Abstract The Joint Laboratory on Extreme-Scale Computing (JLESC) was initiated at the
same time lossy compression for scientific data became an important topic for the scientific …

Lossy compression and asynchronous I/O are two of the most effective solutions for reducing
storage overhead and enhancing I/O performance in large-scale high-performance …

Training wide and deep neural networks (DNNs) require large amounts of storage resources
such as memory because the intermediate activation data must be saved in the memory …

Parallel hardware devices (eg, graphics processor units) have limited high-bandwidth
memory capacity. This negatively impacts the training of deep neural networks (DNNs) by …

Lossy compression is one of the most efficient solutions to reduce storage overhead and
improve I/O performance for HPC applications. However, existing parallel I/O libraries …

Communication compression is a crucial technique for modern distributed learning systems
to alleviate their communication bottlenecks over slower networks. Despite recent intensive …

Learning and Artificial Intelligence (ML/AI) techniques have become increasingly prevalent
in high performance computing (HPC). However, these methods depend on vast volumes of …

Recently, the recurrent neural network, or its most popular type—the Long Short Term
Memory (LSTM) network—has achieved great success in a broad spectrum of real-world …

Communication compression is a crucial technique for modern distributed learning systems
to alleviate their communication bottlenecks over slower networks. Despite recent intensive …

Low-precision training has emerged as a practical approach, saving the cost of time,
memory, and energy during deep neural networks (DNNs) training. Typically, the use of …