The past decade has witnessed the superior power of deep neural networks (DNNs) in
applications across various domains. However, training a high-quality DNN remains a non …

The VTK-m software library enables scientific visualization on exascale-class
supercomputers. Exascale machines are particularly challenging for software development …

Rapidly growing data sizes of scientific simulations pose significant challenges for
interactive visualization and analysis techniques. In this work, we propose a compact …

Despite the popularity of in-situ analytics in scientific computing, there is only limited work to
date on in-situ analytics for simulations running on GPUs. Notably, two unaddressed …

In this paper, we propose a correlation-aware probabilistic data summarization technique to
efficiently analyze and visualize large-scale multi-block volume data generated by massively …

The use of distribution-based data representation to handle large-scale scientific datasets is
a promising approach. Distribution-based approaches often transform a scientific dataset …

Probabilistic summarization is the process of creating compact statistical representations of
the original data. It is used for data reduction, and to facilitate efficient post-hoc visualization …

透過基於分布的資料表示法來處理大規模的科學資料集是一種新興且相當有潛力的方法.
這種資料表示法基本上是將科學資料集轉換為許多分布來表示, 並且每個分布皆由少量的樣本 …

The study of multiphase flow is essential for understanding the complex interactions of
various materials. In particular, when designing chemical reactors such as fluidized bed …

Heterogeneous, dense computing architectures consisting of several accelerators, such as
GPUs, attached to general-purpose CPUs are now integral High-Performance Computing …