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Found 7 papers, 3 papers with code
Nonuniform-Tensor-Parallelism: Mitigating GPU failure impact for Scaled-up LLM Training
We also propose a rack-design with improved electrical and thermal capabilities in order to sustain power-boosting of scale-up domains that have experienced failures; combined with NTP, this can allow the DP replica with the reduced TP degree (i. e., with failed GPUs) to keep up with the others, thereby achieving near-zero throughput loss for large-scale LLM training.
PipeFill: Using GPUs During Bubbles in Pipeline-parallel LLM Training
Unfortunately, PP model training can use GPUs inefficiently, especially at large scale, due to idle GPU time caused by pipeline bubbles, which are often 15-30% and can exceed 60% of the training job's GPU allocation.
GraphPipe: Improving Performance and Scalability of DNN Training with Graph Pipeline Parallelism
GraphPipe partitions a DNN into a graph of stages, optimizes micro-batch schedules for these stages, and parallelizes DNN training using the discovered GPP strategies.
SpecInfer: Accelerating Generative Large Language Model Serving with Tree-based Speculative Inference and Verification
Our evaluation shows that SpecInfer outperforms existing LLM serving systems by 1. 5-2. 8x for distributed LLM inference and by 2. 6-3. 5x for offloading-based LLM inference, while preserving the same generative performance.
HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks
However, the search space for a mixed-precision quantization is exponential in the number of layers.
Unsupervised Projection Networks for Generative Adversarial Networks
We propose the use of unsupervised learning to train projection networks that project onto the latent space of an already trained generator.
Large batch size training of neural networks with adversarial training and second-order information
Our method exceeds the performance of existing solutions in terms of both accuracy and the number of SGD iterations (up to 1\% and $5\times$, respectively).
