High performance multi-GPU computing becomes an inevitable trend due to the ever-
increasing demand on computation capability in emerging domains such as deep learning …

GPUs have long been discussed as accelerators for database query processing because of
their high processing power and memory bandwidth. However, two main challenges limit the …

Recently, software systems powered by deep learning (DL) techniques have significantly
facilitated people's lives in many aspects. As the backbone of these DL systems, various DL …

High-performance quantum circuit simulation in a classic HPC is still imperative in the NISQ
era. Observing that the major obstacle of scalable state-vector quantum simulation arises …

CUDA Unified Memory improves the GPU pro-grammability and also enables GPU memory
oversubscription. Recently, two advanced memory features, memory advises and …

Modern deep learning models have been exploited in various domains, including computer
vision (CV), natural language processing (NLP), search and recommendation. In practical AI …

As transistor scaling becomes increasingly more difficult to achieve, scaling the core count
on a single GPU chip has also become extremely challenging. As the volume of data to …

As quantum computers evolve, simulations of quantum programs on classical computers will
be essential in validating quantum algorithms, understanding the effect of system noise, and …

Graph Neural Networks (GNNs) have emerged as a promising class of Machine Learning
algorithms to train on non-euclidean data. GNNs are widely used in recommender systems …

GPUs have become a mainstream accelerator for database operations such as sorting. Most
GPU sorting algorithms are single-GPU approaches. They neither harness the full …