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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 …
{AlpaServe}: Statistical multiplexing with model parallelism for deep learning serving
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 …
learning model beyond the memory limits of a single device. In this paper, we demonstrate …
Towards efficient generative large language model serving: A survey from algorithms to systems
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 …
models (LLMs) stand at the forefront, revolutionizing how we interact with our data. However …
Microsecond-scale preemption for concurrent {GPU-accelerated}{DNN} inferences
Many intelligent applications like autonomous driving and virtual reality require running both
latency-critical and best-effort DNN inference tasks to achieve both real time and work …
latency-critical and best-effort DNN inference tasks to achieve both real time and work …
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 …
Beware of fragmentation: Scheduling {GPU-Sharing} workloads with fragmentation gradient descent
Large tech companies are piling up a massive number of GPUs in their server fleets to run
diverse machine learning (ML) workloads. However, these expensive devices often suffer …
diverse machine learning (ML) workloads. However, these expensive devices often suffer …
Llmcad: Fast and scalable on-device large language model inference
Generative tasks, such as text generation and question answering, hold a crucial position in
the realm of mobile applications. Due to their sensitivity to privacy concerns, there is a …
the realm of mobile applications. Due to their sensitivity to privacy concerns, there is a …
Orion: Interference-aware, fine-grained GPU sharing for ML applications
GPUs are critical for maximizing the throughput-per-Watt of deep neural network (DNN)
applications. However, DNN applications often underutilize GPUs, even when using large …
applications. However, DNN applications often underutilize GPUs, even when using large …
Power-aware Deep Learning Model Serving with {μ-Serve}
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 …
pressing need to reduce the energy consumption of a model-serving cluster while …
Transparent {GPU} sharing in container clouds for deep learning workloads
Containers are widely used for resource management in datacenters. A common practice to
support deep learning (DL) training in container clouds is to statically bind GPUs to …
support deep learning (DL) training in container clouds is to statically bind GPUs to …