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ORCIDBlake A. Hechtman
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2020 – today
- 2022
[i11]Aidan Clark, Diego de Las Casas, Aurelia Guy, Arthur Mensch, Michela Paganini, Jordan Hoffmann, Bogdan Damoc, Blake A. Hechtman, Trevor Cai, Sebastian Borgeaud, George van den Driessche, Eliza Rutherford, Tom Hennigan, Matthew Johnson, Katie Millican, Albin Cassirer, Chris Jones, Elena Buchatskaya, David Budden, Laurent Sifre, Simon Osindero, Oriol Vinyals, Jack W. Rae, Erich Elsen, Koray Kavukcuoglu, Karen Simonyan:
Unified Scaling Laws for Routed Language Models. CoRR abs/2202.01169 (2022)- 2021
- [j1]Tianjian Lu
, Yi-Fan Chen, Blake A. Hechtman, Tao Wang, John R. Anderson:
Large-Scale Discrete Fourier Transform on TPUs. IEEE Access 9: 93422-93432 (2021) - [c7]Phitchaya Mangpo Phothilimthana, Amit Sabne, Nikhil Sarda, Karthik Srinivasa Murthy, Yanqi Zhou, Christof Angermueller, Mike Burrows, Sudip Roy, Ketan Mandke, Rezsa Farahani, Yu Emma Wang, Berkin Ilbeyi, Blake A. Hechtman, Bjarke Roune, Shen Wang, Yuanzhong Xu, Samuel J. Kaufman:
A Flexible Approach to Autotuning Multi-Pass Machine Learning Compilers. PACT 2021: 1-16 - [c6]Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake A. Hechtman, Jonathon Shlens:
Scaling Local Self-Attention for Parameter Efficient Visual Backbones. CVPR 2021: 12894-12904 - [i10]Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake A. Hechtman, Jonathon Shlens:
Scaling Local Self-Attention for Parameter Efficient Visual Backbones. CoRR abs/2103.12731 (2021) - [i9]Yuanzhong Xu, HyoukJoong Lee, Dehao Chen, Blake A. Hechtman, Yanping Huang, Rahul Joshi, Maxim Krikun, Dmitry Lepikhin, Andy Ly, Marcello Maggioni, Ruoming Pang, Noam Shazeer, Shibo Wang, Tao Wang, Yonghui Wu, Zhifeng Chen:
GSPMD: General and Scalable Parallelization for ML Computation Graphs. CoRR abs/2105.04663 (2021) - [i8]Jack W. Rae, Sebastian Borgeaud, Trevor Cai, Katie Millican, Jordan Hoffmann, H. Francis Song, John Aslanides, Sarah Henderson, Roman Ring, Susannah Young, Eliza Rutherford, Tom Hennigan, Jacob Menick, Albin Cassirer, Richard Powell, George van den Driessche, Lisa Anne Hendricks, Maribeth Rauh, Po-Sen Huang, Amelia Glaese, Johannes Welbl, Sumanth Dathathri, Saffron Huang, Jonathan Uesato, John Mellor, Irina Higgins, Antonia Creswell, Nat McAleese, Amy Wu, Erich Elsen, Siddhant M. Jayakumar, Elena Buchatskaya, David Budden, Esme Sutherland, Karen Simonyan, Michela Paganini, Laurent Sifre, Lena Martens, Xiang Lorraine Li, Adhiguna Kuncoro, Aida Nematzadeh, Elena Gribovskaya, Domenic Donato, Angeliki Lazaridou, Arthur Mensch, Jean-Baptiste Lespiau, Maria Tsimpoukelli, Nikolai Grigorev, Doug Fritz, Thibault Sottiaux, Mantas Pajarskas, Toby Pohlen, Zhitao Gong, Daniel Toyama, Cyprien de Masson d'Autume, Yujia Li, Tayfun Terzi, Vladimir Mikulik, Igor Babuschkin, Aidan Clark, Diego de Las Casas, Aurelia Guy, Chris Jones, James Bradbury, Matthew Johnson, Blake A. Hechtman, Laura Weidinger, Iason Gabriel, William S. Isaac, Edward Lockhart, Simon Osindero, Laura Rimell, Chris Dyer, Oriol Vinyals, Kareem Ayoub, Jeff Stanway, Lorrayne Bennett, Demis Hassabis, Koray Kavukcuoglu, Geoffrey Irving:
Scaling Language Models: Methods, Analysis & Insights from Training Gopher. CoRR abs/2112.11446 (2021) - 2020
- [i7]Tianjian Lu, Yi-Fan Chen, Blake A. Hechtman, Tao Wang, John R. Anderson:
Large-Scale Discrete Fourier Transform on TPUs. CoRR abs/2002.03260 (2020) - [i6]Yuanzhong Xu, HyoukJoong Lee, Dehao Chen, Hongjun Choi, Blake A. Hechtman, Shibo Wang:
Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training. CoRR abs/2004.13336 (2020) - [i5]Sameer Kumar, James Bradbury, Cliff Young, Yu Emma Wang, Anselm Levskaya, Blake A. Hechtman, Dehao Chen, HyoukJoong Lee, Mehmet Deveci, Naveen Kumar, Pankaj Kanwar, Shibo Wang, Skye Wanderman-Milne, Steve Lacy, Tao Wang, Tayo Oguntebi, Yazhou Zu, Yuanzhong Xu, Andy Swing:
Exploring the limits of Concurrency in ML Training on Google TPUs. CoRR abs/2011.03641 (2020)
2010 – 2019
- 2019
- [i4]Sameer Kumar, Victor Bitorff, Dehao Chen, Chiachen Chou, Blake A. Hechtman, HyoukJoong Lee, Naveen Kumar, Peter Mattson, Shibo Wang, Tao Wang, Yuanzhong Xu, Zongwei Zhou:
Scale MLPerf-0.6 models on Google TPU-v3 Pods. CoRR abs/1909.09756 (2019) - 2018
- [c5]Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake A. Hechtman:
Mesh-TensorFlow: Deep Learning for Supercomputers. NeurIPS 2018: 10435-10444 - [i3]Noam Shazeer, Youlong Cheng, Niki Parmar, Dustin Tran, Ashish Vaswani, Penporn Koanantakool, Peter Hawkins, HyoukJoong Lee, Mingsheng Hong, Cliff Young, Ryan Sepassi, Blake A. Hechtman:
Mesh-TensorFlow: Deep Learning for Supercomputers. CoRR abs/1811.02084 (2018) - 2016
- [i2]Blake A. Hechtman, Andrew D. Hilton, Daniel J. Sorin:
TREES: A CPU/GPU Task-Parallel Runtime with Explicit Epoch Synchronization. CoRR abs/1608.00571 (2016) - 2014
- [b1]Blake A. Hechtman:
Exploiting Parallelism in GPUs. Duke University, Durham, NC, USA, 2014 - [c4]Derek R. Hower, Blake A. Hechtman, Bradford M. Beckmann, Benedict R. Gaster, Mark D. Hill, Steven K. Reinhardt, David A. Wood:
Heterogeneous-race-free memory models. ASPLOS 2014: 427-440 - [c3]Blake A. Hechtman, Shuai Che, Derek R. Hower, Yingying Tian, Bradford M. Beckmann, Mark D. Hill, Steven K. Reinhardt, David A. Wood:
QuickRelease: A throughput-oriented approach to release consistency on GPUs. HPCA 2014: 189-200 - 2013
- [c2]Blake A. Hechtman, Daniel J. Sorin:
Exploring memory consistency for massively-threaded throughput-oriented processors. ISCA 2013: 201-212 - [c1]Blake A. Hechtman, Daniel J. Sorin:
Evaluating cache coherent shared virtual memory for heterogeneous multicore chips. ISPASS 2013: 118-119 - [i1]Blake A. Hechtman, Daniel J. Sorin:
Evaluating Cache Coherent Shared Virtual Memory for Heterogeneous Multicore Chips. CoRR abs/1310.7792 (2013)
Coauthor Index
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