Relational query coprocessing on graphics processors

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Efficient Memory Management for GPU-based Deep Learning Systems (2019, methodology): “In data processing, many existing studies (e.g., [5], [7]) partition the data into chunks and ensure that the processing of each data chunk can ﬁt into the GPU memory.”
Optimizing Data Warehousing Applications for GPUs Using Kernel Fusion/Fission (2012, methodology): “INTRODUCTION The use of programmable GPUs has appeared as a potential vehicle for high throughput implementations of data warehousing applications with an order of magnitude or more performance improvement over traditional CPU-based implementations [1], [2].”
Dandelion: a compiler and runtime for heterogeneous systems (2013, background): “Others have explored primitives for supporting GPU-based relational algebra [51, 50] and database operations [43], with emphasis on optimization and performance for operations over a very limited set of data types (primitive types).”
LINQits: big data on little clients (2013, background): “A growing body of work examines the use of GPGPUs to accelerate in-memory databases and query languages (e.g., [17, 19, 20, 25, 3, 46, 38]).”