Blake Hechtman
Cited by
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Scaling language models: Methods, analysis & insights from training gopher
JW Rae, S Borgeaud, T Cai, K Millican, J Hoffmann, F Song, J Aslanides, ...
arXiv preprint arXiv:2112.11446, 2021
Gemini: a family of highly capable multimodal models
G Team, R Anil, S Borgeaud, Y Wu, JB Alayrac, J Yu, R Soricut, ...
arXiv preprint arXiv:2312.11805, 2023
Scaling local self-attention for parameter efficient visual backbones
A Vaswani, P Ramachandran, A Srinivas, N Parmar, B Hechtman, ...
Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021
Mesh-tensorflow: Deep learning for supercomputers
N Shazeer, Y Cheng, N Parmar, D Tran, A Vaswani, P Koanantakool, ...
Advances in neural information processing systems 31, 2018
Heterogeneous-race-free memory models
DR Hower, BA Hechtman, BM Beckmann, BR Gaster, MD Hill, ...
Proceedings of the 19th international conference on Architectural support …, 2014
QuickRelease: A throughput-oriented approach to release consistency on GPUs
BA Hechtman, S Che, DR Hower, Y Tian, BM Beckmann, MD Hill, ...
2014 IEEE 20th International Symposium on High Performance Computer …, 2014
GSPMD: general and scalable parallelization for ML computation graphs
Y Xu, HJ Lee, D Chen, B Hechtman, Y Huang, R Joshi, M Krikun, ...
arXiv preprint arXiv:2105.04663, 2021
Scale mlperf-0.6 models on google tpu-v3 pods
S Kumar, V Bitorff, D Chen, C Chou, B Hechtman, HJ Lee, N Kumar, ...
arXiv preprint arXiv:1909.09756, 2019
Large-scale discrete Fourier transform on TPUs
T Lu, YF Chen, B Hechtman, T Wang, J Anderson
IEEE Access 9, 93422-93432, 2021
Unified scaling laws for routed language models
A Clark, D de Las Casas, A Guy, A Mensch, M Paganini, J Hoffmann, ...
International conference on machine learning, 4057-4086, 2022
Evaluating cache coherent shared virtual memory for heterogeneous multicore chips
BA Hechtman, DJ Sorin
2013 IEEE International Symposium on Performance Analysis of Systems and …, 2013
Automatic cross-replica sharding of weight update in data-parallel training
Y Xu, HJ Lee, D Chen, H Choi, B Hechtman, S Wang
arXiv preprint arXiv:2004.13336, 2020
A flexible approach to autotuning multi-pass machine learning compilers
PM Phothilimthana, A Sabne, N Sarda, KS Murthy, Y Zhou, ...
2021 30th International Conference on Parallel Architectures and Compilation …, 2021
Overlap communication with dependent computation via decomposition in large deep learning models
S Wang, J Wei, A Sabne, A Davis, B Ilbeyi, B Hechtman, D Chen, ...
Proceedings of the 28th ACM International Conference on Architectural …, 2022
TPU-KNN: K nearest neighbor search at peak flop/s
F Chern, B Hechtman, A Davis, R Guo, D Majnemer, S Kumar
Advances in Neural Information Processing Systems 35, 15489-15501, 2022
Exploring the limits of Concurrency in ML Training on Google TPUs
S Kumar, Y Wang, C Young, J Bradbury, N Kumar, D Chen, A Swing
Proceedings of Machine Learning and Systems 3, 81-92, 2021
Method for memory consistency among heterogeneous computer components
DR Hower, MD Hill, D Wood, SK Reinhardt, BR Gaster, BA Hechtman, ...
US Patent 9,361,118, 2016
Hierarchical write-combining cache coherence
BA Hechtman, BM Beckmann
US Patent 9,396,112, 2016
General padding support for convolution on systolic arrays
DA Majnemer, BA Hechtman, BH Roune
US Patent 11,449,739, 2022
Data remapping for heterogeneous processor
S Che, B Beckmann, B Hechtman
US Patent App. 14/055,221, 2015
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