TED-topo-non-expert-blocks.png
TPU-communication-performance.png
a100-dgx-intra-network-topo
dgx-2-network-topology.png
fwd-pass-of-an-moe-layer.png
mixture-of-experts-layer.png
moe_different_parallelism.png
se-moe-overall-training-design.png
st-differentiable-load-balancing-loss.png
st-token-routing-dynamics.png
switch-transformer-encoder-block.png
throughput-different-dispatch-layout.png
token-routing-dynamics.png
moe-meets-instruction-tuning.md
outrageously-large-neural-networks-the-sparsely-gated-mixture-of-experts-layer.md
scalable-and-efficient-moe-training.md
8-bit-floating-point-numbers.md
ActNN-Theorem-Prove-HaotianHe.pdf
GShard：Scaling Giant Models with Conditional Computation and Automatic Sharding.md
PyTorch Distributed-Data Parallel Training.md
Rammer-Enabling-Holistic-DL-Optimizations-with-rTasks.md
characterizing-deep-learning-training-workloads-on-alibaba-pai.md
deformable-convolutional-networks.md
designing-a-profiling-and-visualization-tool-for-scalable-and-in-depth-analysis-of-high-performance-gpu-clusters.md
fp8-formats-for-deep-learning.md
mixed-precision-training.md
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