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Channel and filter parallelism for large-scale CNN training.

, , , , , и . SC, стр. 10:1-10:20. ACM, (2019)

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Newton Maruyama

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Naoya Koseki

 

Другие публикации лиц с тем же именем

Poster: fast GPU read alignment with burrows wheeler transform based index., , и . SC Companion, стр. 21-22. ACM, (2011)Scalable Kernel Fusion for Memory-Bound GPU Applications., и . SC, стр. 191-202. IEEE Computer Society, (2014)Effective Quantization Approaches for Recurrent Neural Networks., , , , и . IJCNN, стр. 1-8. IEEE, (2018)Data-centric GPU-based adaptive mesh refinement., и . IA3@SC, стр. 3:1-3:7. ACM, (2015)Scaling FMM with Data-Driven OpenMP Tasks on Multicore Architectures., , , , , , и . IWOMP, том 9903 из Lecture Notes in Computer Science, стр. 156-170. (2016)Highly optimized full GPU-acceleration of non-hydrostatic weather model SCALE-LES., и . CLUSTER, стр. 1-8. IEEE Computer Society, (2013)From FLOPS to BYTES: disruptive change in high-performance computing towards the post-moore era., , , , , , , , , и 1 other автор(ы). Conf. Computing Frontiers, стр. 274-281. ACM, (2016)CUDA vs OpenACC: Performance Case Studies with Kernel Benchmarks and a Memory-Bound CFD Application., , , и . CCGRID, стр. 136-143. IEEE Computer Society, (2013)Automated GPU Kernel Transformations in Large-Scale Production Stencil Applications., и . HPDC, стр. 259-270. ACM, (2015)Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism., , , , , и . IPDPS, стр. 210-220. IEEE, (2019)