Parallelizing code in a shared-memory environment is commonly done utilizing loop scheduling (LS) in a fork-join manner as in OpenMP. This manner of parallelization is popular due to its ease to code, but the choice of the LS method is important when the workload per iteration is highly variable. Currently, the shared-memory environment is evolving in high-performance computing as larger chiplet-based processors with high core counts and segmented L3 cache are introduced. These processors have a stronger non-uniform memory access (NUMA) effect than the previous generation of x86-64 processors. This work attempts to modify the adaptive self-scheduling loop scheduler known as iCh ( i rregular Ch unk) for these NUMA environments while analyzing the impact of these systems on default OpenMP LS methods. In particular, iCh is as a default LS method for irregular applications (i.e., applications where the workload per iteration is highly variable) that guarantees “good” performance without tuning. The modified version, named NiCh , is demonstrated over multiple irregular applications to show the variation in performance. The work demonstrates that NiCh is able to better handle architectures with stronger NUMA effects, and particularly is better than iCh when the number of threads is greater than the number of cores. However, NiCh also comes with being less universally “good” than iCh and a set of parameters that are hardware dependent.

Abstract Many shared‐memory parallel irregular applications, such as sparse linear algebra and graph algorithms, depend on efficient loop scheduling (LS) in a fork‐join manner despite that the work per loop iteration can greatly vary depending on the application and the input. Because of the importance of LS, many different methods (e.g., workload‐aware self‐scheduling) and parameters (e.g., chunk size) have been explored to achieve reasonable performance, and many of these methods require expert prior knowledge about the application and input before runtime. This work proposes a new LS method that requires little to no expert knowledge to achieve speedups close to those of tuned LS methods by self‐managing chunk size based on a heuristic of throughput and using work‐stealing to recover from workload imbalances. This method, named iCh, is implemented into libgomp for testing. It is evaluated against OpenMP's guided, dynamic, and taskloop methods and is evaluated against BinLPT and generic work‐stealing on an array of applications that includes: a synthetic benchmark, breadth‐first search, K‐Means, the molecular dynamics code LavaMD, and sparse matrix‐vector multiplication. On a 28 thread Intel system, iCh is the only method to always be one of the top three LS methods. On average across all applications, iCh is within of the best method and is even able to outperform other LS methods for breadth‐first search and K‐Means.