@inproceedings{barhamMachineLearningSystems2019,
  location = {{New York, NY, USA}},
  title = {Machine {{Learning Systems Are Stuck}} in a {{Rut}}},
  isbn = {978-1-4503-6727-1},
  url = {http://doi.acm.org/10.1145/3317550.3321441},
  doi = {10.1145/3317550.3321441},
  abstract = {In this paper we argue that systems for numerical computing are stuck in a local basin of performance and programmability. Systems researchers are doing an excellent job improving the performance of 5-year-old benchmarks, but gradually making it harder to explore innovative machine learning research ideas. We explain how the evolution of hardware accelerators favors compiler back ends that hyper-optimize large monolithic kernels, show how this reliance on high-performance but inflexible kernels reinforces the dominant style of programming model, and argue these programming abstractions lack expressiveness, maintainability, and modularity; all of which hinders research progress. We conclude by noting promising directions in the field, and advocate steps to advance progress towards high-performance general purpose numerical computing systems on modern accelerators.},
  booktitle = {Proceedings of the {{Workshop}} on {{Hot Topics}} in {{Operating Systems}}},
  series = {{{HotOS}} '19},
  publisher = {{ACM}},
  urldate = {2019-06-07},
  date = {2019},
  pages = {177--183},
  author = {Barham, Paul and Isard, Michael},
  file = {/home/dimitri/Nextcloud/Zotero/storage/KK5T5X4X/Barham and Isard - 2019 - Machine Learning Systems Are Stuck in a Rut.pdf},
  venue = {Bertinoro, Italy}
}

{"_id":"wvSLgNJR4TooQLqtR","bibbaseid":"barham-isard-machinelearningsystemsarestuckinarut","authorIDs":[],"author_short":["Barham, P.","Isard, M."],"bibdata":{"bibtype":"inproceedings","type":"inproceedings","location":"New York, NY, USA","title":"Machine Learning Systems Are Stuck in a Rut","isbn":"978-1-4503-6727-1","url":"http://doi.acm.org/10.1145/3317550.3321441","doi":"10.1145/3317550.3321441","abstract":"In this paper we argue that systems for numerical computing are stuck in a local basin of performance and programmability. Systems researchers are doing an excellent job improving the performance of 5-year-old benchmarks, but gradually making it harder to explore innovative machine learning research ideas. We explain how the evolution of hardware accelerators favors compiler back ends that hyper-optimize large monolithic kernels, show how this reliance on high-performance but inflexible kernels reinforces the dominant style of programming model, and argue these programming abstractions lack expressiveness, maintainability, and modularity; all of which hinders research progress. We conclude by noting promising directions in the field, and advocate steps to advance progress towards high-performance general purpose numerical computing systems on modern accelerators.","booktitle":"Proceedings of the Workshop on Hot Topics in Operating Systems","series":"HotOS '19","publisher":"ACM","urldate":"2019-06-07","date":"2019","pages":"177–183","author":[{"propositions":[],"lastnames":["Barham"],"firstnames":["Paul"],"suffixes":[]},{"propositions":[],"lastnames":["Isard"],"firstnames":["Michael"],"suffixes":[]}],"file":"/home/dimitri/Nextcloud/Zotero/storage/KK5T5X4X/Barham and Isard - 2019 - Machine Learning Systems Are Stuck in a Rut.pdf","venue":"Bertinoro, Italy","bibtex":"@inproceedings{barhamMachineLearningSystems2019,\n location = {{New York, NY, USA}},\n title = {Machine {{Learning Systems Are Stuck}} in a {{Rut}}},\n isbn = {978-1-4503-6727-1},\n url = {http://doi.acm.org/10.1145/3317550.3321441},\n doi = {10.1145/3317550.3321441},\n abstract = {In this paper we argue that systems for numerical computing are stuck in a local basin of performance and programmability. Systems researchers are doing an excellent job improving the performance of 5-year-old benchmarks, but gradually making it harder to explore innovative machine learning research ideas. We explain how the evolution of hardware accelerators favors compiler back ends that hyper-optimize large monolithic kernels, show how this reliance on high-performance but inflexible kernels reinforces the dominant style of programming model, and argue these programming abstractions lack expressiveness, maintainability, and modularity; all of which hinders research progress. We conclude by noting promising directions in the field, and advocate steps to advance progress towards high-performance general purpose numerical computing systems on modern accelerators.},\n booktitle = {Proceedings of the {{Workshop}} on {{Hot Topics}} in {{Operating Systems}}},\n series = {{{HotOS}} '19},\n publisher = {{ACM}},\n urldate = {2019-06-07},\n date = {2019},\n pages = {177--183},\n author = {Barham, Paul and Isard, Michael},\n file = {/home/dimitri/Nextcloud/Zotero/storage/KK5T5X4X/Barham and Isard - 2019 - Machine Learning Systems Are Stuck in a Rut.pdf},\n venue = {Bertinoro, Italy}\n}\n\n","author_short":["Barham, P.","Isard, M."],"key":"barhamMachineLearningSystems2019","id":"barhamMachineLearningSystems2019","bibbaseid":"barham-isard-machinelearningsystemsarestuckinarut","role":"author","urls":{"Paper":"http://doi.acm.org/10.1145/3317550.3321441"},"downloads":0},"bibtype":"inproceedings","biburl":"https://raw.githubusercontent.com/dlozeve/newblog/master/bib/all.bib","creationDate":"2020-01-08T20:39:39.374Z","downloads":0,"keywords":[],"search_terms":["machine","learning","systems","stuck","rut","barham","isard"],"title":"Machine Learning Systems Are Stuck in a Rut","year":null,"dataSources":["3XqdvqRE7zuX4cm8m"]}