Evaluating Iterative Compilation. - Grigori Fursin; Michael F. P. O'Boyle; Peter M. W. Knijnenburg (LCPC, 2002)

A fast and accurate method for determining a lower bound on execution time. - Grigori Fursin; Michael F. P. O'Boyle; Olivier Temam; G. Watts (Concurr. Comput. Pract. Exp., 2004)

Iterative compilation and performance prediction for numerical applications. - Grigori Fursin (2004)

A Practical Method for Quickly Evaluating Program Optimizations. - Grigori Fursin; Albert Cohen 001; Michael F. P. O'Boyle; Olivier Temam (HiPEAC, 2005)

A Heuristic Search Algorithm Based on Unified Transformation Framework. - Shun Long; Grigori Fursin (ICPP Workshops, 2005)

Probabilistic source-level optimisation of embedded programs. - Björn Franke; Michael F. P. O'Boyle; John Thomson; Grigori Fursin (LCTES, 2005)

Automatic performance model construction for the fast software exploration of new hardware designs. - John Cavazos; Christophe Dubach; Felix V. Agakov; Edwin V. Bonilla; Michael F. P. O'Boyle; Grigori Fursin; Olivier Temam (CASES, 2006)

Using Machine Learning to Focus Iterative Optimization. - Felix V. Agakov; Edwin V. Bonilla; John Cavazos; Björn Franke; Grigori Fursin; Michael F. P. O'Boyle; John Thomson; Marc Toussaint; Christopher K. I. Williams (CGO, 2006)

Fast compiler optimisation evaluation using code-feature based performance prediction. - Christophe Dubach; John Cavazos; Björn Franke; Grigori Fursin; Michael F. P. O'Boyle; Olivier Temam (Conf. Computing Frontiers, 2007)

Rapidly Selecting Good Compiler Optimizations using Performance Counters. - John Cavazos; Grigori Fursin; Felix V. Agakov; Edwin V. Bonilla; Michael F. P. O'Boyle; Olivier Temam (CGO, 2007)

MiDataSets: Creating the Conditions for a More Realistic Evaluation of Iterative Optimization. - Grigori Fursin; John Cavazos; Michael F. P. O'Boyle; Olivier Temam (HiPEAC, 2007)

A Cost-Aware Parallel Workload Allocation Approach Based on Machine Learning Techniques. - Shun Long; Grigori Fursin; Björn Franke (NPC, 2007)

Quick and Practical Run-Time Evaluation of Multiple Program Optimizations. - Grigori Fursin; Albert Cohen 001; Michael F. P. O'Boyle; Olivier Temam (Trans. High Perform. Embed. Archit. Compil., 2007)

Collective Optimization. - Grigori Fursin; Olivier Temam (HiPEAC, 2009)

Predictive Runtime Code Scheduling for Heterogeneous Architectures. - Víctor J. Jiménez; Lluís Vilanova; Isaac Gelado; Marisa Gil; Grigori Fursin; Nacho Navarro (HiPEAC, 2009)

Reducing Training Time in a One-Shot Machine Learning-Based Compiler. - John Thomson; Michael F. P. O'Boyle; Grigori Fursin; Björn Franke (LCPC, 2009)

Portable compiler optimisation across embedded programs and microarchitectures using machine learning. - Christophe Dubach; Timothy M. Jones 001; Edwin V. Bonilla; Grigori Fursin; Michael F. P. O'Boyle (MICRO, 2009)

Systematic search within an optimisation space based on Unified Transformation Framework. - Shun Long; Grigori Fursin (Int. J. Comput. Sci. Eng., 2009)

Practical aggregation of semantical program properties for machine learning based optimization. - Mircea Namolaru; Albert Cohen 001; Grigori Fursin; Ayal Zaks; Ari Freund 001 (CASES, 2010)

Evaluating iterative optimization across 1000 datasets. - Yang Chen; Yuanjie Huang; Lieven Eeckhout; Grigori Fursin; Liang Peng; Olivier Temam; Chengyong Wu (PLDI, 2010)

Collective optimization: A practical collaborative approach. - Grigori Fursin; Olivier Temam (ACM Trans. Archit. Code Optim., 2010)

Milepost GCC: Machine Learning Enabled Self-tuning Compiler. - Grigori Fursin; Yuriy Kashnikov; Abdul Wahid Memon; Zbigniew Chamski; Olivier Temam; Mircea Namolaru; Elad Yom-Tov; Bilha Mendelson; Ayal Zaks; Eric Courtois; François Bodin; Phil Barnard; et al. (Int. J. Parallel Program., 2011)

Abstract: cTuning.org: Novel Extensible Methodology, Framework and Public Repository to Collaboratively Address Exascale Challenges. - Grigori Fursin (SC Companion, 2012)

Poster: Collective Tuning: Novel Extensible Methodology, Framework and Public Repository to Collaboratively Address Exascale Challenges. - Grigori Fursin (SC Companion, 2012)

Deconstructing iterative optimization. - Yang Chen; Shuangde Fang; Yuanjie Huang; Lieven Eeckhout; Grigori Fursin; Olivier Temam; Chengyong Wu (ACM Trans. Archit. Code Optim., 2012)

Crowdtuning: systematizing auto-tuning using predictive modeling and crowdsourcing. - Abdul Wahid Memon; Grigori Fursin (PARCO, 2013)

Collective Mind: cleaning up the research and experimentation mess in computer engineering using crowdsourcing, big data and machine learning. - Grigori Fursin (CoRR, 2013)

Community-driven reviewing and validation of publications. - Grigori Fursin; Christophe Dubach (TRUST@PLDI, 2014)

Proceedings of the 5th International Workshop on Adaptive Self-tuning Computing Systems 2015 (ADAPT'15). - Christophe Dubach; Grigori Fursin (CoRR, 2014)

Collective Tuning Initiative. - Grigori Fursin (CoRR, 2014)

Finding representative sets of optimizations for adaptive multiversioning applications. - Lianjie Luo; Yang Chen; Chengyong Wu; Shun Long; Grigori Fursin (CoRR, 2014)

Collective mind: Towards practical and collaborative auto-tuning. - Grigori Fursin; Renato Miceli; Anton Lokhmotov; Michael Gerndt; Marc Baboulin; Allen D. Malony; Zbigniew Chamski; Diego Novillo; Davide Del Vento (Sci. Program., 2014)

Collective Mind, Part II: Towards Performance- and Cost-Aware Software Engineering as a Natural Science. - Grigori Fursin; Abdul Wahid Memon; Christophe Guillon; Anton Lokhmotov (CoRR, 2015)

Artifact Evaluation for Publications (Dagstuhl Perspectives Workshop 15452). - Bruce R. Childers; Grigori Fursin; Shriram Krishnamurthi; Andreas Zeller (Dagstuhl Reports, 2015)

Collective Knowledge: Towards R&D sustainability. - Grigori Fursin; Anton Lokhmotov; Ed Plowman (DATE, 2016)

Optimizing convolutional neural networks on embedded platforms with OpenCL. - Anton Lokhmotov; Grigori Fursin (IWOCL, 2016)

Benchmarking, autotuning and crowdtuning OpenCL programs using the Collective Knowledge framework. - Anton Lokhmotov; Grigori Fursin (IWOCL, 2016)

PANEL: Open panel and discussion on tackling complexity, reproducibility and tech transfer challenges in a rapidly evolving AI/ML/systems research. - Grigori Fursin; Thierry Moreau; Hillery C. Hunter; Yiran Chen 001; Charles Qi; Tianqi Chen 001 (ReQuEST@ASPLOS, 2018)

Multi-objective autotuning of MobileNets across the full software/hardware stack. - Anton Lokhmotov; Nikolay Chunosov; Flavio Vella; Grigori Fursin (ReQuEST@ASPLOS, 2018)

Invited Talk Abstract: Introducing ReQuEST: An Open Platform for Reproducible and Quality-Efficient Systems-ML Tournaments. - Grigori Fursin (EMC2@ASPLOS, 2018)

Introducing ReQuEST: an Open Platform for Reproducible and Quality-Efficient Systems-ML Tournaments. - Thierry Moreau; Anton Lokhmotov; Grigori Fursin (CoRR, 2018)

A Collective Knowledge workflow for collaborative research into multi-objective autotuning and machine learning techniques. - Grigori Fursin; Anton Lokhmotov; Dmitry Savenko; Eben Upton (CoRR, 2018)

A model-driven approach for a new generation of adaptive libraries. - Marco Cianfriglia; Flavio Vella; Cedric Nugteren; Anton Lokhmotov; Grigori Fursin (CoRR, 2018)

SysML'19 demo: customizable and reusable Collective Knowledge pipelines to automate and reproduce machine learning experiments. - Grigori Fursin (CoRR, 2019)

SysML: The New Frontier of Machine Learning Systems. - Alexander Ratner; Dan Alistarh; Gustavo Alonso; David G. Andersen; Peter Bailis; Sarah Bird; Nicholas Carlini; Bryan Catanzaro; Eric S. Chung; Bill Dally; Jeff Dean; Inderjit S. Dhillon; et al. (CoRR, 2019)

CodeReef: an open platform for portable MLOps, reusable automation actions and reproducible benchmarking. - Grigori Fursin; Hervé Guillou; Nicolas Essayan (CoRR, 2020)

The Collective Knowledge project: making ML models more portable and reproducible with open APIs, reusable best practices and MLOps. - Grigori Fursin (CoRR, 2020)

Collective Knowledge: organizing research projects as a database of reusable components and portable workflows with common APIs. - Grigori Fursin (CoRR, 2020)

A Community Roadmap for Scientific Workflows Research and Development. - Rafael Ferreira da Silva; Henri Casanova; Kyle Chard; Ilkay Altintas; Rosa M. Badia; Bartosz Balis; Tainã Coleman; Frederik Coppens; Frank Di Natale; Bjoern Enders; Thomas Fahringer; Rosa Filgueira; et al. (WORKS, 2021)

Workflows Community Summit: Bringing the Scientific Workflows Community Together. - Rafael Ferreira da Silva; Henri Casanova; Kyle Chard; Dan Laney; Dong H. Ahn; Shantenu Jha; Carole A. Goble; Lavanya Ramakrishnan; J. Luc Peterson; Bjoern Enders; Douglas Thain; Ilkay Altintas; et al. (CoRR, 2021)

Workflows Community Summit: Advancing the State-of-the-art of Scientific Workflows Management Systems Research and Development. - Rafael Ferreira da Silva; Henri Casanova; Kyle Chard; Tainã Coleman; Dan Laney; Dong H. Ahn; Shantenu Jha; Dorran Howell; Stian Soiland-Reyes; Ilkay Altintas; Douglas Thain; Rosa Filgueira; et al. (CoRR, 2021)

MedPerf: Open Benchmarking Platform for Medical Artificial Intelligence using Federated Evaluation. - Alexandros Karargyris; Renato Umeton; Micah J. Sheller; Alejandro Aristizabal; Johnu George; Srini Bala; Daniel J. Beutel; Victor Bittorf; Akshay Chaudhari; Alexander Chowdhury; Cody Coleman; Bala Desinghu; et al. (CoRR, 2021)

On the Anatomy of Predictive Models for Accelerating GPU Convolution Kernels and Beyond. - Paolo Sylos Labini; Marco Cianfriglia; Damiano Perri; Osvaldo Gervasi; Grigori Fursin; Anton Lokhmotov; Cedric Nugteren; Bruno Carpentieri; Fabiana Zollo; Flavio Vella (ACM Trans. Archit. Code Optim., 2021)

Federated benchmarking of medical artificial intelligence with MedPerf. - Alexandros Karargyris; Renato Umeton; Micah J. Sheller; Alejandro Aristizabal; Johnu George; Anna Wuest; Sarthak Pati; Hasan Kassem; Maximilian Zenk; Ujjwal Baid; Prakash Narayana Moorthy; Alexander Chowdhury; et al. (Nat. Mac. Intell., 2023)

Enabling more efficient and cost-effective AI/ML systems with Collective Mind, virtualized MLOps, MLPerf, Collective Knowledge Playground and reproducible optimization tournaments. - Grigori Fursin (CoRR, 2024)

MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from Microwatts to Megawatts for Sustainable AI. - Arya Tschand; Arun Tejusve Raghunath Rajan; Sachin Idgunji; Anirban Ghosh; Jeremy Holleman; Csaba Király 002; Pawan Ambalkar; Ritika Borkar; Ramesh Chukka; Trevor Cockrell; Oliver Curtis; Grigori Fursin; et al. (CoRR, 2024)

MLPerf Power: Benchmarking the Energy Efficiency of Machine Learning Systems from μWatts to MWatts for Sustainable AI. - Arya Tschand; Arun Tejusve Raghunath Rajan; Sachin Idgunji; Anirban Ghosh; Jeremy Holleman; Csaba Király 002; Pawan Ambalkar; Ritika Borkar; Ramesh Chukka; Trevor Cockrell; Oliver Curtis; Grigori Fursin; et al. (HPCA, 2025)