OWL: cooperative thread array aware scheduling techniques for improving GPGPU performance

By Adwait Jog, Onur Kayiran, Nachiappan Chidambaram Nachiappan, Asit K. Mishra, Mahmut Taylan Kandemir, Onur Mutlu, R. Iyer, Chita R. Das. International Conference on Architectural Support for Programming Languages and Operating Systems, 2013

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MASK: Redesigning the GPU Memory Hierarchy to Support Multi-Application Concurrency (2018, background): “4More detailed information about the GPU execution model and its memory hierarchy can be found in [14, 16, 17, 59, 61, 62, 63, 68, 90, 104, 111, 136, 137].”
Processing Data Where It Makes Sense: Enabling In-Memory Computation (2019, background): “Despite that, many GPU applications are still very memory-bound [198, 199, 200, 201, 202, 203, 204, 205, 206, 207], because the limited off-chip pin bandwidth cannot supply enough data to the running threads.”
Zorua: A holistic approach to resource virtualization in GPUs (2016, unknown): “Prior works aim to improve parallelism by increasing resource utilization using hardware-based [3, 4, 22, 30, 31, 32, 41, 50, 68, 77, 84] and software-based [24, 27, 39, 43, 55, 79, 84] approaches.”
Accelerating Mobile Audio Sensing Algorithms through On-Chip GPU Offloading (2017, unknown): “[38] attempt to streamline the optimization process.”