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 …

This paper presents the design, implementation, and evaluation of the PyTorch distributed
data parallel module. PyTorch is a widely-adopted scientific computing package used in …

Deep learning (DL) shows its prosperity in a wide variety of fields. The development of a DL
model is a time-consuming and resource-intensive procedure. Hence, dedicated GPU …

The high computational and memory requirements of generative large language models
(LLMs) make it challenging to serve them cheaply. This paper aims to reduce the monetary …

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 …

Web applications rely heavily on software caches to achieve low-latency, high-throughput
services. To adapt to changing workloads, three types of learned caches (learned evictions) …

Serverless applications are typically composed of function workflows in which multiple short-
lived functions are triggered to exchange data in response to events or state changes …

Stragglers, Byzantine workers, and data privacy are the main bottlenecks in distributed cloud
computing. Some prior works proposed coded computing strategies to jointly address all …

Prediction serving systems are designed to provide large volumes of low-latency inferences
machine learning models. These systems mix data processing and computationally …

Large-scale computations are ubiquitous and demand exorbitant resources, with matrix
multiplication being a prominent example. Multiplying high-dimensional matrices is …