Fluid Memory Regions for HPC Latency Reduction
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Solution Overview
Problem
Existing memory management technologies in High Performance Computing (HPC) systems face challenges in efficiently managing dynamic memory regions, leading to increased latency and costs due to passive memory management that requires explicit requests for deallocation, and inefficient use of memory resources as data importance diminishes over time.
Innovation Solution
The introduction of 'fluid' dynamic memory regions that are automatically deallocated upon expiration of a validity time interval and allocated based on data importance, allowing for improved resource utilization and reduced latency by automatically managing memory without requiring explicit requests from computing entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If passive memory management is used requiring explicit requests for deallocation, then memory can be retained for extended periods, but latency increases and memory resources are inefficiently utilized
Solution Approach 1:
The memory management system automatically deallocates fluid memory regions based on data age and importance criteria without requiring explicit requests from computing entities. The system self-monitors memory regions and performs deallocation autonomously, eliminating the need for manual intervention and reducing latency associated with explicit deallocation requests.
Solution Approach 2:
The system dynamically adjusts memory retention policies by evaluating data age and importance metrics. Memory regions transition from retained to deallocated state based on dynamic criteria rather than static timing, allowing the system to adapt to changing data values and computational needs in real-time.
2Reliability
If memory regions are retained for extended periods, then data availability is maintained, but memory resource utilization becomes inefficient
Solution Approach 1:
The system changes the parameter of memory retention duration based on data importance and age. Rather than using a fixed retention period, the system adjusts retention dynamically - keeping high-importance data longer while automatically deallocating low-importance or stale data, thereby optimizing both availability and resource utilization.
Solution Approach 2:
Different memory regions are treated differently based on their specific characteristics (data importance, age, access patterns). Each memory region receives a customized retention policy tailored to its local qualities rather than applying a uniform retention strategy across all memory, improving overall resource efficiency.
3Productivity
If fluid memory regions are automatically deallocated, then memory leaks are prevented and resource utilization improves, but complex memory management logic is introduced
Solution Approach 1:
The system automatically discards fluid memory regions when they meet deallocation criteria (age threshold, low importance) and recovers the memory resources for reuse. This systematic discard and recover process prevents memory leaks and improves utilization while maintaining manageable complexity through rule-based automation.
Solution Approach 2:
The memory management system continuously monitors memory region characteristics (age, importance metrics) and uses this feedback to make automated deallocation decisions. The feedback loop enables the system to adapt to changing conditions and maintain optimal memory utilization without complex manual intervention.
Data Source
AI summary
Examples of the presently disclosed technology provide new memory management systems and methods that improve dynamic memory region utilization by: (1) creating a new class/type of dynamic memory regions—i.e., “fluid” dynamic memory regions—that are automatically relinquished to a free pool of dynamic memory regions upon expiration of a “fluid memory validity time interval;” and (2) responsive to requests for dynamic memory regions, allocating “fluid” dynamic memory regions when levels of importance for data to be stored in the requested dynamic memory regions fall below a “data-oriented priority-fluidity threshold.”