Hierarchical Prefetching: A Software-Hardware Instruction Prefetcher for Server Applications

The large working set of instructions in server-side applications causes a significant bottleneck in the front-end, even for high-performance processors equipped with fetch-directed instruction prefetching (FDIP). Prefetchers specifically designed for server scenarios typically rely on a record-and-replay mechanism that exploits the repetitiveness of instruction sequences. However, the efficacy of these techniques is compromised by discrepancies between actual and predicted control flows, resulting in loss of coverage and timeliness. This paper proposes Hierarchical Prefetching, a novel approach that tackles the limitations of existing prefetchers. It identifies common coarse-grained functionality blocks (called Bundles) within the server code and prefetches them as a whole. Bundles are significantly larger than typical prefetch targets, encompassing tens to hundreds of kilobytes of code. The approach combines simple software analysis of code for bundle formation and light-weight hardware for record-and-replay prefetching. The prefetcher requires under 2KB of on-chip storage by keeping most of the metadata in main memory. Experiments with 11 popular server workloads reveal that Hierarchical Prefetching significantly improves miss coverage and timeliness over prior techniques, achieving a 6.6% average performance gain over FDIP.

Authors

Institutions

Publication Details

Published
2025-03-27
DOI
https://doi.org/10.1145/3676641.3716260
Citations
2
Primary Topic
Parallel Computing and Optimization Techniques
Type
article
Field-Weighted Citation Impact
4.54
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Hierarchical Prefetching: A Software-Hardware Instruction Prefetcher for Server Applications

Wenjian He, Boris Grot, Youhui Zhang, Yashuai Lv et al.
2 citations
Parallel Computing and Optimization Techniques
4.54
article

Hierarchical Prefetching: A Software-Hardware Instruction Prefetcher for Server Applications

Wenjian He, Boris Grot, Youhui Zhang, Yashuai Lv, Guowei Zhang, Tingji Zhang, Peng Qu, Wenxin Wang, Fang Su, Xuefeng Zhang
article en
2 citations

Abstract

The large working set of instructions in server-side applications causes a significant bottleneck in the front-end, even for high-performance processors equipped with fetch-directed instruction prefetching (FDIP). Prefetchers specifically designed for server scenarios typically rely on a record-and-replay mechanism that exploits the repetitiveness of instruction sequences. However, the efficacy of these techniques is compromised by discrepancies between actual and predicted control flows, resulting in loss of coverage and timeliness. This paper proposes Hierarchical Prefetching, a novel approach that tackles the limitations of existing prefetchers. It identifies common coarse-grained functionality blocks (called Bundles) within the server code and prefetches them as a whole. Bundles are significantly larger than typical prefetch targets, encompassing tens to hundreds of kilobytes of code. The approach combines simple software analysis of code for bundle formation and light-weight hardware for record-and-replay prefetching. The prefetcher requires under 2KB of on-chip storage by keeping most of the metadata in main memory. Experiments with 11 popular server workloads reveal that Hierarchical Prefetching significantly improves miss coverage and timeliness over prior techniques, achieving a 6.6% average performance gain over FDIP.

Huawei Technologies (China) (CN), University of Edinburgh (GB), Tsinghua University (CN)
Openalex Percentile: Top 7%
Parallel Computing and Optimization Techniques
4.54
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.