Created by W.Langdon from gp-bibliography.bib Revision:1.7970
This paper proposes and evaluates a statistical analysis technique, Starchart, that partitions the GPU hardware/software tuning space by automatically discerning important inflection points in design parameter values. Unlike prior methods, Starchart can identify the best parameter choices within different regions of the space. Our tool is efficient, evaluating at most 0.3 percent of the tuning space, and often much less, and is robust enough to analyze highly variable real-system measurements, not just simulation. In one case study, we use it to automatically find platform-specific parameter settings that are 6.3 fold faster (for AMD) and 1.3 fold faster (for NVIDIA) than a single general setting. We also show how power-optimized parameter settings can save 47 Watts (26 percent of total GPU power) with little performance loss. Overall, Starchart can serve as a foundation for a range of GPU compiler optimisations, auto-tuners, and programmer tools. Furthermore, because Starchart does not rely on specific GPU features, we expect it to be useful for broader CPU/GPU studies as well.",
Genetic Programming entries for Wenhao Jia Kelly A Shaw Margaret Martonosi