Tiered Memory Data Prefetching Based on Processing Urgency
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Solution Overview
Problem
Existing prefetching and caching techniques in tiered memory systems do not effectively utilize the different tiers of memory, leading to increased latency due to improper placement of data in higher or lower performance tiers.
Innovation Solution
Implementing prefetching and caching techniques that take into account the different tiers of memory by placing to-be-processed data close to processing threads based on processing urgency, using higher-performance tiers for urgent data and lower-performance tiers for less urgent data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data is placed in higher-performance memory tiers, then data processing speed is improved, but memory cost and complexity increase
Solution Approach 1:
The system performs preliminary actions by prefetching data into appropriate memory tiers before it is actually needed for processing. The manager analyzes the data set and generates instructions to load data subsets into specific memory tiers in advance, based on predicted processing urgency. This ensures that when data is needed, it is already in the optimal memory location, improving speed without requiring all data to constantly reside in high-performance memory.
Solution Approach 2:
The patent applies local quality by placing different data subsets in different memory tiers based on their specific access patterns and urgency. Hot data that requires frequent access is placed in higher-performance tiers, while cold data is placed in lower-performance tiers. This selective placement optimizes the overall system performance without uniformly increasing memory complexity across all data.
2Loss of energy
If data is placed in lower-performance memory tiers, then memory cost is reduced, but data access latency increases
Solution Approach 1:
The manager performs preliminary analysis of the data set to identify which data subsets will be needed and when. Based on this analysis, it generates instructions to prefetch data into appropriate memory tiers in advance. This preliminary action ensures that data is loaded into lower-performance tiers with sufficient lead time, so when processing occurs, the data is already positioned optimally, minimizing access latency while maintaining cost efficiency.
Solution Approach 2:
The system dynamically adjusts data placement between memory tiers based on changing access patterns and processing urgency. As data becomes more urgent to process, it can be moved from lower-performance to higher-performance tiers. This dynamic repositioning allows the system to optimize the balance between memory cost and access latency in real-time, rather than using static placement strategies.
3Ease of operation
If prefetching is performed without considering memory tiers, then data loading is simplified, but latency increases due to improper data placement
Solution Approach 1:
The patent changes the parameter of data placement by introducing memory tier awareness into the prefetching operation. Instead of simply loading data into a single memory space, the manager analyzes data characteristics and assigns different data subsets to different memory tiers based on their access urgency and performance requirements. This parameter change maintains operational simplicity from the application's perspective while dramatically improving latency through intelligent tiered placement.
Solution Approach 2:
The manager acts as an intermediary between the application's simple prefetching request and the complex tiered memory system. It receives high-level prefetching instructions, analyzes the data set, determines optimal memory tier placements, and generates the appropriate load instructions. This intermediary layer shields applications from memory tier complexity while ensuring optimal data placement, thus maintaining ease of operation while reducing latency.
Data Source
AI summary
Methods, systems, and devices for loading data in a tiered memory system are described. A respective allocation of computing resources may be determined for each node in a cluster, where at least one of the nodes may include multiple memory tiers, and a data set to be processed by the nodes may be analyzed. Based on the allocation of computing resources and the analysis of the data set, respective data processing instructions indicating respective portions of the data set to be processed by respective nodes may be generated and sent to the respective nodes. The respective data processing instructions may also indicate a respective distribution of subsets of the respective portions of the data set across the multiple memory tiers at the respective nodes.