ML Pipeline Data Access Interface Selection for Zero-Copy Throughput
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Solution Overview
Problem
ML pipelines face challenges due to memory constraints, GPU stalls, and data redundancy, particularly when executing concurrently, leading to inefficient utilization of memory and storage resources.
Innovation Solution
An architecture that optimizes data access interfaces by discovering and selecting the best-performing interfaces for in-memory object stores and persistent distributed storage using a Common Store Configuration (CSC) module, Resource Performance Analyzer (RPA) module, and Zero-Copy Control Engine to minimize data duplication and resource consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If multiple data access interfaces are provided for in-memory object stores and persistent distributed storage, then data access performance can be optimized for different workload characteristics, but system complexity increases due to interface selection and configuration management
Solution Approach 1:
The system automatically discovers available data access interfaces and selects the optimal one based on workload characteristics without requiring manual configuration. The interface selection is performed autonomously by analyzing workload patterns and choosing the most suitable interface from multiple available options.
Solution Approach 2:
The system changes the parameter of interface selection based on workload characteristics such as data size, access patterns, and performance requirements. Different interfaces are selected dynamically depending on the specific workload parameters to optimize performance.
2Reliability
If data is stored in both in-memory object stores and persistent distributed storage, then data availability and persistence are improved, but memory consumption increases due to data redundancy
Solution Approach 1:
The system creates copies of data in both in-memory object stores and persistent distributed storage to ensure data availability and persistence. Multiple copies are maintained across different storage layers to protect against data loss and ensure continuous access.
Solution Approach 2:
The in-memory object store is nested within the persistent distributed storage system, with the in-memory store acting as a cache layer. This nested structure allows data to be accessed quickly from memory while maintaining persistence in the underlying distributed storage system.
3Productivity
If concurrent ML pipeline executions are supported, then system throughput increases, but memory constraints are exceeded leading to intermittent failures
Solution Approach 1:
The system segments data access operations into different interfaces and storage layers, allowing concurrent pipeline executions to access data through appropriate interfaces without interfering with each other. This segmentation enables parallel processing while maintaining memory constraints.
Solution Approach 2:
The system dynamically adjusts memory allocation and interface selection based on real-time workload conditions during concurrent executions. Memory constraints are managed dynamically by selecting appropriate data access interfaces that fit within available memory resources.
4Ease of operation
If data is read from persistent distributed storage using traditional interfaces, then data can be accessed, but CPU resources are consumed by data copying operations
Solution Approach 1:
The system replaces traditional mechanical data copying operations with zero-copy data access interfaces. This substitution eliminates the need for CPU-intensive data copying by allowing direct access to data in persistent distributed storage without intermediate copies.
Solution Approach 2:
The system uses zero-copy techniques to access data from persistent distributed storage without creating intermediate copies. This approach reduces CPU resource consumption by avoiding the copying process while maintaining data access capability.
Data Source
AI summary
Systems and methods are provided for utilization of optimal data access interface usage in machine learning pipelines. Examples of the systems and methods disclosed herein include identifying data access interfaces comprising at least a first data access interface for a persistent storage distributed across a plurality of storage nodes and at least a second data access interface for an in-memory object store, and receiving, from a compute node, a data operation request as part of a machine learning pipeline. Additionally, performance metrics are obtained for the plurality of access interfaces, and based on a type of data operation request, the data operation is executed using a data access interface selected from the plurality of data access interface based on the performance metrics and providing an object handle to the compute node.