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Return of the Runtimes

Martin Maas, Krste Asanović, John Kubiatowicz
2017 Proceedings of the 16th Workshop on Hot Topics in Operating Systems - HotOS '17  
We then outline the design of a general substrate for building such runtime systems, based on these seven tenets.  ...  To address this, we propose seven tenets for designing future language runtime systems for cloud data centers.  ...  While this work looked at big data frameworks, we believe that the idea generalizes.  ... 
doi:10.1145/3102980.3103003 dblp:conf/hotos/MaasAK17 fatcat:qmj47ywapbgmnpm6aphkeq3wra

Applications and Techniques for Fast Machine Learning in Science

Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik (+35 others)
2022 Frontiers in Big Data  
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science—the concept of integrating powerful ML methods into the real-time experimental data processing  ...  The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for  ...  “Linqits: big data on little clients,” in Proceedings of the 40th Annual International Symposium on Computer Architecture (Tel-Aviv: Association for Computing Machinery). Cireşan, D.  ... 
doi:10.3389/fdata.2022.787421 pmid:35496379 pmcid:PMC9041419 fatcat:5w2exf7vvrfvnhln7nj5uppjga

Applications and Techniques for Fast Machine Learning in Science [article]

Allison McCarn Deiana, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik, Maurizio Pierini (+74 others)
2021 arXiv   pre-print
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science -- the concept of integrating power ML methods into the real-time experimental data processing  ...  The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for  ...  As a result, experimentalists have little information on how to design their experiments.  ... 
arXiv:2110.13041v1 fatcat:cvbo2hmfgfcuxi7abezypw2qrm

Applications and Techniques for Fast Machine Learning in Science

Allison McCarn Deiana, Nhan Tran, Joshua Agar, Michaela Blott, Giuseppe Di Guglielmo, Javier Duarte, Philip Harris, Scott Hauck, Mia Liu, Mark S. Neubauer, Jennifer Ngadiuba, Seda Ogrenci-Memik (+35 others)
2022
In this community review report, we discuss applications and techniques for fast machine learning (ML) in science - the concept of integrating powerful ML methods into the real-time experimental data processing  ...  The material for the report builds on two workshops held by the Fast ML for Science community and covers three main areas: applications for fast ML across a number of scientific domains; techniques for  ...  As a result, experimentalists have little information on how to design their experiments.  ... 
doi:10.26083/tuprints-00021245 fatcat:q5g26rdbfbfozmfcywdpew56be