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Record W2963523287 · doi:10.1145/3330999

Polyhedral Compilation for Multi-dimensional Stream Processing

2019· article· en· W2963523287 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACM Transactions on Architecture and Code Optimization · 2019
Typearticle
Languageen
FieldComputer Science
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsComputer scienceParallel computingXeonAffine transformationCode (set theory)Parallelism (grammar)Stream processingScheduleTransformation (genetics)ThroughputPolytope modelXeon PhiDigital signal processingAlgorithmProgramming languageComputer hardwareMathematics

Abstract

fetched live from OpenAlex

We present a method for compilation of multi-dimensional stream processing programs from affine recurrence equations with unbounded domains into imperative code with statically allocated memory. The method involves a novel polyhedral schedule transformation called periodic tiling. It accommodates existing polyhedral optimizations to improve memory access patterns and expose parallelism. This enables efficient execution of programming languages with unbounded recurrence equations, as well as optimization of existing languages from which this form can be derived. The method is experimentally evaluated on 5 DSP algorithms with large problem sizes. Results show potential for improved throughput compared to hand-optimized C++ (speedups on a 6-core Intel Xeon CPU up to 10× with a geometric mean 3.3×). 1

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: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.225
Threshold uncertainty score0.624

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 head0.021
GPT teacher head0.273
Teacher spread0.253 · 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