Benchmarking Performance of Deep Learning Model for Material Segmentation on Two HPC Systems
Performance Benchmarking of HPC systems is an ongoing effort that seeks to provide information that will allow for increased performance and improve the job schedulers that manage these systems. We develop a benchmarking tool that utilizes machine learning models and gathers performance data on GPU-accelerated nodes while they perform material segmentation analysis. The benchmark uses a ML model that has been converted from Caffe to PyTorch using the MMdnn toolkit and the MINC-2500 dataset. Performance data is gathered on two ERDC DSRC systems, Onyx and Vulcanite. The data reveals that while Vulcanite has faster model times in a large number of benchmarks, and it is also more subject to some environmental factors that can cause performances slower than Onyx. In contrast the model times from Onyx are consistent across benchmarks.
Code (0)
등록된 구현이 없습니다.
Tasks
BenchmarkingGPUMaterial SegmentationSimilar Papers 제목 키워드 기반
MatText: Do Language Models Need More than Text & Scale for Materials Modeling?
Effectively representing materials as text has the potential to leverage the vast advancements of large language models (LLMs) for discovering new materials. While LLMs have shown remarkable success in various domains, t…
BenchmarkingBenchML: an extensible pipelining framework for benchmarking representations of materials and molecules at scale
We introduce a machine-learning (ML) framework for high-throughput benchmarking of diverse representations of chemical systems against datasets of materials and molecules. The guiding principle underlying the benchmarkin…
BenchmarkingHyperparameter OptimizationregressionEvaluating Large and Lightweight Vision Models for Irregular Component Segmentation in E-Waste Disassembly
Precise segmentation of irregular and densely arranged components is essential for robotic disassembly and material recovery in electronic waste (e-waste) recycling. This study evaluates the impact of model architecture …
Data AugmentationBenchmarking the Performance of Bayesian Optimization across Multiple Experimental Materials Science Domains
In the field of machine learning (ML) for materials optimization, active learning algorithms, such as Bayesian Optimization (BO), have been leveraged for guiding autonomous and high-throughput experimentation systems. Ho…
Active LearningBayesian OptimisationBayesian OptimizationBenchmarking+3LLM4Mat-Bench: Benchmarking Large Language Models for Materials Property Prediction
Large language models (LLMs) are increasingly being used in materials science. However, little attention has been given to benchmarking and standardized evaluation for LLM-based materials property prediction, which hinde…
BenchmarkingPredictionProperty Prediction