Improving the accessibility of NUMA-aware C plus plus application development based on the PGASUS framework

Certain workloads such as in-memory databases are inherently hard to scale-out and rely on cache-coherent scale-up non-uniform memory access (NUMA) systems to keep up with the ever-increasing demand for compute resources. However, many parallel programming frameworks such as OpenMP do not make efficient use of large scale-up NUMA systems as they do not consider data locality sufficiently. In this work, we present PGASUS, a C++ framework for NUMA-aware application development that provides integrated facilities for NUMA-aware task parallelism and data placement. The framework is based on an extensive review of parallel programming languages and frameworks to incorporate the best practices of the field. In a comprehensive evaluation, we demonstrate that PGASUS provides average performance improvements of 1.56x and peak performance improvements of up to 4.67xacross a wide range of workloads.