Hardware Indirect Prefetcher for Irregular Memory Access Patterns
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Solution Overview
Problem
Existing processor technologies face performance bottlenecks in graph analytics and sparse matrix applications due to high latency and bandwidth limitations in memory access, particularly for irregular memory access patterns, which current hardware and software prefetching methods fail to adequately address.
Innovation Solution
The implementation of a hardware-based indirect prefetcher that detects and anticipates indirect memory access patterns by reading an index array ahead, and a partial cacheline accessing technique that only loads the necessary data into the cache, reducing bandwidth and power consumption without requiring programmer input.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional hardware prefetching methods are used, then memory access latency is reduced for regular access patterns, but performance deteriorates for irregular indirect access patterns
Solution Approach 1:
The patent implements a hardware prefetcher that performs preliminary actions by detecting indirect access patterns and prefetching data before it is actually needed by the processor. The system monitors memory access patterns, identifies indirect addressing sequences, and proactively loads required data into the cache hierarchy in advance, thereby reducing latency for irregular access patterns without requiring programmer intervention.
Solution Approach 2:
The prefetcher operates autonomously to detect and handle indirect access patterns without external control. It self-monitors memory access streams, automatically identifies indirect addressing sequences through pattern recognition logic, and self-manages the prefetching operation by generating appropriate memory requests, thereby improving productivity for irregular patterns while maintaining low latency.
2Reliability
If full cachelines are loaded into cache, then data availability is improved, but bandwidth consumption and power usage increase
Solution Approach 1:
The patent applies local quality by differentiating between different portions of cachelines based on actual access patterns. Instead of uniformly loading entire cachelines, the system identifies and loads only the specific data elements or sub-regions that are actually needed for indirect access patterns, thereby maintaining data availability for required operations while reducing unnecessary bandwidth consumption and power usage for unused cache data.
Solution Approach 2:
The system performs partial action by loading only the necessary portion of cachelines into the cache hierarchy rather than complete cachelines. The prefetcher analyzes indirect access patterns to determine the exact data requirements and selectively prefetches only those specific data elements, avoiding the excessive action of loading entire cachelines when only partial data is needed, thus reducing energy loss while maintaining reliability.
Data Source
AI summary
Two techniques address bottlenecking in processors. The first is indirect prefetching. The technique can be especially useful for graph analytics and sparse matrix applications. For graph analytics and sparse matrix applications, the addresses of most random memory accesses come from an index array B which is sequentially scanned by an application. The random accesses are actually indirect accesses in the form A[B[i]]. A hardware component is introduced to detect this pattern. The hardware can then read B a certain distance ahead, and prefetch the corresponding element in A. For example, if the “prefetch distance” is k, when B[i] is accessed, the hardware reads B[i+k], and then A[B[i+k]. For partial cacheline accessing, the indirect accesses are usually accessing random memory locations and only accessing a small portion of a cacheline. Instead of loading the whole cacheline into L1 cache, the second technique only loads a part of the cacheline.