We present the GPU version of DeePMD-kit, which, upon training a deep neural network
model using ab initio data, can drive extremely large-scale molecular dynamics (MD) …

Historically, improvements in GPU-based high performance computing have been tightly
coupled to transistor scaling. As Moore's law slows down, and the number of transistors per …

Processing data in or near memory (PIM), as opposed to in conventional computational units
in a processor, can greatly alleviate the performance and energy penalties of data transfers …

There has been a growing interest in exploiting contextual information in addition to local
features to detect and localize multiple object categories in an image. Context models can …

Graphics processing units (GPUs) have become a very powerful platform embracing a
concept of heterogeneous many-core computing. However, application domains of GPUs …

Many processors today integrate a CPU and GPU on the same die, which allows them to
share resources like physical memory and lowers the cost of CPU-GPU communication. As …

Heterogeneous system architectures are evolving towards tighter integration among
devices, with emerging features such as shared virtual memory, memory coherence, and …

Modern GPUs are broadly adopted in many multitasking environments, including data
centers and smartphones. However, the current support for the scheduling of multiple GPU …

General-purpose computing on graphics processing units, also known as GPGPU, is a
burgeoning technique to enhance the computation of parallel programs. Applying this …

A GPU's computing power lies in its abundant memory bandwidth and massive parallelism.
However, its hardware thread schedulers, despite being able to quickly distribute …