CGO/PPoPP'17 Artifact Evaluation Discussion (enabling open and reproducible research)
Notice bibliographique
Résumé
14 March 2017, CNRS webinar, Grenoble, France A decade ago my research nearly stalled. I was investigating how to crowdsource performance analysis and optimization of realistic workloads across diverse hardware provided by volunteers and combine it with machine learning [1]. Often, it was simply impossible to reproduce crowdsourced empirical results and build predictive models due to continuously changing software and hardware stacks. Worse still, lack of realistic workloads and representative data sets in our community severely limited the usefulness of such models. All these problems motivated me to create a public portal (cTuning.org) to share, validate and reuse workloads, data sets, tools, experimental results, and predictive models while involving the community in this effort [2]. This experience, in turn, helped us to initiate the so-called Artifact Evaluation (AE) at ACM conferences on parallel programming, architecture and code generation (ASPLOS, CGO, PPoPP, PACT, SC and MLSys). AE aims to independently validate experimental results reported in the publications and to encourage code and data sharing. These slides are from my webinar “Enabling open and reproducible research at computer systems conferences: the good, the bad and the ugly” at CNRS Grenoble (14 March 2017). I shared my practical experience organizing Artifact Evaluation over the past years, along with encountered problems and possible solutions. On the one hand, we have received incredible support from the research community, ACM, universities, and companies. We have even received a record number of artifact submissions at the CGO/PPoPP'17 AE (27 vs 17 two years ago) sponsored by NVIDIA and the cTuning foundation. We have also introduced Artifact Appendices and co-authored the new ACM Result and Artifact Review and Badging policy now used at Supercomputing. On the other hand, the use of proprietary benchmarks, rare hardware platforms, and totally ad-hoc scripts to set up, run and process experiments all place a huge burden on evaluators. It is simply too difficult and time-consuming to customize and rebuild experimental setups, reuse artifacts and eventually build upon others’ efforts - the main pillars of open science! I then present Collective Knowledge (CK), my attempt to introduce a customizable workflow framework with a unified JSON API and a cross-platform package manager, that can automate ML&systems R&D and enable live papers while automatically adapting to continuously evolving software and hardware [3]. I also demonstrate a practical CK workflow to collaboratively optimize deep learning across different compilers, libraries, data sets and diverse platforms from resource-constrained mobile devices to data centers (see our Android app to crowdsource DNN optimization across diverse mobile devices provided by volunteers, and the public repository with results) [4]. Finally, I describe our novel publication model to reproduce results from published papers with the help of the community [5]. Please feel free to contact me at Grigori.Fursin@cTuning.org if you have any questions or comments! I am looking forward to your feedback! References “Milepost GCC: Machine learning enabled self-tuning compiler”, International journal of parallel programming, Volume 39, Issue 3, pp.296-327, 2009 “Collective Tuning Initiative: automating and accelerating development and optimization of computing systems”, GCC Developers' Summit, Montreal, Canada. 2009 “Collective Knowledge: towards R&D sustainability”, Proceedings of the Conference on Design, Automation, and Test in Europe (DATE), 2016 “Optimizing Convolutional Neural Networks on Embedded Platforms with OpenCL”, IWOCL'16, Vienna, Austria, 2016 “Community-driven reviewing and validation of publications”, Proceedings of the 1st ACM SIGPLAN Workshop on Reproducible Research Methodologies and New Publication Models in Computer Engineering @ PLDI’14, Edinburgh, UK
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,109 | 0,152 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,002 |
| Bibliométrie | 0,004 | 0,003 |
| Études des sciences et des technologies | 0,005 | 0,005 |
| Communication savante | 0,018 | 0,010 |
| Science ouverte | 0,004 | 0,017 |
| Intégrité de la recherche | 0,007 | 0,007 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,062 | 0,043 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».