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Robert Hagmann
Researcher at Google
Publications - 7
Citations - 6124
Robert Hagmann is an academic researcher from Google. The author has contributed to research in topics: Task (project management) & Central processing unit. The author has an hindex of 4, co-authored 7 publications receiving 4663 citations.
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In-Datacenter Performance Analysis of a Tensor Processing Unit
Norman P. Jouppi,Cliff Young,Nishant Patil,David A. Patterson,Gaurav Agrawal,Raminder Bajwa,Sarah Bates,Suresh Bhatia,Nan Boden,Albert T. Borchers,Rick Boyle,Pierre-luc Cantin,Clifford Chao,Christopher Aaron Clark,Jeremy Coriell,Michael J. Daley,Matt Dau,Jeffrey Dean,Ben Gelb,Tara Vazir Ghaemmaghami,Rajendra Gottipati,William John Gulland,Robert Hagmann,C. Richard Ho,Doug Hogberg,John Hu,Robert Hundt,D. Hurt,Julian Ibarz,Aaron Jaffey,Alek Jaworski,Alexander Kaplan,Khaitan Harshit,Andy Koch,Naveen Kumar,Steve Lacy,James Laudon,James Law,Diemthu Le,Chris Leary,Zhuyuan Liu,Kyle Lucke,Alan Lundin,Gordon MacKean,Adriana Maggiore,Maire Mahony,Kieran Miller,Rahul Nagarajan,Ravi Narayanaswami,Ray Ni,Kathy Nix,Thomas Norrie,Mark Omernick,Narayana Penukonda,Andrew Everett Phelps,Jonathan Ross,Matt Ross,Amir Salek,Emad Samadiani,Chris Severn,Gregory Sizikov,Matthew Snelham,Jed Souter,Dan Steinberg,Andy Swing,Mercedes Tan,Gregory Michael Thorson,Bo Tian,Horia Toma,Erick Tuttle,Vijay K. Vasudevan,Richard Walter,Walter Wang,Eric Wilcox,Doe Hyun Yoon +74 more
TL;DR: This paper evaluates a custom ASIC-called a Tensor Processing Unit (TPU)-deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN) and compares it to a server-class Intel Haswell CPU and an Nvidia K80 GPU, which are contemporaries deployed in the samedatacenters.
Proceedings ArticleDOI
In-Datacenter Performance Analysis of a Tensor Processing Unit
Norman P. Jouppi,Cliff Young,Nishant Patil,David A. Patterson,Gaurav Agrawal,Raminder Bajwa,Sarah Bates,Suresh Bhatia,Nan Boden,Albert T. Borchers,Rick Boyle,Pierre-luc Cantin,Clifford Chao,Christopher Aaron Clark,Jeremy Coriell,Michael J. Daley,Matt Dau,Jeffrey Dean,Ben Gelb,Tara Vazir Ghaemmaghami,Rajendra Gottipati,William John Gulland,Robert Hagmann,C. Richard Ho,Doug Hogberg,John Hu,Robert Hundt,D. Hurt,Julian Ibarz,Aaron Jaffey,Alek Jaworski,Alexander Kaplan,Khaitan Harshit,Daniel Killebrew,Andy Koch,Naveen Kumar,Steve Lacy,James Laudon,James Law,Diemthu Le,Chris Leary,Zhuyuan Liu,Kyle Lucke,Alan Lundin,Gordon MacKean,Adriana Maggiore,Maire Mahony,Kieran Miller,Rahul Nagarajan,Ravi Narayanaswami,Ray Ni,Kathy Nix,Thomas Norrie,Mark Omernick,Narayana Penukonda,Andrew Everett Phelps,Jonathan Ross,Matt Ross,Amir Salek,Emad Samadiani,Chris Severn,Gregory Sizikov,Matthew Snelham,Jed Souter,Dan Steinberg,Andy Swing,Mercedes Tan,Gregory Michael Thorson,Bo Tian,Horia Toma,Erick Tuttle,Vijay K. Vasudevan,Richard Walter,Walter Wang,Eric Wilcox,Doe Hyun Yoon +75 more
TL;DR: The Tensor Processing Unit (TPU) as discussed by the authors is a custom ASIC deployed in datacenters since 2015 that accelerates the inference phase of neural networks (NN) using a 65,536 8-bit MAC matrix multiply unit that offers a peak throughput of 92 TeraOps/second (TOPS).
Proceedings ArticleDOI
CPI2: CPU performance isolation for shared compute clusters
TL;DR: CPI2, which uses cycles-per-instruction (CPI) data obtained by hardware performance counters to identify problems, select the likely perpetrators, and then optionally throttle them so that the victims can return to their expected behavior.
Proceedings ArticleDOI
Optimizing Google's warehouse scale computers: The NUMA experience
TL;DR: This paper investigates the impact of non-uniform memory access (NUMA) for several Google's key web-service workloads in large-scale production WSCs and reveals surprising tradeoffs between optimizing for NUMA performance and reducing cache contention.
Proceedings ArticleDOI
Evaluation of NUMA-Aware Scheduling in Warehouse-Scale Clusters
TL;DR: A holistic NUMA-aware scheduling policy is introduced that combines both machine-level and cluster-level NUma-aware optimizations and there is a non-trivial tradeoff between cluster job packing efficiency and N UMA-fitness for the E-PVM algorithm under certain circumstances.