Graph-Based Memory Deallocation in CUDA Parallel Computing

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Solution Overview

Problem

Existing techniques for deallocating memory using data structures that represent operations and dependencies are inefficient, often requiring additional computing resources and failing to effectively manage memory outside of these structures, particularly in parallel computing platforms like CUDA.

Innovation Solution

The implementation of a graph-based data structure that utilizes MemAlloc and MemFree nodes to manage memory allocation and deallocation, allowing for efficient memory reuse and sharing across graphs, with explicit node creation and stream capture interfaces to track and reorder memory operations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If memory is deallocated outside of a data structure representing operations and dependencies, then memory can be managed flexibly, but additional computing resources are required

Engineering Contradiction:

Improvememory management flexibilityVSAvoidcomputing resource consumption

Core Design Contradiction:

Adaptability or versatilityVSUse of energy by moving object

Solution Approach 1:

The patent introduces an intermediary data structure (graph) that represents operations and dependencies among memory operations. This graph serves as a mediator between memory allocation/deallocation operations and the underlying memory management system, enabling efficient tracking and management of memory lifecycle without requiring additional computing resources for external management.

Inventive Principle:

Principle #24Intermediary (Mediator)

Solution Approach 2:

The memory management system performs self-service by automatically tracking memory operations through the graph data structure. The system autonomously manages memory allocation, deallocation, and reuse based on the operational dependencies encoded in the graph, eliminating the need for external computing resources to intervene in memory management tasks.

Inventive Principle:

Principle #25Self-service

2Reliability

If memory operations are tracked outside of the graph data structure, then operational control is maintained, but synchronization requirements increase

Engineering Contradiction:

Improveoperational controlVSAvoidsynchronization overhead

Core Design Contradiction:

ReliabilityVSLoss of time

Solution Approach 1:

The patent merges memory operation tracking directly into the graph data structure by representing memory allocation, deallocation, and dependency relationships as nodes and edges within the graph. This integration eliminates separate tracking mechanisms and their associated synchronization overhead, while maintaining complete operational control through the unified graph representation.

Inventive Principle:

Principle #5Merging (Combining)

3Ease of manufacture

If memory is not reused across graphs, then memory management is simpler, but resource utilization efficiency decreases

Engineering Contradiction:

Improvememory management simplicityVSAvoidresource utilization efficiency

Core Design Contradiction:

Ease of manufactureVSProductivity

Solution Approach 1:

The patent implements universal memory management by enabling memory allocations to be shared across multiple graphs through the graph data structure. The system tracks reference counts and dependencies in the graph to determine when shared memory can be safely reused, allowing the same memory resources to serve multiple computational graphs and improving overall resource utilization efficiency.

Inventive Principle:

Principle #6Universality (Multi-functionality)

Data Source

PatentUS20230005097A1Memory deallocation using graphs

Publication Date: 2023.01.05 NVIDIA CORP

AI summary

Apparatuses, systems, and techniques to generate one or more graph code nodes to deallocate memory. In at least one embodiment, one or more graph code nodes to deallocate memory are generated, based on, for example, CUDA or other parallel computing platform code.