Federated Data Operator Management for Near-Memory Compute Nodes
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Solution Overview
Problem
Existing network-attached memory systems face delays due to inefficient management of data operators across near-memory compute (NMC) nodes, leading to poor resource utilization and performance degradation for client applications.
Innovation Solution
Implementing federated management of data operators across distributed NMC nodes, where data operators are loaded, scaled, and executed collectively based on data access patterns and performance metrics to optimize resource allocation and reduce iterative data traversals.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If data operators are managed independently at each NMC node, then each node can operate autonomously, but resource utilization is poor and performance degrades
Solution Approach 1:
The patent implements federated management that combines independent NMC node operations with centralized coordination. Data operators are managed across the federated system as a whole, allowing nodes to operate autonomously while sharing resources and information through the federation framework, thus improving resource utilization without sacrificing operational independence.
Solution Approach 2:
The federated management system creates a universal framework where data operators can serve multiple NMC nodes simultaneously. A single data operator instance can be shared across multiple nodes through the federation, enabling one operator to perform multiple functions and serve different nodes, thereby improving overall resource utilization.
2Productivity
If more data operators are loaded at each NMC node, then more operations can be performed locally, but resource constraints are exceeded
Solution Approach 1:
The patent implements dynamic data operator allocation where the federation manager continuously monitors resource usage and workload demands across NMC nodes. Data operators are dynamically loaded, unloaded, or migrated based on real-time conditions, allowing the system to adapt resource allocation to current needs without permanently exceeding resource constraints at any individual node.
Solution Approach 2:
The federated management system applies local quality by allowing different NMC nodes to have different sets of data operators based on their specific workload requirements. Each node receives the appropriate data operators needed for its local operations, while the federation ensures efficient distribution and sharing across the system, optimizing resource usage at each location.
3Adaptability or versatility
If data operators are frequently loaded and unloaded at NMC nodes, then resource allocation can adapt to changing demands, but overhead increases and performance decreases
Solution Approach 1:
The federated management system performs preliminary actions by pre-loading and pre-positioning data operators at NMC nodes based on predicted workload patterns and historical data. The federation manager anticipates future demands and prepares data operators in advance, reducing the need for frequent loading and unloading operations during actual execution, thereby decreasing management overhead and improving performance.
Solution Approach 2:
The patent implements continuity of useful action by maintaining data operators in a ready state across the federation rather than repeatedly loading and unloading them. The federated system keeps data operators available and continuously accessible across multiple nodes, eliminating the stop-start nature of frequent loading/unloading and reducing the associated overhead and performance degradation.
Data Source
AI summary
Examples described herein relate to federated management of data operators across multiple near-memory compute (NMC) nodes attached to memory devices in a network-attached memory system. Federated management includes loading, executing, and scaling data operators across the multiple NMC nodes together as a group. Examples include receiving a data access request from a client application and loading data operators in the multiple NMC nodes based on a data access pattern associated with the data access request. Examples include scaling the data operators based on performance metrics for the data operators or the multiple NMC nodes in correlation with client application performance. The multiple NMC nodes may dynamically scale the data operators based on request-load, execution frequency of data operators, resource availability, or other scaling strategies. Examples also include loading and scaling the data operators based on one or more of request characteristics or data operator characteristics.