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 …

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 …

Over the years, accelerating neural networks with quantization has been widely studied.
Unfortunately, prior efforts with diverse precisions (eg, 1-bit weights and 2-bit activations) are …

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 …

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 …

The recent advent of compute-intensive GPU architecture has allowed application
developers to explore high-order 3D stencils for better computational accuracy. A common …

Despite foreseeing tremendous speedups over conventional deep neural networks, the
performance advantage of binarized neural networks (BNNs) has merely been showcased …

Binarized neural networks (or BNNs) promise tremendous performance improvement over
traditional DNNs through simplified bit-level computation and significantly reduced memory …

Multi-accelerator servers are increasingly being deployed in shared multi-tenant
environments (such as in cloud data centers) in order to meet the demands of large-scale …

Stencil computations are widely used in high performance computing (HPC) applications.
Many HPC platforms utilize the high computation capability of GPUs to accelerate stencil …