Thoth: Uncovering Data-Dependent Memory Access Patterns via Annotation-Directed Load Sampling

Key findings from this study

This research indicates that:

• Explicit producer-consumer load pair tracking via register-level dependency identification outperforms instruction-based dependency chain construction for capturing multi-level range relations in DDMA patterns.
• Annotation-directed load sampling that marks and samples only matched load instances avoids mismatches caused by out-of-order execution and range relations.
• Precise load annotation using reorder identifiers maintains correctness across pipeline flushes and prevents performance degradation from invalid or misaligned annotations.

Overview

Thoth is a hardware prefetcher designed to mitigate performance degradation caused by data-dependent memory access (DDMA) patterns in sparse data structures. DDMA patterns occur frequently in graph analytics, machine learning, and high-performance computing workloads. Existing hardware prefetching approaches—address-based and instruction-based methods—fail to capture multi-level range relations characteristic of DDMA-intensive algorithms, leaving significant prefetching opportunities unexploited. Thoth addresses these limitations by tracking explicit producer-consumer load pairs at the register level rather than constructing dependency chains.

Methods and approach

Thoth detects producer-consumer load pairs via register-level dependency tracking. The prefetcher employs an annotation-directed load sampling strategy that marks matched load instances and samples only annotated pairs. This approach avoids mismatches arising from out-of-order execution and range relations themselves. Precise load annotation leverages reorder identifiers to maintain correctness across pipeline flushes, enabling annotation to resume or terminate exactly at appropriate points. The architecture operates at the granularity of explicit load pairs rather than instruction-level dependency chains.

Results

Thoth delivers a 51.1% speedup relative to a no-prefetching baseline on DDMA-intensive benchmarks. The prefetcher outperforms two state-of-the-art DDMA prefetchers by 14.7% and 8.2%, respectively. The annotation-directed sampling strategy successfully uncovers multi-level range relations that address-based and instruction-based methods cannot capture robustly. Precise load annotation maintains correctness during pipeline flushes, preventing performance degradation from misaligned or invalid annotations.

Implications

Thoth advances hardware prefetching design by demonstrating that explicit producer-consumer tracking at the register level enables robust capture of complex DDMA patterns. The annotation-directed sampling strategy offers a practical approach to managing the challenges posed by out-of-order execution and multi-level dependencies in sparse data structure workloads. This work suggests that granularity of tracking and annotation precision are critical factors in prefetcher effectiveness for irregular memory access patterns. The 14.7% improvement over existing methods indicates substantial performance gains are achievable through architectural innovation in DDMA handling.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.