Decision Tree Ensemble Compilation for Low Latency
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Solution Overview
Problem
Decision tree ensembles, such as random forests, face computational inefficiencies due to the high cost of evaluating multiple trees for each prediction, leading to increased latency and poor performance on pipelined processors, especially in applications requiring low latency like search ranking.
Innovation Solution
Compiling decision trees into machine code either offline before runtime or through just-in-time compilation, where statistics are collected to optimize branch predictions and arrange code for efficient execution, reducing the computational overhead and improving performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If decision tree ensembles are evaluated by executing multiple trees for each prediction, then classification accuracy is improved, but evaluation time and computational overhead increase
Solution Approach 1:
The patent applies preliminary action by pre-compiling decision trees into machine code before runtime. The compilation process translates high-level decision tree representations into optimized machine code that can be executed efficiently. This pre-processing step stores the compiled machine code for later use, eliminating the need to interpret and execute multiple trees during actual predictions, thus reducing evaluation time while maintaining classification accuracy.
2Speed
If decision trees are compiled into machine code offline before runtime, then evaluation speed is improved, but compilation time and initial processing overhead increase
Solution Approach 1:
The patent performs the compilation action in advance, before the decision tree ensemble needs to be used for predictions. By compiling decision trees offline into machine code and storing them, the system eliminates the time-consuming interpretation step during runtime. The initial compilation time is a one-time cost that enables significantly faster evaluation speeds for all subsequent predictions.
3Adaptability or versatility
If decision trees are evaluated in interpreted mode dynamically, then adaptability to different computing systems is improved, but execution efficiency and performance decrease
Solution Approach 1:
The patent creates machine code copies of decision trees that can be executed efficiently on target computing systems. Instead of interpreting the original high-level decision tree representations during runtime, the system uses pre-compiled machine code copies that are optimized for execution. This copying approach maintains adaptability to different computing systems while dramatically improving execution efficiency.
Data Source
AI summary
In one embodiment, a decision tree is evaluated in interpreted mode while statistics are collected. The decision tree is then represented as source code, and each decision in the decision tree is annotated with instructions determined based on the collected statistics. The source code is compiled into machine code, and the machine code is optimized based on the instructions annotating each decision in the decision tree.