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Record W4399348269 · doi:10.1145/3626183.3659966

When Is Parallelism Fearless and Zero-Cost with Rust?

2024· article· en· W4399348269 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputability, Logic, AI Algorithms
Canadian institutionsUniversity of Toronto
FundersUniversitas Brawijaya
KeywordsParallelism (grammar)Zero (linguistics)Computer scienceRust (programming language)Parallel computingProgramming languageLinguisticsPhilosophy

Abstract

fetched live from OpenAlex

The Rust programming language is lauded for enabling fearless concurrency with zero cost: detecting concurrency errors at compile time. Given the enduring difficulty of parallel programming in other languages, this implied panacea warrants analysis. In particular, the efficacy of Rust across types of parallelism remains unexplored. Is parallel programming always devoid of fear with Rust? We answer this question through a case study, porting 14 benchmarks with abundant regular and irregular parallelism from C++ to Rust and reporting our experience and observations. We find that Rust, with the Rayon library, indeed delivers fearlessness for program phases comprising only regular parallelism, e.g., prefix-sum. However, for applications with any irregular parallelism, the programmer must choose between unsafe code or high-overhead dynamic checks with errors that manifest at run time, leaving the arduous task of parallel programming as scary with Rust as with its predecessors.

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.848
Threshold uncertainty score0.749

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 communication0.0010.000
Open science0.0010.001
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 head0.017
GPT teacher head0.241
Teacher spread0.224 · 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

Quick stats

Citations2
Published2024
Admission routes1
Has abstractyes

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