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- W3175136721 abstract "Data-level parallelism (DLP) is a heavily used hardware-driven parallelization technique to optimize the analytical query processing, especially in in-memory column stores. This kind of parallelism is characterized by executing essentially the same operation on different data elements simultaneously. Besides Single Instruction Multiple Data (SIMD) extensions on common x86-processors, GPUs also provide DLP but with a different execution model called Single Instruction Multiple Threads (SIMT), where multiple scalar threads are executed in a SIMD manner. Unfortunately, a complete GPU-specific implementation of all query operators has to be set up, since the state of the vectorized implementations cannot be ported from x86-processors to GPUs right now. To avoid this implementation effort, we present our vision to virtualize GPUs as virtual vector engines with software-defined SIMD instructions and to specialize hardware-oblivious vectorized operators to GPUs using our Template Vector Library (TVL) in this paper." @default.
- W3175136721 created "2021-07-05" @default.
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- W3175136721 date "2021-06-20" @default.
- W3175136721 modified "2023-09-23" @default.
- W3175136721 title "The Case for SIMDified Analytical Query Processing on GPUs" @default.
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- W3175136721 doi "https://doi.org/10.1145/3465998.3466015" @default.
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