4 citations · 4 across the 5 of their papers we have counts for
5 papers
A Query-Efficient Stochastic Volume Rendering Framework for Time-Varying Implicit Neural Volumes
Alper Sahistan, Haichao Miao, Zhimin Li +3
Time-varying implicit neural representations (INRs) provide a compact representation of scientific volumes and, for modalities such as dynamic X-ray computed tomography (CT), are o…
RAFI -- A Ray/Work Forwarding Infrastructure for Data Parallel Multi-Node/Multi-GPU Computing
Ingo Wald, Serkan Demirci, Alper Sahistan +7
We present RaFI, a CUDA and MPI based software framework that simplifies the task of building GPU-enabled data-parallel software where rays or similar work items need to migrate be…
Animating Petascale Time-varying Data on Commodity Hardware with LLM-assisted Scripting
Ishrat Jahan Eliza, Xuan Huang, Aashish Panta +4
Scientists face significant visualization challenges as time-varying datasets grow in speed and volume, often requiring specialized infrastructure and expertise to handle massive d…
Beyond ExaBricks: GPU Volume Path Tracing of AMR Data
Stefan Zellmann, Qi Wu, Alper Sahistan +2
Adaptive Mesh Refinement (AMR) is becoming a prevalent data representation for scientific visualization. Resulting from large fluid mechanics simulations, the data is usually cell…
GPU-based Data-parallel Rendering of Large, Unstructured, and Non-convexly Partitioned Data
Alper Sahistan, Serkan Demirci, Ingo Wald +4
Computational fluid dynamic simulations often produce large clusters of finite elements with non-trivial, non-convex boundaries and uneven distributions among compute nodes, posing…