REVIEW 10 cited by
Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs
Not yet reviewed by Pith; the record is open.
This paper has not been read by Pith yet. Machine review is queued; the pith claim, tier, and objections will appear here once it completes.
SPECIMEN: schema-true, not a live event
T0 review · schema-true
One-sentence machine reading of the paper's core claim.
pith:XXXXXXXX · record.json · timestamp
Performance Portable Monte Carlo Particle Transport on Intel, NVIDIA, and AMD GPUs
read the original abstract
OpenMC is an open source Monte Carlo neutral particle transport application that has recently been ported to GPU using the OpenMP target offloading model. We examine the performance of OpenMC at scale on the Frontier, Polaris, and Aurora supercomputers, demonstrating that performance portability has been achieved by OpenMC across all three major GPU vendors (AMD, NVIDIA, and Intel). OpenMC's GPU performance is compared to both the traditional CPU-based version of OpenMC as well as several other state-of-the-art CPU-based Monte Carlo particle transport applications. We also provide historical context by analyzing OpenMC's performance on several legacy GPU and CPU architectures. This work includes some of the first published results for a scientific simulation application at scale on a supercomputer featuring Intel's Max series "Ponte Vecchio" GPUs. It is also one of the first demonstrations of a large scientific production application using the OpenMP target offloading model to achieve high performance on all three major GPU platforms.
Forward citations
Cited by 10 Pith papers
-
Transformers with Physics-Informed Encodings and Simulation-Based Inference for Robust Detection of Eccentric Binary Black Holes in Pulsar Timing Array Data
Physics-informed Transformer encodings plus conditional normalizing flows yield sharper, better-calibrated posteriors for eccentric BBHs in white-noise PTA data than physics-agnostic SBI baselines.
-
OpenAaaS: An Open Agent-as-a-Service Framework for Distributed Materials-Informatics Research
OpenAaaS is a hierarchical agent-as-a-service system that enables secure multi-agent collaboration for materials informatics by moving code to data rather than data to code.
-
Robust 4D Visual Geometry Transformer with Uncertainty-Aware Priors
The Robust 4D Visual Geometry Transformer with Uncertainty-Aware Priors outperforms prior methods on dynamic benchmarks by cutting Mean Accuracy error 13.43% and raising segmentation F-measure 10.49% via three uncerta...
-
SIC3D: Style Image Conditioned Text-to-3D Gaussian Splatting Generation
SIC3D generates text-to-3D objects with Gaussian splatting then stylizes them using Variational Stylized Score Distillation loss plus scaling regularization to improve style match and geometry fidelity.
-
Directing the World: Fast Autoregressive Video Generation with Compositional Human-Camera Control
A decoupled-control autoregressive video model using Fast-Slow Memory training, dynamic projection, and staged camera control to produce stable long-horizon outputs with human and viewpoint guidance.
-
SaluNet: Enabling Total Plasticity in Normalization-Free Deep Networks
SaluNet replaces normalization layers with the SALU activation and reports competitive accuracies on CIFAR-10/100 and ImageNet-1K without normalization.
-
Multilingual Training and Evaluation Resources for Vision-Language Models
Releases regenerated multilingual training data and translated benchmarks for VLMs in five languages and demonstrates consistent benefits from multilingual training over English-only baselines.
-
Improving Factuality in LLMs via Inference-Time Knowledge Graph Construction
A framework for inference-time knowledge graph construction and expansion improves factual accuracy in LLMs on three QA benchmarks by combining internal LLM knowledge with selective external retrieval.
-
Multilingual Training and Evaluation Resources for Vision-Language Models
A multilingual VLM training corpus and evaluation benchmark suite for five European languages shows consistent cross-lingual transfer gains without English degradation.
-
Training Language Models to Use Prolog as a Tool
Fine-tuning Qwen2.5-3B with GRPO on GSM8K to use Prolog yields competitive zero-shot MMLU performance but exposes an accuracy-auditability trade-off interpreted as reward hacking.
discussion (0)
Sign in with ORCID, Apple, or X to comment. Anyone can read and Pith papers without signing in.