Accelerating database analytic query workloads using an associative processor

Abstract

Database analytic query workloads are heavy consumers of datacenter cycles, and there is constant demand to improve their performance. Associative processors (AP) have re-emerged as an attractive architecture that offers very large data-level parallelism that can be used to implement a wide range of general-purpose operations. Associative processing is based primarily on efficient search and bulk update operations. Analytic query workloads benefit from data parallel execution and often feature both search and bulk update operations. In this paper, we investigate how amenable APs are to improving the performance of analytic query workloads. For this study, we use the recently proposed Content-Addressable Processing Engine (CAPE) framework. CAPE is an AP core that is highly programmable via the RISC-V ISA with standard vector extensions. By mapping key database operators to CAPE and introducing APaware changes to the query optimizer, we show that CAPE is a good match for database analytic workloads. We also propose a set of database-aware microarchitectural changes to CAPE to further improve performance. Overall, CAPE achieves a 10.8× speedup on average (up to 61.1×) on the SSB benchmark (a suite of 13 queries) compared to an iso-area aggressive out-of-order processor with AVX-512 SIMD support.

AM4: MRAM Crossbar Based CAM/TCAM/ACAM/AP for In-Memory Computing

“…Garzón et al [14] and Yantir et al [70] separately proposed a convolutional neural network design using an in-memory associative processor. Complete system designs of in-memory associative processors have been separately proposed by Zha, et al [71] and Caminal, et al [72]. Yantir [73] studied CMOS and resistive NOR CAM based associative processors and their applications.…”