Accelerating Relational Database Analytical Processing with Bulk-Bitwise Processing-in-Memory

TL;DR

This paper demonstrates how bulk-bitwise processing-in-memory can significantly accelerate OLAP queries in relational databases by reducing data movement and leveraging parallel in-memory computation, achieving a 4.65x speedup over traditional systems.

Contribution

It presents a full stack adaptation of bulk-bitwise PIM for OLAP, from SQL compilation to hardware implementation, optimizing database query processing.

Findings

Achieves 4.65x speedup on SSB benchmark

Reduces data transfer between memory and CPU

Leverages inherent parallelism of bulk-bitwise PIM

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 bit-vector 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 4.65X speedup over Monet-DB, a standard database system.