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Record W2158753164 · doi:10.1109/ipdps.2001.924959

On-line debugging and performance monitoring with barriers

2002· article· en· W2158753164 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
TopicParallel Computing and Optimization Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsDebuggingComputer sciencePOSIX ThreadsOperating systemPOSIXSPMDConstruct (python library)Thread (computing)Parallel computingEmbedded systemProgramming language

Abstract

fetched live from OpenAlex

We introduce the Stupid Barrier Tricks (SBT) library for on-line debugging and performance monitoring of shared-memory parallel programs. Single-program-multiple-data (SPMD) programs often use barriers to synchronize threads of execution and to delimit the start and end of different phases of computation. Through the novel (and simple) named barriers construct, dynamic performance warnings, and integration with lightweight performance counter libraries, SBT helps programmers localize deadlocks and performance bottlenecks in their programs. SBT is a portable library that currently supports both POSIX threads and SGI Irix sproc threads. SBT also supports both the PCL and Irix libperfex performance counter libraries. For production runs, the SBT overheads can be eliminated using conditional compilation.

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 score0.726
Threshold uncertainty score0.208

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 head0.237
Teacher spread0.216 · 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