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Using the SGI Pro64 Open Source Compiler Infra-Structure for Teaching and Research

2001· article· en· W1637346 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
TopicSoftware Testing and Debugging Techniques
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsCompilerComputer scienceSuiteCompiler constructionCompiler correctnessInterprocedural optimizationClass (philosophy)Optimizing compilerProgramming languageSource codeParallel computingLoop optimizationArtificial intelligence

Abstract

fetched live from OpenAlex

Modem optimizing compilers are complex programs that require from tens to hundreds of people-years to be developed. Thus professors must use third-party compiler infra-structures to introduce students to compiler optimizations. Until recently only infra-structures developed at universities, research institutes, or by GNU were widely available for teaching. However, in May 2000, SGI made public the source code for Pro64, a highly optimized suite of compilers for the Intel Architecture 64 (IA-64) that is an evolution of the established MIPSPro suite of compilers. The use of a production-level compiler infra-structure for teaching is thus new. In this paper we report our experience using the Pro64 in a graduate compiler optimization class. We paired the study of the Pro64 with the use of IMPACT within Trimaran, and with performance studies conducted with the MIPSPro compilers. The students feedback indicate that they valued working with a state-of-the-art compiler infrastructure and studying open research topics for their class projects.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.619
Threshold uncertainty score0.841

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
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 head0.180
GPT teacher head0.433
Teacher spread0.254 · 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