Automated Scheduling Algorithm Selection and Chunk Parameter Calculation in OpenMP

Increasing node and cores-per-node counts in supercomputers render scheduling and load balancing critical for exploiting parallelism. OpenMP applications can achieve high performance via careful selection of scheduling
kind
and
chunk
parameters on a per-loop, per-application, and per-system basis from a portfolio of advanced scheduling algorithms (Korndörfer
et al.
, 2022). This selection approach is time-consuming, challenging, and may need to change during execution. We propose
Auto4OMP
, a novel approach for automated load balancing of OpenMP applications. With Auto4OMP, we introduce three scheduling
algorithm selection methods
and an
expert-defined chunk parameter
for OpenMP's
schedule
clause's
kind
and
chunk
, respectively. Auto4OMP extends the OpenMP
schedule(auto)
and
chunk
parameter implementation in LLVM's OpenMP runtime library to automatically select a scheduling algorithm and calculate a chunk parameter during execution. Loop characteristics are inferred in Auto4OMP from the loop execution over the application's time-steps. The experiments performed in this work show that Auto4OMP improves applications performance by up to
$11\%$
compared to LLVM's
schedule(auto)
implementation and outperforms manual selection. Auto4OMP improves MPI+OpenMP applications performance by
explicitly
minimizing thread- and
implicitly
reducing process-load imbalance.