The calibration of Earth system model parameters is subject to data, time, and computational
constraints. The high dimensionality of this calibration problem, combined with errors arising …

Nowadays, data parallelism has been widely applied to train large datasets on distributed
deep learning clusters, but it has suffered from costly global parameter updates at batch …

High-performance computing (HPC) plays a crucial role in scientific computing, and the
efficient utilization of HPC to accomplish computational tasks remains a focal point of …

The ever-growing complexity of HPC applications and the computer architectures cost more
efforts than ever to learn application behaviors. In this paper, we propose the APMT, an …

Executing the resource-consuming parallel model, Earth System Models (ESMs), on the
many-core CPU clusters can effectively improve the performance due to the clusters' higher …

It is becoming increasingly significant for humans to predict and understand future climate
changes using coupled climate system models. Although the performance and scalability of …

Multimedia data has become a major data type in the Big Data era. The explosive volume of
such data and the increasing real-time requirement to retrieve useful information from it have …

The prediction ability of the climate system is highly depended on the efficient integration of
observations and simulations of the Earth, which is regarded as a canonical example of the …

The calibration of Earth System Model parameters is subject to both data, time and
computational constraints. The high dimensionality of this calibration problem, combined …

Coupled climate system models are important tools for climatologists to predict and
understand future climate. These models are usually resource-consuming due to the large …