API Memory Pool Allocation for Parallel Processor Workloads
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
The complexity of memory management in parallel computing environments, particularly when different processors interact, often leads to inefficiencies and performance issues due to improper allocation or under-allocation of memory resources.
Innovation Solution
The implementation of application programming interfaces (APIs) that facilitate memory reservation, allocation, and management for parallel processing units (PPUs) such as GPUs, including stream ordered memory allocators to manage memory in a logical grouping and ensure efficient use across multiple software programs.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If memory is over-allocated to ensure sufficient resources for parallel processing operations, then reliability of memory availability is improved, but device complexity and resource waste increase
Solution Approach 1:
The patent introduces memory pools as intermediary structures between the memory hierarchy and parallel processing units. These pools act as mediators that manage memory allocation systematically, tracking which memory regions are allocated to which PPUs and their respective streams. This intermediary layer resolves the contradiction by providing structured oversight that ensures reliable memory availability while maintaining manageable complexity through centralized control mechanisms.
Solution Approach 2:
The patent segments memory into distinct pools that are logically grouped and assigned to specific parallel processing units or streams. This segmentation allows independent management of memory regions, enabling precise tracking of allocation status and facilitating efficient reuse of freed memory within the same pool. The segmentation approach improves reliability by ensuring dedicated memory resources while reducing overall complexity through modular organization.
2Device complexity
If memory is under-allocated to reduce resource waste, then device complexity is reduced, but productivity of parallel processing operations deteriorates
Solution Approach 1:
The patent implements preliminary action by pre-allocating memory pools and establishing allocation rules before parallel processing operations begin. Memory pools are created and configured in advance with defined capacities and assignment criteria. This preliminary setup enables rapid memory allocation during execution without complex runtime decisions, thereby maintaining low device complexity while ensuring sufficient memory resources are available to sustain high productivity throughout the processing operation.
Solution Approach 2:
The patent utilizes parameter changes by dynamically adjusting memory pool characteristics such as allocation size, assignment priorities, and reuse policies based on observed workload patterns. These parameter adjustments allow the system to optimize memory utilization for different parallel processing scenarios, ensuring adequate resources for high productivity while maintaining manageable complexity through adaptive rather than static management approaches.
3Productivity
If memory allocation is optimized for specific software programs, then productivity of those programs is improved, but adaptability to different programming models deteriorates
Solution Approach 1:
The patent implements universality by designing memory pools with multi-functional capabilities that can serve different programming models and software programs through a unified interface. The same memory pool infrastructure supports various parallel processing paradigms, allocation strategies, and program types without requiring separate specialized memory systems. This universal approach maintains adaptability to different programming models while achieving optimized productivity through consistent, efficient memory management principles applied across all workloads.
4Ease of operation
If memory management is simplified to reduce device complexity, then ease of operation is improved, but loss of information about memory allocation status increases
Solution Approach 1:
The patent incorporates feedback mechanisms within the memory pool system that continuously monitor and report allocation status, usage patterns, and availability information. This feedback is collected systematically from individual memory regions and aggregated at the pool level, providing comprehensive visibility into memory state without requiring complex manual tracking. The feedback loop enables automated decision-making for allocation and reuse while maintaining simplified operation through centralized information management that prevents data loss about allocation status.
Data Source
AI summary
Apparatuses, systems, and techniques to execute one or more application programming interface (API) functions to facilitate parallel computing. In at least one embodiment, one or more APIs are to indicate one or more storage locations using various novel techniques described herein.