Qilin: Exploiting parallelism on heterogeneous multiprocessors with adaptive mapping

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Edge Computing for Autonomous Driving: Opportunities and Challenges (2019, background): “[61] focus on improving the latency and energy consumption of a single process.”
ad-heap: an Efficient Heap Data Structure for Asymmetric Multicore Processors (2014, background): “StarPU [5], Qilin [24], Glinda [33] and HDSS [7] are representatives that can simultaneously execute suitable compute programs for different data portions on CPUs and GPUs.”
Dandelion: a compiler and runtime for heterogeneous systems (2013, background): “Several systems [69, 46] automatically choose whether to send jobs to the CPU or GPU [11, 12, 10, 30, 87], others focus on support for scheduling in the presence of heterogeneity in a cluster [23].”
A Survey of CPU-GPU Heterogeneous Computing Techniques (2015, unknown): “…et al. 2010; Singh and Aruni 2011; So et al. 2011; Wang et al. 2013b; Xiao et al. 2011; Yao et al. 2010] Smith-Watermanalgorithm [Chen et al. 2010; Luk et al. 2009; Singh and Aruni 2011] N-body simulations [Hu et al. 2011; Jetley et al. 2010; Joselli et al. 2008; Nakasato et al. 2012; Rahimian et…”