Magellan: A High-Performance Loop-Guided Prefetcher for Indirect Memory Access
Bibliographic record
Abstract
Graph analytics and sparse linear algebra applications heavily rely on indirect memory access (IMA).IMAs are characterized by poor temporal and spatial locality, which causes frequent high-latency DRAM accesses.While dedicated hardware prefetchers for IMA have been explored, they target narrow access patterns and tend to introduce significant hardware complexity.Software prefetching offers a promising alternative, leveraging compiler analysis to prefetch indirection patterns.However, existing software prefetchers struggle with sparse applications due to limited loop iterations and complex IMA patterns across nested loops.We propose Magellan, a novel loop-guided software prefetcher designed to detect and schedule IMA prefetches efficiently.Magellan introduces two key innovations: (1) extracting dependence graphs across loop levels to detect complex IMA patterns and (2) capturing inner-outer loop semantics to prefetch for both current and future iterations.We evaluate Magellan on 14 memory-intensive benchmarks using real-world datasets from social networks and web graphs.Compared to the best existing IMA software prefetcher, Magellan reduces cache misses by 25% and dynamic instruction counts by 14% on average.This results in a 1.14 average speedup, with performance gains of up to 1.41.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".