Causality Detection Between Parallel Application Tasks
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Solution Overview
Problem
In multitasking applications, identifying causal relationships between tasks is challenging due to parallel execution and interdependence, making it difficult to predict total execution time and manage compute resources effectively.
Innovation Solution
The implementation of instrumentation points in tasks allows for the determination of causal relationships by observing delays induced by one task on another, using a sequence predictor model to analyze inter-arrival times and predict timestamps, and generating a profile for processor core setting or allocation based on these relationships.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If tasks execute in parallel on multiple processors, then productivity is improved, but difficulty of detecting and measuring causal relationships worsens
Solution Approach 1:
The system installs instrumentation points in tasks before execution to preemptively capture timing data. This preliminary instrumentation enables subsequent causal analysis by recording inter-arrival times and timestamps that would otherwise be unavailable in parallel executing tasks
Solution Approach 2:
The system introduces an intermediary causal relationship determination module that observes delays between instrumentation points. This intermediary component translates raw timing data from parallel tasks into meaningful causal relationships, bridging the gap between parallel execution and causal detection
2Measurement precision
If instrumentation points are installed in tasks, then measurement precision of causal relationships is improved, but device complexity increases
Solution Approach 1:
The instrumentation points automatically record their own timestamps and inter-arrival times without external intervention. Each instrumentation point serves itself by capturing execution timing data, reducing the need for complex external measurement infrastructure
Solution Approach 2:
The system changes the parameter being measured from qualitative causal relationships to quantitative inter-arrival times and timestamps. This parameter transformation enables precise measurement of causal relationships through numerical timing data that can be analyzed statistically
Data Source
AI summary
An electronic device includes: one or more processors; a memory storing instructions configured to cause the one or more processors to: install instrumentation points in respective tasks of an application, the instrumentation points including a source instrumentation point installed in a source task and a target instrumentation point installed in a target task, wherein the source task and the target task are configured to execute in parallel on the one or more processors, and wherein each task includes a respective sequence of instructions executable by the one or more processors, and determine a measure of a causal relationship between the source instrumentation point and the target instrumentation point based on observation of a delay in the target instrumentation point induced by a delay amount generated by the source instrumentation point.