To keep up with increasing dataset sizes and model complexity, distributed training has
become a necessity for large machine learning tasks. Parameter servers ease the …

Trusted execution environments (TEE) such as In-tel's Software Guard Extension (SGX)
have been widely studied to boost security and privacy protection for the computation of …

Gradient Boosting Decision Tree (GBDT) is a widely used machine learning algorithm,
whose training involves both irregular computation and random memory access and is …

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 …

Our project is at the interface of Big Data and HPC–High-Performance Big Data computing
and this paper describes a collaboration between 7 collaborating Universities at Arizona …

This paper describes opportunities at the interface between large-scale simulations,
experiment design and control, machine learning (ML including deep learning DL) and High …

Subgraph counting aims to count the number of occurrences of a subgraph T (aka as a
template) in a given graph G. The basic problem has found applications in diverse domains …

Our project is at the interface of Big Data and HPC--High-Performance Big Data computing
and this paper describes a collaboration between 7 collaborating Universities at Arizona …

Biological tissues consist of various cell types, each contributing differently to tissue function
and disease responses. Bulk tissue gene expression measurements cannot directly reflect …

Abstract Machine learning (ML) has become an essential tool for solving problems that have
traditionally been challenging for computers, for example in the fields of natural language …