Profiler-Guided Execution of Recurrent OpenMP Task Graphs on Heterogeneous Clusters

• Rémy Neveu UNICAMP
• Rodrigo Ceccato UNICAMP
• Adrian Munera Barcelona Supercomputing Center
• Sara Royuela Barcelona Supercomputing Center
• Jose M. Monsalve Diaz AMD
• Hervé Yviquel UNICAMP

Resumo

Distributed task-based execution models are well-suited for parallelizing irregular applications across clusters. OpenMP Cluster (OMPC) extends the traditional OpenMP tasking model to support distributed memory systems, leveraging a HEFT-based scheduler to improve resource utilization. However, the efficiency of such a scheduler depends heavily on accurate estimates of task execution and communication costs – information that is often difficult to obtain reliably and efficiently. To address this limitation, we propose a novel scheduling framework that combines the recent taskgraph directive introduced in OpenMP 6.0 with partial online profiling of iterative applications. Our approach performs quasi-static scheduling by recording task graphs at runtime and selectively profiling representative iterations to estimate performance. This information is interpolated and fed back into the scheduler to enhance decision-making. We demonstrate that our framework can improve scheduling quality with minimal overhead, making it suitable for long-running or repetitive workloads commonly found in High-Performance Computing (HPC) applications. We achieve up to 20% speedup for the total application and 4× speedup for scheduling.

Palavras-chave: Runtime, Processor scheduling, High performance computing, Decision making, Parallel processing, Heterogeneous networks, Recording, Resource management, Reliability, Iterative methods, heterogeneous computing, high-performance computing, OpenMP, performance profiling, scheduling, task-based parallelism