Exploring memory consistency for massively-threaded throughput-oriented processors. - Blake A. Hechtman; Daniel J. Sorin (ISCA, 2013)

Evaluating cache coherent shared virtual memory for heterogeneous multicore chips. - Blake A. Hechtman; Daniel J. Sorin (ISPASS, 2013)

Heterogeneous-race-free memory models. - Derek R. Hower; Blake A. Hechtman; Bradford M. Beckmann; Benedict R. Gaster; Mark D. Hill; Steven K. Reinhardt; David A. Wood 001 (ASPLOS, 2014)

QuickRelease: A throughput-oriented approach to release consistency on GPUs. - Blake A. Hechtman; Shuai Che; Derek R. Hower; Yingying Tian; Bradford M. Beckmann; Mark D. Hill; Steven K. Reinhardt; David A. Wood 001 (HPCA, 2014)

Exploiting Parallelism in GPUs. - Blake A. Hechtman (2014)

TREES: A CPU/GPU Task-Parallel Runtime with Explicit Epoch Synchronization. - Blake A. Hechtman; Andrew D. Hilton; Daniel J. Sorin (CoRR, 2016)

Mesh-TensorFlow: Deep Learning for Supercomputers. - 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 (NeurIPS, 2018)

Scale MLPerf-0.6 models on Google TPU-v3 Pods. - 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 (CoRR, 2019)

Automatic Cross-Replica Sharding of Weight Update in Data-Parallel Training. - Yuanzhong Xu; HyoukJoong Lee; Dehao Chen; Hongjun Choi; Blake A. Hechtman; Shibo Wang (CoRR, 2020)

Exploring the limits of Concurrency in ML Training on Google TPUs. - 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; et al. (CoRR, 2020)

A Flexible Approach to Autotuning Multi-Pass Machine Learning Compilers. - Phitchaya Mangpo Phothilimthana; Amit Sabne; Nikhil Sarda; Karthik Srinivasa Murthy; Yanqi Zhou; Christof Angermueller; Mike Burrows; Sudip Roy 002; Ketan Mandke; Rezsa Farahani; Yu Emma Wang; Berkin Ilbeyi; et al. (PACT, 2021)

Scaling Local Self-Attention for Parameter Efficient Visual Backbones. - Ashish Vaswani; Prajit Ramachandran; Aravind Srinivas; Niki Parmar; Blake A. Hechtman; Jonathon Shlens (CVPR, 2021)

Large-Scale Discrete Fourier Transform on TPUs. - Tianjian Lu; Yi-Fan Chen; Blake A. Hechtman; Tao Wang; John R. Anderson (IEEE Access, 2021)

GSPMD: General and Scalable Parallelization for ML Computation Graphs. - 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; et al. (CoRR, 2021)

Scaling Language Models: Methods, Analysis & Insights from Training Gopher. - 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; et al. (CoRR, 2021)

Unified Scaling Laws for Routed Language Models. - 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 002; Eliza Rutherford; et al. (ICML, 2022)

Unified Scaling Laws for Routed Language Models. - 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 002; Eliza Rutherford; et al. (CoRR, 2022)