Glinda: a framework for accelerating imbalanced applications on heterogeneous platforms

Abstract

Heterogeneous platforms integrating different processors like GPUs and multi-core CPUs become popular in high performance computing. While most applications are currently using the homogeneous parts of these platforms, we argue that there is a large class of applications that can benefit from their heterogeneity: massively parallel imbalanced applications. Such applications emerge, for example, from variable time step based numerical methods and simulations. In this paper, we present Glinda, a framework for accelerating imbalanced applications on heterogeneous computing platforms. Our framework is able to correctly detect the application workload characteristics, make choices based on the available parallel solutions and hardware configuration, and automatically obtain the optimal workload decomposition and distribution. Our experiments on parallelizing a heavily imbalanced acoustic ray tracing application show that Glinda improves application performance in multiple scenarios, achieving up to 12x speedup against manually configured parallel solutions.