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- W4385624988 abstract "Online Analytical Processing (OLAP) for relational databases is a business decision support application. The application receives queries about the business database, usually requesting to summarize many database records, and produces few results. Existing OLAP requires transferring a large amount of data between the memory and the CPU, having a few operations per datum, and producing a small output. Hence, OLAP is a good candidate for processing-in-memory (PIM), where computation is performed where the data is stored, thus accelerating applications by reducing data movement between the memory and CPU. In particular, bulk-bitwise PIM, where the memory array is a bitvector processing unit, seems a good match for OLAP. With the extensive inherent parallelism and minimal data movement of bulk-bitwise PIM, OLAP applications can process the entire database in parallel in memory, transferring only the results to the CPU. This paper shows a full stack adaptation of a bulk-bitwise PIM, from compiling SQL to hardware implementation, for supporting OLAP applications. Evaluating the Star Schema Benchmark (SSB), bulk-bitwise PIM achieves a 4.65× speedup over Monet-DB, a standard database system." @default.
- W4385624988 created "2023-08-08" @default.
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- W4385624988 date "2023-06-26" @default.
- W4385624988 modified "2023-09-23" @default.
- W4385624988 title "Accelerating Relational Database Analytical Processing with Bulk-Bitwise Processing-in-Memory" @default.
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- W4385624988 doi "https://doi.org/10.1109/newcas57931.2023.10198122" @default.
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