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Deep learning workload scheduling in gpu datacenters: A survey
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 …
development of a DL model is a time-consuming and resource-intensive procedure. Hence …
Current issues and perspectives in nanosensors-based artificial olfactory systems for breath diagnostics and environmental exposure monitoring
Artificial olfactory systems that can provide sustainable monitoring and non-invasive
diagnostics are emerging for environmental exposure detection and exhaled breath …
diagnostics are emerging for environmental exposure detection and exhaled breath …
{MLaaS} in the wild: Workload analysis and scheduling in {Large-Scale} heterogeneous {GPU} clusters
With the sustained technological advances in machine learning (ML) and the availability of
massive datasets recently, tech companies are deploying large ML-as-a-Service (MLaaS) …
massive datasets recently, tech companies are deploying large ML-as-a-Service (MLaaS) …
Oort: Efficient federated learning via guided participant selection
Federated Learning (FL) is an emerging direction in distributed machine learning (ML) that
enables in-situ model training and testing on edge data. Despite having the same end goals …
enables in-situ model training and testing on edge data. Despite having the same end goals …
{INFaaS}: Automated model-less inference serving
Despite existing work in machine learning inference serving, ease-of-use and cost efficiency
remain challenges at large scales. Developers must manually search through thousands of …
remain challenges at large scales. Developers must manually search through thousands of …
A unified architecture for accelerating distributed {DNN} training in heterogeneous {GPU/CPU} clusters
Data center clusters that run DNN training jobs are inherently heterogeneous. They have
GPUs and CPUs for computation and network bandwidth for distributed training. However …
GPUs and CPUs for computation and network bandwidth for distributed training. However …
Loongserve: Efficiently serving long-context large language models with elastic sequence parallelism
The context window of large language models (LLMs) is rapidly increasing, leading to a
huge variance in resource usage between different requests as well as between different …
huge variance in resource usage between different requests as well as between different …
Fast distributed inference serving for large language models
Large language models (LLMs) power a new generation of interactive AI applications
exemplified by ChatGPT. The interactive nature of these applications demands low latency …
exemplified by ChatGPT. The interactive nature of these applications demands low latency …
Characterization of large language model development in the datacenter
Large Language Models (LLMs) have presented impressive performance across several
transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster …
transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster …
Parrot: Efficient Serving of {LLM-based} Applications with Semantic Variable
The rise of large language models (LLMs) has enabled LLM-based applications (aka AI
agents or co-pilots), a new software paradigm that combines the strength of LLM and …
agents or co-pilots), a new software paradigm that combines the strength of LLM and …