Tackle the Full Simulation Lifecycle With CMB

Simulation workflows are inherently complex, often involving a variety of computational tools that each play a crucial role in modeling, analyzing, and interpreting physical systems. From geometric kernel operations to mesh generation, numerical solvers, and post-processing analysis tools, these workflows require a high level of coordination and integration. Unfortunately, most simulation environments fall short of providing all the necessary tools in one cohesive package, and even those that do often lack the flexibility engineers need to incorporate specialized components. This lack of an adaptable, integrated environment has been a major stumbling block for advancing simulation technologies.

The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC’24) is the premier conference for supercomputing experts where they can discuss the latest developments, technologies, and applications in high-performance computing, networking, storage, and analysis. This conference provides researchers, industry leaders, and practitioners a platform to exchange ideas, share knowledge, and collaborate on cutting-edge advancements.

Building simulation workflows can be complicated. Trying to find and connect different tools to get the results you’re looking for can be inefficient, error-prone and take up valuable resources, including time and money. To make matters worse, sharing and deploying these workflows to fellow engineers, researchers, and academics tend to involve relying on hand written notes and scripts that can be difficult to follow and maintain.

LidarView: One software to read and process them all!  We’ve added to LidarView several new LiDARs from different manufacturers. They can be used in the same way, regardless of the model, to visualize live streams, replay .pcap records and running algorithms. You can even open two different LiDARs at the same time! Here is a […]

High-performance computing (HPC) is the perfect platform for AI and machine learning (AI/ML) research. This is because modern leadership class supercomputers rely on the massive parallelism of GPUs to perform scientific computation at exascale. Those same GPUs are the bedrock that the proliferation of AI/ML technologies are built upon. Therefore, these machines make it possible to combine traditional simulation workflows with AI/ML techniques and offer the potential to significantly accelerate scientific discovery through the use of partial to full surrogate models. It is essential for the community to fully unlock this potential in order to fully leverage modern supercomputers.

The third CMake 3.31 release candidate!

The Visualization Toolkit (VTK) is a widely used open-source solution for desktop post-processing analysis. Even though the library is written in C++, its sophisticated wrapping infrastructure allows developers to build entire visualization applications in Python and Java. Until recently, the python wrapper naively exposed the C++ functions to Python. For example, a C++ member function […]

VTK and ParaView have a huge library of visualization tools built on a data model that progressively adds conceptual assumptions based on how the data can be represented; the more information you can provide about your data, the more efficient the tools can be. We are adding a new data representation – called a cell grid – to address a large range of novel discretizations.