NUMA Memory API Prefetching for Latency Reduction
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Solution Overview
Problem
Current memory management operations in parallel computing platforms are inefficient in terms of time, power, and computing resources, 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 querying 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 in parallel computing platforms, then basic memory access and storage functions are provided, but significant time, power, and computing resources are consumed
Solution Approach 1:
The patent implements preliminary action through the `cudaMemPrefetchAsync` function, which allows data to be fetched in advance from remote memory locations before they are needed. This prefetching mechanism reduces the time spent on memory management operations by performing memory access preparation ahead of time, thereby resolving the contradiction between productivity and time loss.
2Speed
If data is stored in remote memory locations without optimization, then memory allocation is simplified, but access latency increases
Solution Approach 1:
The patent applies preliminary action by implementing a prefetching mechanism that fetches data from remote memory locations in advance. The `cudaMemPrefetchAsync` function enables applications to specify preferred storage locations and prefetch data before it is needed, thereby reducing access latency and improving data access speed.
Solution Approach 2:
The patent implements local quality through the concept of preferred storage locations associated with specific memory ranges. By allowing applications to indicate preferred NUMA nodes or memory regions for storing data, the system optimizes data placement locally for each memory range, improving access speed for subsequent operations.
3Productivity
If memory management operations are performed without NUMA awareness, then implementation is simpler, but computing resources are wasted
Solution Approach 1:
The patent implements universality through the `cudaMemAdvise` function, which serves multiple purposes: it allows applications to indicate preferred storage locations, specify caching behavior, and provide hints for memory placement. This multi-functional approach improves resource utilization efficiency while maintaining a unified and manageable API interface.
Data Source
AI summary
Apparatuses, systems, and techniques to store information within 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 stored within 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.