The appeal of serverless (FaaS) has triggered a growing interest on how to use it in data-
intensive applications such as ETL, query processing, or machine learning (ML). Several …

Deep learning (DL) is growing in popularity for many data analytics applications, including
among enterprises. Large business-critical datasets in such settings typically reside in …

Knowledge graph embedding (KGE) models represent the entities and relations of a
knowledge graph (KG) using dense continuous representations called embeddings. KGE …

Embedding models have been recognized as an effective learning paradigm for high-
dimensional data. However, a major embedding model training obstacle is that updating …

Numerous applications today rely on artificial intelligence over images. Image AI is,
however, extremely expensive. In particular, the inference cost of image AI dominates the …

Parameter servers (PSs) facilitate the implementation of distributed training for large
machine learning tasks. In this paper, we argue that existing PSs are inefficient for tasks that …

Parameter server (PS) as the state-of-the-art distributed framework for large-scale iterative
machine learning tasks has been extensively studied. However, existing PS-based systems …

Top-k sparsification has recently been widely used to reduce the communication volume in
distributed deep learning. However, due to the Sparse Gradient Accumulation (SGA) …

Machine learning (ML) training and scoring fundamentally relies on linear algebra programs
and more general tensor computations. Most ML systems utilize distributed parameter …

Parameter servers (PSs) ease the implementation of distributed machine learning systems,
but their performance can fall behind that of single machine baselines due to communication …