6 papers
Configurable and Hierarchical Allreduce
Valentino Guerrini, Ke Fan, Sidharth Kumar
MPI_Allreduce is among the most performance-critical collectives in large-scale scientific computing and distributed machine learning, yet the small- and medium-message regime rema…
Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation
Landon Dyken, Sharmistha Chakrabarti, Nathan Debardeleben +4
Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying…
Volume Encoding Gaussians: Transfer Function-Agnostic 3D Gaussians for Volume Rendering
Landon Dyken, Andres Sewell, Will Usher +3
Visualizing the large-scale datasets output by HPC resources presents a difficult challenge, as the memory and compute power required become prohibitively expensive for end user sy…
Configurable Non-uniform All-to-all Algorithms
Ke Fan, Jens Domke, Seydou Ba +1
MPI_Alltoallv generalizes the uniform all-to-all communication (MPI_Alltoall) by enabling the exchange of data blocks of varied sizes among processes. This function plays a crucial…
Enabling Fast and Accurate Crowdsourced Annotation for Elevation-Aware Flood Extent Mapping
Landon Dyken, Saugat Adhikari, Pravin Poudel +4
Mapping the extent of flood events is a necessary and important aspect of disaster management. In recent years, deep learning methods have evolved as an effective tool to quickly l…
Speculative Progressive Raycasting for Memory Constrained Isosurface Visualization of Massive Volumes
Will Usher, Landon Dyken, Sidharth Kumar
New web technologies have enabled the deployment of powerful GPU-based computational pipelines that run entirely in the web browser, opening a new frontier for accessible scientifi…