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) …

Smart buildings use advanced technologies to automate building functions. One important
function is occupancy detection using Internet of Things (IoT) sensors for smart buildings …

Large Language Models (LLMs) have presented impressive performance across several
transformative tasks. However, it is non-trivial to efficiently utilize large-scale cluster …

We present FFCV, a library for easy, fast, resource-efficient training of machine learning
models. FFCV speeds up model training by eliminating (often subtle) data bottlenecks from …

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 …

AI integration is revolutionizing the landscape of HPC simulations, enhancing the
importance, use, and performance of AI-driven HPC workflows. This paper surveys the …

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 …

Datacenter-scale AI training clusters consisting of thousands of domain-specific accelerators
(DSA) are used to train increasingly-complex deep learning models. These clusters rely on a …

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 …

When training a deep learning (DL) model, input data are pre-processed on CPUs and
transformed into tensors, which are then fed into GPUs for gradient computations of model …