5 papers
AAFLOW: Scalable Patterns for Agentic AI Workflows
Arup Kumar Sarker, Mills Staylor, Aymen Alsaadi +3
Agentic workflows in large language model systems integrate retrieval, reasoning, and memory, but existing frameworks suffer from scalability and reproducibility limitations due to…
An MLCommons Scientific Benchmarks Ontology
Ben Hawks, Gregor von Laszewski, Matthew D. Sinclair +6
Scientific machine learning research spans diverse domains and data modalities, yet existing benchmark efforts remain siloed and lack standardization. This makes novel and transfor…
Combining Serverless and High-Performance Computing Paradigms to support ML Data-Intensive Applications
Mills Staylor, Arup Kumar Sarker, Gregor von Laszewski +3
Data is found everywhere, from health and human infrastructure to the surge of sensors and the proliferation of internet-connected devices. To meet this challenge, the data enginee…
Towards Experiment Execution in Support of Community Benchmark Workflows for HPC
Gregor von Laszewski, Wesley Brewer, Sean R. Wilkinson +5
A key hurdle is demonstrating compute resource capability with limited benchmarks. We propose workflow templates as a solution, offering adaptable designs for specific scientific a…
Deep RC: A Scalable Data Engineering and Deep Learning Pipeline
Arup Kumar Sarker, Aymen Alsaadi, Alexander James Halpern +7
Significant obstacles exist in scientific domains including genetics, climate modeling, and astronomy due to the management, preprocess, and training on complicated data for deep l…