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Hanayo: Harnessing wave-like pipeline parallelism for enhanced large model training efficiency
Large-scale language models have become increasingly challenging and expensive to
train. Among various methods addressing this issue, Pipeline Parallelism has been widely …
train. Among various methods addressing this issue, Pipeline Parallelism has been widely …
Bpipe: Memory-balanced pipeline parallelism for training large language models
Pipeline parallelism is a key technique for training large language models within GPU
clusters. However, it often leads to a memory imbalance problem, where certain GPUs face …
clusters. However, it often leads to a memory imbalance problem, where certain GPUs face …
Baechi: fast device placement of machine learning graphs
Machine Learning graphs (or models) can be challenging or impossible to train when either
devices have limited memory, or the models are large. Splitting the model graph across …
devices have limited memory, or the models are large. Splitting the model graph across …
Merak: An efficient distributed dnn training framework with automated 3d parallelism for giant foundation models
Foundation models are in the process of becoming the dominant deep learning technology.
Pretraining a foundation model is always time-consuming due to the large scale of both the …
Pretraining a foundation model is always time-consuming due to the large scale of both the …
Model: memory optimizations for deep learning
The size of deep neural networks has grown exponentially in recent years. Unfortunately,
hardware devices have not kept pace with the rapidly increasing memory requirements. To …
hardware devices have not kept pace with the rapidly increasing memory requirements. To …
A Comparative Analysis of Distributed Training Strategies for GPT-2
The rapid advancement in Large Language Models has been met with significant
challenges in their training processes, primarily due to their considerable computational and …
challenges in their training processes, primarily due to their considerable computational and …
Characterizing multi-instance gpu for machine learning workloads
As machine learning (ML) becomes more and more popular, datacenter operators use
hardware accelerators such as GPUs to tackle the high computation demand of ML …
hardware accelerators such as GPUs to tackle the high computation demand of ML …
Unicron: Economizing self-healing llm training at scale
Training large-scale language models is increasingly critical in various domains, but it is
hindered by frequent failures, leading to significant time and economic costs. Current failure …
hindered by frequent failures, leading to significant time and economic costs. Current failure …
Automated tensor model parallelism with overlapped communication for efficient foundation model training
Deep learning is experiencing a rise in foundation models that are expected to lead in
various fields. The massive number of parameters necessitates the use of tensor model …
various fields. The massive number of parameters necessitates the use of tensor model …
Comparative analysis of AWS model deployment services
Amazon Web Services (AWS) offers three important Model Deployment Services for model
developers: SageMaker, Lambda, and Elastic Container Service (ECS). These services …
developers: SageMaker, Lambda, and Elastic Container Service (ECS). These services …