ML Flow Scheduling via Execution Graph Analysis
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Solution Overview
Problem
Current machine learning implementations lack optimal execution on distributed platforms due to blind assumptions about program execution, leading to suboptimal performance and resource contention, particularly in long-running processes where opportunities for optimization are overlooked.
Innovation Solution
The proposed system utilizes an execution graph to determine the parallel execution environment, optimizing resource allocation and scheduling across multiple systems, processors, and memory mapping by analyzing execution patterns and hardware constraints to minimize CPU overlap and cache misses, and reconfiguring layouts to achieve maximal resource utilization.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If blind assumptions about program execution are made for distributed computation, then generalization is achieved, but optimal execution performance is lost
Solution Approach 1:
The system performs preliminary analysis of machine learning workloads to identify repetitive operations and data access patterns before execution. This allows the scheduler to pre-optimize task allocation and resource assignment, transforming blind generalization into informed decision-making that maintains both adaptability and performance
Solution Approach 2:
The system implements feedback mechanisms that monitor actual execution performance and use this information to refine scheduling decisions. By continuously learning from execution outcomes, the system adapts its resource allocation strategies to achieve optimal performance while maintaining generalization across different workloads
2Ease of operation
If operating system scheduling is used to manage resource contention, then basic resource management is achieved, but maximal optimization is not realized
Solution Approach 1:
The system introduces an intermediary layer between the operating system scheduler and the machine learning workload. This intermediary analyzes workload characteristics and provides optimized scheduling hints to the OS, enabling maximal optimization while maintaining the ease of operation provided by standard OS scheduling mechanisms
Solution Approach 2:
The system dynamically changes scheduling parameters based on workload characteristics, such as task priority, resource requirements, and execution patterns. By adjusting these parameters in real-time, the system achieves maximal optimization beyond what fixed OS scheduling can provide
3Productivity
If optimization overhead is incurred at the beginning of long-running processes, then performance improvement is achieved, but the overhead time is currently ignored
Solution Approach 1:
The system performs optimization actions at the beginning of long-running machine learning processes by analyzing workload patterns and pre-configuring resource allocation. This preliminary optimization reduces execution time throughout the remainder of the process, making the initial overhead time worthwhile
Data Source
AI summary
A distributed machine learning optimization flow processing engine is proposed. The processing engine takes into account the structure of the programming to assign proper allocation within a distributed computing infrastructure. The processing engine also takes into account availability and loads of the different computing elements within the distributed infrastructure to maximize their utilization according to the software being executed.