Inventive Principle:
Principle #3Local quality

2Loss of information

If bounds for all inference variables are propagated through nested method invocations, then complete type information is available, but computational resources and processing overhead increase

Engineering Contradiction:
Improvetype information completenessVSAvoidcomputational resources
Core Design Contradiction:
Loss of informationVSUse of energy by moving object

Solution Approach 1:

The patent extracts and removes from the propagation process those inference variables whose bounds do not contribute to essential type information. By identifying and excluding redundant or non-essential variables from propagation, the compiler maintains the necessary type information completeness while significantly reducing the computational burden associated with processing and propagating unnecessary bounds data through nested method invocations.

Inventive Principle:
Principle #2Taking out (Extraction)

Solution Approach 2:

The patent applies partial action by propagating bounds for only a subset of inference variables rather than all variables. This partial propagation is sufficient to maintain type information completeness for the purposes of correct type inference, while avoiding the excessive computational resources that would be required to propagate bounds for every single inference variable through the nested method invocation chain.

Inventive Principle:
Principle #16Partial or excessive action

3Productivity

If selective propagation of inference variable bounds is implemented, then compilation efficiency improves, but complexity of the type inference algorithm increases

Engineering Contradiction:
Improvecompilation efficiencyVSAvoidalgorithm complexity
Core Design Contradiction:
ProductivityVSDevice complexity

Solution Approach 1:

The patent applies preliminary action by performing analysis and classification of inference variables before the actual bounds propagation process. The compiler identifies and categorizes which variables should have their bounds propagated and which should not, based on their roles and dependencies in the type inference context. This preliminary classification simplifies the subsequent propagation step, as the decision logic has already been established, reducing the apparent complexity during execution.

Inventive Principle:
Principle #10Preliminary action

Solution Approach 2:

The patent introduces an intermediary analysis phase that acts as a mediator between the type inference requirements and the bounds propagation mechanism. This intermediary layer analyzes the nested method invocation context, determines dependency relationships, and generates a propagation plan that guides which bounds should be propagated. While this adds an intermediate step, it simplifies the overall algorithm by providing clear decision rules rather than requiring complex real-time evaluations during propagation.

Inventive Principle:
Principle #24Intermediary (Mediator)

Data Source

PatentUS11163545B2Type inference optimization
Publication Date: 2021.11.02 ORACLE INT CORP
  • US11163545B2 patent drawing
  • US11163545B2 patent drawing
  • US11163545B2 patent drawing

AI summary

Operations include (a) identifying bounds corresponding to two or more inference variables corresponding to a nested method invocation context, (b) determining that resolution of a first inference variable can be determined as a function of a resolution of a second inference variable, (c) propagating bounds corresponding to the second inference variable from the nested method invocation context to an outer method invocation context without propagating bounds corresponding to the first inference variable, (d) resolving a constraint set to resolve the second inference variable, and (e) resolving the first inference variable based on the resolution of the second inference variable.