Profile-Guided Temporal Prefetching

2025-06-19

AI Summary

To address low temporal prefetching efficiency and underutilized on-chip metadata storage under irregular memory access patterns, this paper proposes Prophet, a hardware-software co-designed framework. Methodologically, Prophet introduces: (1) a lightweight, counter-based program profiling technique—replacing conventional trace recording—to efficiently capture access regularity; (2) dynamic injection and adaptive tuning of compiler-inserted prefetch hints, enabling input-aware optimization; and (3) seamless coexistence with existing hardware prefetchers, coupled with frequency-aware hierarchical metadata management. Evaluation shows that Prophet achieves an average 14.23% speedup over the state-of-the-art temporal prefetcher Triangle—substantially outperforming prior approaches (which yield only +0.1%)—and maintains consistent superiority across all workloads. Crucially, the overhead from profiling, analysis, and inserted instructions is negligible.

Technology Category

• Machine Learning: Hardware-aware ML
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Data Mining & Knowledge Management: Representing, Reasoning, and Using Provenance, Trust

Application Category

• Security and Privacy: Tracking, profiling, and countermeasures against them
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs

Abstract

Temporal prefetching shows promise for handling irregular memory access patterns, which are common in data-dependent and pointer-based data structures. Recent studies introduced on-chip metadata storage to reduce the memory traffic caused by accessing metadata from off-chip DRAM. However, existing prefetching schemes struggle to efficiently utilize the limited on-chip storage. An alternative solution, software indirect access prefetching, remains ineffective for optimizing temporal prefetching. In this work, we propose Prophet—a hardware-software co-designed framework that leverages profile-guided methods to optimize metadata storage management. Prophet profiles programs using counters instead of traces, injects hints into programs to guide metadata storage management, and dynamically tunes these hints to enable the optimized binary to adapt to different program inputs. Prophet is designed to coexist with existing hardware temporal prefetchers, delivering efficient, high-performance solutions for frequently executed workloads while preserving the original runtime scheme for less frequently executed workloads. Prophet outperforms the state-of-the-art temporal prefetcher, Triangel, by 14.23%, effectively addressing complex temporal patterns where prior profile-guided solutions fall short (only achieving 0.1% performance gain). Prophet delivers superior performance across all evaluated workload inputs, introducing negligible profiling, analysis, and instruction overhead.

Problem

• Optimizing metadata storage for temporal prefetching efficiency
• Handling irregular memory access patterns in data-dependent structures
• Improving performance of profile-guided temporal prefetching solutions

Innovation

• Profile-guided metadata storage optimization
• Hardware-software co-designed framework
• Dynamic hint tuning for input adaptation