Qurator jointly optimizes queue time and fidelity for hybrid quantum-classical workflows across providers using quantum-aware DAG scheduling and a unified logarithmic fidelity score, achieving 30-75% wait reduction at high load with bounded accuracy cost.
IEEE Transactions on Parallel and Distributed Systems 25(6), 1522–1532 (Jun 2014)
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
years
2026 3verdicts
UNVERDICTED 3representative citing papers
CoGPU resolves the tradeoff in GPU sharing by introducing GPU coroutines for semantic-preserving resource migration, delivering up to 79.2% higher training throughput and zero token mismatch in inference.
SET is a new CUDA runtime framework that combines event-chaining, work-stealing, and per-stream buffers in graph-based pipelines to deliver 1.15-1.44X speedups and 18-54% lower scheduling overhead versus prior CUDA graph methods.
citing papers explorer
-
Qurator: Scheduling Hybrid Quantum-Classical Workflows Across Heterogeneous Cloud Providers
Qurator jointly optimizes queue time and fidelity for hybrid quantum-classical workflows across providers using quantum-aware DAG scheduling and a unified logarithmic fidelity score, achieving 30-75% wait reduction at high load with bounded accuracy cost.
-
Performance Isolation and Semantic Determinism in Efficient GPU Spatial Sharing
CoGPU resolves the tradeoff in GPU sharing by introducing GPU coroutines for semantic-preserving resource migration, delivering up to 79.2% higher training throughput and zero token mismatch in inference.
-
SET: Stream-Event-Triggered Scheduling for Efficient CUDA Graph Pipelines
SET is a new CUDA runtime framework that combines event-chaining, work-stealing, and per-stream buffers in graph-based pipelines to deliver 1.15-1.44X speedups and 18-54% lower scheduling overhead versus prior CUDA graph methods.