MapReduce ML Algorithm Execution Plan Generation
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Solution Overview
Problem
Current implementations of machine learning algorithms require manual tuning and manual parallelization on specialized hardware, making it challenging to select an effective execution strategy for processing large datasets in a distributed network environment.
Innovation Solution
The system parses machine learning algorithms into statement blocks and automatically determines execution plans, using a declarative higher-level language to optimize their execution in a MapReduce environment, allowing for scalable processing of large datasets without manual low-level implementation details.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual tuning and manual parallelization are used on specialized hardware, then execution performance can be optimized, but the complexity and difficulty of implementation increases significantly
Solution Approach 1:
The system implements automatic execution plan generation that analyzes the ML algorithm and data characteristics to select optimal processing strategies without requiring manual intervention. The framework automatically parallelizes algorithms across distributed clusters, tuning parameters and determining execution strategies based on runtime conditions, thereby eliminating the need for manual optimization while maintaining high performance.
Solution Approach 2:
The patent introduces an intermediary execution framework that sits between the high-level ML algorithm specification and the low-level distributed system implementation. This framework translates algorithmic descriptions into optimized execution plans, handling the complexity of parallelization and resource allocation automatically, thus shielding users from implementation details while achieving efficient execution.
2Productivity
If distributed networks are used to process large datasets, then processing capacity and scalability improve, but the number of possible execution strategies increases making selection difficult
Solution Approach 1:
The system incorporates feedback mechanisms that monitor runtime conditions such as data distribution, cluster resource availability, and intermediate computation results. Based on this feedback, the execution framework dynamically adjusts and selects optimal execution strategies, automatically adapting to changing conditions without requiring manual intervention or pre-planning of execution paths.
Solution Approach 2:
The framework automatically varies execution parameters such as parallelization degree, data partitioning strategies, and algorithmic optimizations based on the specific characteristics of the ML algorithm and dataset. By dynamically changing these parameters, the system selects and adapts execution strategies that are optimal for each specific processing scenario without requiring manual configuration.
Data Source
AI summary
Systems and methods for processing Machine Learning (ML) algorithms in a MapReduce environment are described. In one embodiment of a method, the method includes receiving a ML algorithm to be executed in the MapReduce environment. The method further includes parsing the ML algorithm into a plurality of statement blocks in a sequence, wherein each statement block comprises a plurality of basic operations (hops). The method also includes automatically determining an execution plan for each statement block, wherein at least one of the execution plans comprises one or more low-level operations (lops). The method further includes implementing the execution plans in the sequence of the plurality of the statement blocks.