Model parallelism is conventionally viewed as a method to scale a single large deep
learning model beyond the memory limits of a single device. In this paper, we demonstrate …

Deep learning (DL) has demonstrated its remarkable success in a wide variety of fields. The
development of a DL model is a time-consuming and resource-intensive procedure. Hence …

The practical needs of the" right to be forgotten" and poisoned data removal call for efficient
machine unlearning techniques, which enable machine learning models to unlearn, or to …

Large foundation models, including large language models, vision transformers, diffusion,
and large language model based multimodal models, are revolutionizing the entire machine …

In the rapidly evolving landscape of artificial intelligence (AI), generative large language
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …

With the increasing popularity of large deep learning model-serving workloads, there is a
pressing need to reduce the energy consumption of a model-serving cluster while …

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 …

Training Deep Neural Networks (DNNs) is a popular workload in both enterprises and cloud
data centers. Existing schedulers for DNN training consider GPU as the dominant resource …

In public clouds, users must manually select a datacenter region to upload their ML training
data and launch ML training workloads in the same region to ensure data and computation …

Training Deep Learning (DL) model requires multiple resource types, including CPUs,
GPUs, storage IO, and network IO. Advancements in DL have produced a wide spectrum of …