The Sia scheduler efficiently assigns heterogeneous deep learning (DL) cluster resources to
elastic resource-adaptive jobs. Although some recent schedulers address one aspect or …

Introduced to enhance the efficiency of large language model (LLM) inference, speculative
decoding operates by having a smaller model generate a draft. A larger target model then …

With the slowing of Moore's law and decline of Dennard scaling, computing systems
increasingly rely on specialized hardware accelerators in addition to general-purpose …

Recent innovation in large language models (LLMs), and their myriad use-cases have
rapidly driven up the compute capacity demand for datacenter GPUs. Several cloud …

Deep Learning (DL) workloads have rapidly increased in popularity in enterprise clusters
and several new cluster schedulers have been proposed in recent years to support these …

Recent innovation in large language models (LLMs), and their myriad use cases have
rapidly driven up the compute demand for datacenter GPUs. Several cloud providers and …

Reliability in cloud AI infrastructure is crucial for cloud service providers, prompting the
widespread use of hardware redundancies. However, these redundancies can inadvertently …

Reliability in cloud AI infrastructure is crucial for cloud service providers, prompting the
widespread use of hardware redundancies. However, these redundancies can inadvertently …

Large-scale computing systems are increasingly using accelerators such as GPUs to enable
peta-and exa-scale levels of compute to meet the needs of Machine Learning (ML) and …

Distributed learning with multiple GPUs has been widely adopted to accelerate the training
process of large-scale deep neural networks. However, misconfiguration of the GPU clusters …