Load Coalescing Prediction Circuitry for Latency Reduction
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Solution Overview
Problem
Current data processing systems face inefficiencies in handling load requests due to increased latency caused by coalescing processing, which may not be offset by the benefits of improved memory access bandwidth, especially when only a small number of load instructions can be coalesced.
Innovation Solution
The implementation of coalescing prediction circuitry to determine whether a load request will coalesce with other requests based on previous handling, allowing for the suppression of non-coalescing load requests and optimizing the processing path through the use of a bypass path and probabilistic data structures like Bloom filters to reduce storage requirements and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If coalescing processing is applied to all load requests, then memory access bandwidth is improved, but processing latency increases
Solution Approach 1:
The coalescing prediction circuitry performs preliminary assessment of load requests before they enter the main coalescing processing pipeline. By predicting in advance whether a load request is likely to coalesce with others, the system can prepare appropriate processing paths ahead of time, reducing the latency impact of coalescing operations while maintaining bandwidth benefits.
Solution Approach 2:
The load request processing is segmented into different paths based on coalescing predictions. High-probability coalescing requests are directed to the coalescing processing pipeline to maximize memory bandwidth utilization, while low-probability requests take a direct path to minimize latency. This segmentation allows the system to optimize for bandwidth where beneficial and for speed where coalescing is unlikely.
2Productivity
If coalescing circuitry processes all load requests, then memory access efficiency is improved, but device complexity increases
Solution Approach 1:
Instead of applying full coalescing processing to all load requests, the system applies coalescing processing only to requests that the prediction circuitry identifies as having high coalescing probability. This partial action approach maintains memory access efficiency for the subset of requests that benefit most from coalescing, while avoiding the complexity overhead of processing every request through the complete coalescing pipeline.
Solution Approach 2:
The coalescing prediction circuitry acts as an intermediary between the load request generator and the coalescing circuitry. It assesses each load request and determines whether it should proceed to coalescing processing or take a direct path, thereby mediating the flow of requests to optimize the balance between memory access efficiency and processing complexity.
3Productivity
If coalescing prediction is implemented for all load requests, then processing effort is reduced, but storage requirements increase
Solution Approach 1:
The prediction circuitry uses probabilistic data structures with configurable parameters to balance processing effort reduction against storage requirements. By adjusting parameters such as false positive rates and data structure sizes, the system can optimize the trade-off between how many requests are correctly identified for coalescing (reducing processing effort) and how much storage is consumed by the prediction data structures.
Data Source
AI summary
Apparatuses and methods for handling load requests are disclosed. In response to a load request specifying a data item to retrieve from memory, a series of data items comprising the data item identified by the load request are retrieved. Load requests are buffered prior to the load requests being carried out. Coalescing circuitry determines for the load request and a set of one or more other load requests buffered in the pending load buffer circuitry whether an address proximity condition is true. The address proximity condition is true when all data items identified by the set of one or more other load requests are comprised within the series of data items. When the address proximity condition is true, the set of one or more other load requests are suppressed. Coalescing prediction circuitry generates a coalescing prediction for each load request based on previous handling of load requests by the coalescing circuitry.