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Performance enhancement of artificial intelligence: A survey
The advent of machine learning (ML) and Artificial intelligence (AI) has brought about a
significant transformation across multiple industries, as it has facilitated the automation of …
significant transformation across multiple industries, as it has facilitated the automation of …
A survey of resource-efficient llm and multimodal foundation models
Large foundation models, including large language models (LLMs), vision transformers
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …
(ViTs), diffusion, and LLM-based multimodal models, are revolutionizing the entire machine …
Efficient large-scale language model training on gpu clusters using megatron-lm
Large language models have led to state-of-the-art accuracies across several tasks.
However, training these models efficiently is challenging because: a) GPU memory capacity …
However, training these models efficiently is challenging because: a) GPU memory capacity …
PanGu-: Large-scale Autoregressive Pretrained Chinese Language Models with Auto-parallel Computation
Large-scale Pretrained Language Models (PLMs) have become the new paradigm for
Natural Language Processing (NLP). PLMs with hundreds of billions parameters such as …
Natural Language Processing (NLP). PLMs with hundreds of billions parameters such as …
Resource-efficient algorithms and systems of foundation models: A survey
Large foundation models, including large language models, vision transformers, diffusion,
and large language model based multimodal models, are revolutionizing the entire machine …
and large language model based multimodal models, are revolutionizing the entire machine …
Decentralized training of foundation models in heterogeneous environments
Training foundation models, such as GPT-3 and PaLM, can be extremely expensive, often
involving tens of thousands of GPUs running continuously for months. These models are …
involving tens of thousands of GPUs running continuously for months. These models are …
GNNLab: a factored system for sample-based GNN training over GPUs
We propose GNNLab, a sample-based GNN training system in a single machine multi-GPU
setup. GNNLab adopts a factored design for multiple GPUs, where each GPU is dedicated to …
setup. GNNLab adopts a factored design for multiple GPUs, where each GPU is dedicated to …
P3: Distributed deep graph learning at scale
Graph Neural Networks (GNNs) have gained significant attention in the recent past, and
become one of the fastest growing subareas in deep learning. While several new GNN …
become one of the fastest growing subareas in deep learning. While several new GNN …
Oobleck: Resilient distributed training of large models using pipeline templates
Oobleck enables resilient distributed training of large DNN models with guaranteed fault
tolerance. It takes a planning-execution co-design approach, where it first generates a set of …
tolerance. It takes a planning-execution co-design approach, where it first generates a set of …
Lyra: Elastic scheduling for deep learning clusters
Organizations often build separate training and inference clusters for deep learning, and use
separate schedulers to manage them. This leads to problems for both: inference clusters …
separate schedulers to manage them. This leads to problems for both: inference clusters …