NUMA Memory API Prefetching for Parallel Computing Latency
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current memory management operations in parallel computing platforms are inefficient in terms of time, power, and resource utilization, particularly when accessing and storing data across non-uniform memory access (NUMA) nodes.
Innovation Solution
The implementation of a set of APIs that allow for managed memory operations, including setting preferred memory locations, prefetching data, and retrieving memory attributes, to optimize memory access and storage across NUMA nodes, enabling more efficient use of resources by specifying NUMA nodes as preferred storage locations and prefetching data to reduce latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional memory management operations are used, then memory access and storage can be performed, but significant time, power, and computing resources are consumed
Solution Approach 1:
The patent implements prefetching mechanisms that load data into memory before it is actually needed by the CPU. This preliminary action reduces the time when data must be fetched during critical operations, thereby improving memory management efficiency and reducing time consumption. The system proactively identifies and loads frequently accessed data structures into optimal memory locations ahead of time.
Solution Approach 2:
The patent employs dynamic memory management techniques where the system can adaptively allocate and deallocate memory resources based on real-time workload requirements. Memory pages are dynamically swapped between physical and virtual memory spaces, and memory locations are dynamically adjusted based on access patterns, optimizing the balance between speed and resource usage.
2Productivity
If traditional memory management operations are used, then memory access and storage can be performed, but significant power consumption occurs
Solution Approach 1:
By prefetching data into optimal memory locations before it is needed, the system reduces the frequency of high-power memory access operations. This preliminary loading of data minimizes subsequent power-intensive read/write operations, thereby reducing overall power consumption while maintaining memory management efficiency.
Solution Approach 2:
The patent implements NUMA-aware memory management that assigns different memory access characteristics to different CPU cores based on their physical proximity to memory controllers. Local memory regions are optimized for specific cores, reducing cross-node memory accesses that consume more power, thus optimizing power usage without sacrificing productivity.
3Productivity
If traditional memory management operations are used, then basic memory access can be performed, but computing resources are not optimized
Solution Approach 1:
The patent implements a unified memory management framework that handles multiple memory access patterns and scenarios through a single cohesive system. The memory manager performs multiple functions including prefetching, cache management, NUMA awareness, and dynamic allocation within a unified architecture, optimizing resource utilization while managing complexity through integration rather than separate specialized components.
Solution Approach 2:
The system incorporates feedback mechanisms that continuously monitor memory access patterns, CPU workload distribution, and memory usage statistics. This feedback information is used to dynamically adjust memory management policies, optimizing computing resource allocation in real-time based on actual system behavior rather than static configurations.
4Productivity
If data is accessed across NUMA nodes, then parallel computing can proceed, but latency increases
Solution Approach 1:
The patent implements prefetching that proactively loads data into the closest available NUMA node before it is needed. By anticipating data requirements and pre-loading them into optimally positioned memory, the system eliminates latency during critical parallel computing operations, maintaining high data access speeds across distributed memory architectures.
Solution Approach 2:
The system assigns different memory access optimization strategies to different NUMA nodes based on their spatial relationship with executing CPU cores. Local memory regions are cached more aggressively and with different policies depending on proximity to active cores, optimizing data access speed for parallel operations while managing the complexity of heterogeneous memory access patterns.
Data Source
AI summary
Apparatuses, systems, and techniques to cause information to be read from one or more non-uniform memory access (NUMA) storages. In at least one embodiment, one or more circuits are to perform an application programming interface (API) to cause information to be read from one or more NUMA storages or one or more graphics processor unit (GPU) physical storages based, at least in part, on one or more indicators to be indicated by one or more users of the API.