A hybrid task graph scheduler for high performance image processing workflows

Designing applications for scalability is key to improving their performance in hybrid and cluster computing. Scheduling code to utilize parallelism is difficult, particularly when dealing with data dependencies, memory management, data motion, and processor occupancy. The Hybrid Task Graph Scheduler (HTGS) increases programmer productivity when implementing hybrid workflows that scale to multi-core and multi-GPU systems. HTGS manages dependencies between tasks, represents CPU and GPU memories independently, overlaps computations with disk I/O and memory transfers, keeps multiple GPUs occupied, and uses all available compute resources. We present an implementation of hybrid microscopy image stitching using HTGS that reduces code size by ≈ 25% and shows favorable performance compared to a similar hybrid workflow implementation without HTGS. The HTGS-based implementation reuses the computational functions of the hybrid workflow implementation.