StarPU: A Unified Platform for Task Scheduling on Heterogeneous Multicore Architectures
Euro-Par '09 Proceedings of the 15th International Euro-Par Conference on Parallel Processing
Understanding throughput-oriented architectures
Communications of the ACM
Compact data structure and scalable algorithms for the sparse grid technique
Proceedings of the 16th ACM symposium on Principles and practice of parallel programming
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Multi-core parallelism and accelerators are becoming common features of today's computer systems, as they allow for computational power without sacrificing energy efficiency. Due to heterogeneity, tuning for each type of compute unit and adequate load balancing is essential. This paper proposes static and dynamic solutions for load balancing in the context of an application for visualizing high-dimensional simulation data. The application relies on the sparse grid technique for data compression. Its performance critical part is the interpolation routine used for decompression. Results show that our load balancing scheme allows for an efficient acceleration of interpolation on heterogeneous systems containing multi-core CPUs and GPUs.