EDBT 2026 Demo / reviewers in the wild / expert
Zike Xu
dblp:441/1224
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2026
0009-0007-3658-9864ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Graphics, computer vision, multimedia, augmented reality and games · 1 · 1 first-author · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
High-performance computing · 75% GPUs and heterogeneous computing · 25% | |
| Computer graphics and multimedia
1 paper |
Computer animation and physical simulation · 100% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing › scientific computing systems
computational fluid dynamics |
1.0 | 1 | 2026 | Kinetic Predicted-Moment Flux Reconstruction for High-Order High-Performance Fluid Simulation · ACM Trans. Graph. 2026 |
GPUs and heterogeneous computing
GPU computing |
1.0 | 1 | 2026 | Kinetic Predicted-Moment Flux Reconstruction for High-Order High-Performance Fluid Simulation · ACM Trans. Graph. 2026 |
High-performance computing
performance optimization at scale |
1.0 | 1 | 2026 | Kinetic Predicted-Moment Flux Reconstruction for High-Order High-Performance Fluid Simulation · ACM Trans. Graph. 2026 |
High-performance computing
scientific computing systems |
1.0 | 1 | 2026 | Kinetic Predicted-Moment Flux Reconstruction for High-Order High-Performance Fluid Simulation · ACM Trans. Graph. 2026 |
Computer animation and physical simulation
fluid simulation |
0.3 | 1 | 2026 | Kinetic Predicted-Moment Flux Reconstruction for High-Order High-Performance Fluid Simulation · ACM Trans. Graph. 2026 |
Methods — techniques the papers use, named apart from their topics
multigrid · 2.0gas-kinetic scheme · 2.0flux reconstruction · 2.0
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2026 | Kinetic Predicted-Moment Flux Reconstruction for High-Order High-Performance Fluid SimulationabstractThe simultaneous pursuit of high fidelity, large computational throughput, and a minimal memory footprint has long constituted the central challenge in fluid simulation research. Yet state-of-the-art methods struggle to reconcile all these objectives, and often entail navigating trade-offs among them. We present Kinetic Predicted-Moment Flux Reconstruction (KPM-FR), a high-order kinetic-based scheme for low-Mach-number weakly compressible flows that advances all three fronts within a single framework. KPM-FR is a flux-form fluid flow solver rooted in the principles of the gas-kinetic scheme (GKS), deriving numerical fluxes from the locally evolved Boltzmann-BGK equation to recover Navier-Stokes (NS) solutions. Departing from the GKS and its variants, it carries out kinetic evolution entirely in moment space within the high-order flux reconstruction (FR) framework through a concise predictor-corrector scheme. This translates to two fused GPU kernels per time step, streamlining computation and confining intermediate data to on-chip memory. This design confers several practical advantages. First, compared to conventional high-fidelity lattice Boltzmann methods (LBM), the moment-based formulation reduces the per-point memory footprint by more than fivefold. Second, at matched resolutions, its high-order spatial formulation exhibits markedly lower numerical dissipation, preserving fine-scale vortical structures with greater fidelity. Combining these advantages with a near-saturated throughput exceeding 8 billion solution-point updates per second on a single consumer GPU, KPM-FR delivers large-scale fluid simulation on commodity hardware. Quantitative benchmarks confirm highorder spatial convergence and spectral-like dissipation characteristics, while validation against reference data for flow past solid bodies verifies its practical accuracy. Ultimately, we demonstrate the versatility of KPM-FR across complex geometries and large-scale turbulent flows, capturing multiscale structures with 1.8 billion solution points on a single desktop workstation. Zike Xu, Xiaopei Liu |
ACM Trans. Graph. | 1 |