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6 de may. de 2014 · Cliff Woolley is a senior developer technology engineer with NVIDIA. He received his master's degree in Computer Science from the University of Virginia in 2003, where he was among the earliest academic researchers to explore the use of GPUs for general purpose computing.
View Cliff Woolley’s profile on LinkedIn, a professional community of 1 billion members. Experience: NVIDIA · Location: San Jose, California, United States · 12 connections on LinkedIn.
3 de oct. de 2014 · cuDNN: Efficient Primitives for Deep Learning. Sharan Chetlur, Cliff Woolley, Philippe Vandermersch, Jonathan Cohen, John Tran, Bryan Catanzaro, Evan Shelhamer. We present a library of efficient implementations of deep learning primitives.
3 de mar. de 2024 · Abstract. We present a library of efficient implementations of deep learning primitives. Deep learning workloads are computationally intensive, and optimizing their kernels is difficult and time-consuming. As parallel architectures evolve, kernels must be reoptimized, which makes maintaining codebases difficult over time.
Cliff Woolley. University of Virginia. As GPU programmability has become more pervasive and GPU performance has become almost irresistibly appealing, increasing numbers of programmers have begun to recast applications of all sorts to make use of GPUs.
Forked from mjpost/sacrebleu. Reference BLEU implementation that auto-downloads test sets and reports a version string to facilitate cross-lab comparisons. Python. Something went wrong, please refresh the page to try again. If the problem persists, check the GitHub status page or contact support . cliffwoolley has 18 repositories available.
GTC 2020. NCCL (NVIDIA Collective Communication Library) optimizes inter-GPU communication on PCI, NVIDIA NVLink, and Infiniband, powering large-scale training for most DL frameworks, including Tensorflow, PyTorch, MXNet, and Chainer. Come discuss NCCL's performance, features, and latest advances.
