Front-End Gather/Scatter Memory Coalescing Prediction
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Solution Overview
Problem
General-purpose instruction set architectures lack efficient support for gather/scatter instructions, leading to performance bottlenecks in modern applications that utilize these operations, as they are typically modified to process each memory location separately, increasing the number of micro-operations and reducing performance.
Innovation Solution
A system with a front-end and back-end subsystem that uses a prediction unit to coalesce memory access operations, determining the predicted and actual quantity of coalesced accesses, and updates a history table to improve prediction accuracy, allowing for fusion of micro-operations in the front-end pipeline and enhancements in the back-end pipeline structures like the load reorder queue and store reorder queue to optimize gather/scatter instruction processing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If gather/scatter instructions are processed by converting each memory location access into separate micro-operations, then the instruction can be executed on general-purpose architectures, but the number of micro-operations increases significantly, reducing processing performance
Solution Approach 1:
The patent merges multiple separate micro-operations into coalesced memory access operations by identifying and combining accesses to the same memory location. The load-store unit analyzes the plurality of access operations and merges those targeting identical locations, reducing the total operation count while maintaining correctness on general-purpose architectures.
Solution Approach 2:
The patent performs preliminary analysis of access operations in the load-store unit before execution, identifying which operations can be coalesced. This advance preparation allows the system to optimize the instruction stream in advance, converting individual micro-operations into fewer coalesced operations without requiring specialized hardware support.
2Device complexity
If the system uses a fixed number of micro-operations for gather/scatter instructions, then the pipeline can be simplified, but it cannot adapt to varying actual memory access patterns, leading to inefficiency
Solution Approach 1:
The patent implements dynamic determination of the number of coalesced memory access operations based on actual memory access patterns. The load-store unit analyzes the specific access operations generated by each instruction and dynamically adjusts the coalescing degree, allowing the system to adapt to varying patterns without requiring complex pre-configured pipelines.
Solution Approach 2:
The patent uses feedback from actual memory access execution to refine prediction accuracy. The system monitors the relationship between predicted and actual coalescing outcomes, using this feedback to improve future predictions and optimize performance for recurring access patterns.
3Productivity
If the system predicts the quantity of coalesced memory access operations accurately, then performance is significantly improved, but maintaining high prediction accuracy requires complex prediction mechanisms and history tables
Solution Approach 1:
The prediction unit serves itself by using feedback from actual execution outcomes to automatically refine its prediction accuracy. The system monitors mispredictions and uses this information to improve future predictions, creating a self-improving mechanism that increases performance without requiring external intervention or overly complex control structures.
4Ease of manufacture
If the system processes each memory access operation separately, then implementation is simpler, but the number of operations increases and performance decreases
Solution Approach 1:
The patent implements partial coalescing by analyzing and merging only those access operations that target the same memory location, rather than attempting to coalesce all operations. This selective approach maintains implementation simplicity while achieving performance improvements through targeted optimization of redundant accesses.
Data Source
AI summary
A system for processing gather and scatter instructions can implement a front-end subsystem, a back-end subsystem, or both. The front-end subsystem includes a prediction unit configured to determine a predicted quantity of coalesced memory access operations required by an instruction. A decode unit converts the instruction into a plurality of access operations based on the predicted quantity, and transmits the plurality of access operations and an indication of the predicted quantity to an issue queue. The back-end subsystem includes a load-store unit that receives a plurality of access operations corresponding to an instruction, determines a subset of the plurality of access operations that can be coalesced, and forms a coalesced memory access operation from the subset. A queue stores multiple memory addresses for a given load-store entry to provide for execution of coalesced memory accesses.