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Record W2143226612 · doi:10.5555/1950815.1950911

Register pressure aware scheduling for high level synthesis

2011· article· en· W2143226612 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

VenueAsia and South Pacific Design Automation Conference · 2011
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsRegister allocationComputer scienceInstruction schedulingHigh-level synthesisParallel computingScheduling (production processes)SerializationOptimizing compilerCompilerData-flow analysisTwo-level schedulingDynamic priority schedulingScheduleEmbedded systemField-programmable gate arrayProgramming languageMathematical optimizationOperating systemData flow diagramMathematics

Abstract

fetched live from OpenAlex

Variations of list scheduling became the de-facto standard of scheduling straight line code in software compilers, a trend faithfully inherited by high-level synthesis solutions. Due to its nature, list scheduling is oblivious of the tightly coupled register pressure; a dangling fundamental problem that has been attacked by the compiler community for decades, and which results, in case of highlevel synthesis, in excessive instantiations of registers and accompanying steering logic. To alleviate this problem, we propose a synthesis framework called soft scheduling, which acts as a resource unconstrained pre-scheduling stage that restricts subsequent scheduling to minimize register pressure. This optimization objective is formulated as a live range minimization problem, a measure shown to be proportional to register pressure, and optimally solved in polynomial time using minimum cost network flow formulation. Unlike past solutions in the compiler community, which try to reduce register pressure by local serialization of subject instructions, the proposed solution operates on the entire basic block or hyperblock and systematically handles instruction chaining subject to the same objective. The application of the proposed solution to a set of real-life benchmarks results in a register pressure reduction ranging, on average, between 11% and 41% depending on the compilation and synthesis configurations with minor 2% to 4% increase in schedule latency.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.977
Threshold uncertainty score0.635

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.113
GPT teacher head0.260
Teacher spread0.147 · 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