1 citations · 1 across the 6 of their papers we have counts for
7 papers
LooseLeaf: Enabling Serverless GPUs for Computational Notebooks
Yusen Wu, Tingfeng Lan, Benjamin Carver +3
Computational notebooks reserve a GPU for the whole session, yet the GPU computes only while a cell executes and sits idle otherwise. Serverless execution would release the GPU bet…
OpRAG: A Resource-Deterministic Runtime for GPU-Backed Multi-Stage RAG Workflows
Arup Kumar Sarker, Mills Staylor, Aymen Alsaadi +3
Agentic retrieval-augmented generation (RAG) systems combine preprocessing, embedding, retrieval, memory access, context construction, generation, and vector-index updates. Althoug…
[AAFLOW+] Stateful Operator Abstraction with Zero-Copy Distributed KV Cache Orchestration for Multi-Agent Workflows
Arup Kumar Sarker, Alexander James Halpern, Mills Staylor +5
Multi-agent LLM systems increasingly integrate retrieval, planning, and reasoning, but remain fundamentally text-centric, requiring agents to repeatedly recompute shared context th…
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…
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…
Feasibility Study on Active Learning of Smart Surrogates for Scientific Simulations
Pradeep Bajracharya, Javier Quetzalcóatl Toledo-Marín, Geoffrey Fox +2
High-performance scientific simulations, important for comprehension of complex systems, encounter computational challenges especially when exploring extensive parameter spaces. Th…
Design and Implementation of an Analysis Pipeline for Heterogeneous Data
Arup Kumar Sarker, Aymen Alsaadi, Niranda Perera +8
Managing and preparing complex data for deep learning, a prevalent approach in large-scale data science can be challenging. Data transfer for model training also presents difficult…