High Performance and Predictable Shared Last-level Cache for Safety-Critical Systems
Bibliographic record
Abstract
We propose ZeroCost-LLC (ZCLLC), a novel shared inclusive last-level cache (LLC) design for timing predictable multi-core platforms that offers lower worst-case latency (WCL) when compared with a traditional shared inclusive LLC design. ZCLLC achieves low WCL by eliminating certain memory operations in the form of cache line invalidations across the cache hierarchy that are a consequence of a core’s memory request that misses in the cache hierarchy and when there is no vacant entry in the LLC to accommodate the fetched data for this request. In addition to low WCL, ZCLLC offers performance benefits in the form of additional caching capacity and unlike state-of-the-art approaches, ZCLLC does not impose any constraints on its usage across multiple cores. In this work, we describe the impact of LLC cache line invalidations on the WCL and systematically build solutions to eliminate these invalidations resulting in ZCLLC. We also present ZCLLC-OPT, an optimized variant of ZCLLC that offers lower WCL and improved average-case performance over ZCLLC. We apply optimizations to the shared bus arbitration mechanism and extend the micro-architecture of ZCLLC to allow for overlapping memory requests to the main memory. Our analysis reveals that the analytical WCL of a memory request under ZCLLC-OPT is 87.0%, 93.8%, and 97.1% lower than that under state-of-the-art LLC partition sharing techniques for 2, 4, and 8 cores, respectively. ZCLLC-OPT shows average-case performance speedups of 1.89×, 3.36×, and 6.24× compared with the state-of-the-art LLC partition sharing techniques for 2, 4, and 8 cores, respectively. When compared with the original ZCLLC that does not have any optimizations, ZCLLC-OPT shows lower analytical WCLs that are 76.5%, 82.6%, and 86.2% lower compared with ZCLLC-NORMAL for 2, 4, and 8 cores, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".