Many applications in high performance computing are designed based on underlying
performance and execution models. While these models could successfully be employed in …

Load balancing is often a challenge in task‐parallel applications. The balancing problems
are divided into static and dynamic.“Static” means that we have some prior knowledge about …

Many applications in high performance computing are designed based on underlying
performance and execution models. While these models could successfully be employed in …

Models of parallel processing systems typically assume that one has workers and jobs are
split into an equal number of tasks. Splitting jobs into smaller tasks, ie using “tiny tasks”, can …

Increasingly complex hardware architectures as well as numerical algorithms make
balancing load in parallel numerical software for adaptive mesh refinement an inherently …

In an era where we can not afford to checkpoint frequently, replication is a generic way
forward to construct numerical simulations that can continue to run even if hardware parts …

Scientific parallel applications often use MPI for inter-node communications and OpenMP for
intra-node orches-tration. Parallel applications such as particle transport, seismic wave …

Balancing the workload of sophisticated simulations is inherently difficult, since we have to
balance both computational workload and memory footprint over meshes that can change …

Adaptive Mesh Refinement (AMR) is a prevalent method used by distributed-memory
simulation applications to adapt the accuracy of their solutions depending on the turbulent …

The task-based dataflow programming model has emerged as an alternative to the process-
centric programming model for extreme-scale applications. However, load balancing is still a …