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Record W2733045287 · doi:10.4230/lipics.ecrts.2017.24

WCET-Driven Dynamic Data Scratchpad Management With Compiler-Directed Prefetching

2017· article· en· W2733045287 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDROPS (Schloss Dagstuhl – Leibniz Center for Informatics) · 2017
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsComputer scienceCompilerWorst-case execution timeParallel computingLatency (audio)CAS latencyScheme (mathematics)Embedded systemOperating systemExecution timeMemory controllerSemiconductor memory

Abstract

fetched live from OpenAlex

In recent years, the real-time community has produced a variety of approaches targeted at managing on-chip memory (scratchpads and caches) in a predictable way. However, to obtain safe WCET bounds, such techniques generally assume that the processor is stalled while waiting to reload the content of the on-chip memory; hence, they are less effective at hiding main memory latency compared to speculation-based techniques, such as hardware prefetching, that are largely used in general-purpose systems. In this work, we introduce a novel compiler-directed prefetching scheme for scratchpad memory that effectively hides the latency of main memory accesses by overlapping data transfers with the program execution. We implement and test an automated program compilation and optimization flow within the LLVM framework, and we show how to obtain improved WCET bounds through static analysis.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.924
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0020.004
Open science0.0070.003
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.030
GPT teacher head0.299
Teacher spread0.268 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it