Buddy-RAM: Improving the Performance and Efficiency of Bulk Bitwise Operations Using DRAM

TL;DR

Buddy leverages the analog capabilities of DRAM to perform bulk bitwise operations inside the memory chip, significantly improving throughput and energy efficiency for data-intensive tasks.

Contribution

This paper introduces Buddy, a novel DRAM-based mechanism that performs fast, energy-efficient bulk bitwise operations directly within DRAM, requiring minimal modifications.

Findings

Achieves 10.9X to 25.6X throughput improvement

Reduces energy consumption by 25.1X to 59.5X

Outperforms existing methods in real-world applications

Abstract

Bitwise operations are an important component of modern day programming. Many widely-used data structures (e.g., bitmap indices in databases) rely on fast bitwise operations on large bit vectors to achieve high performance. Unfortunately, in existing systems, regardless of the underlying architecture (e.g., CPU, GPU, FPGA), the throughput of such bulk bitwise operations is limited by the available memory bandwidth. We propose Buddy, a new mechanism that exploits the analog operation of DRAM to perform bulk bitwise operations completely inside the DRAM chip. Buddy consists of two components. First, simultaneous activation of three DRAM rows that are connected to the same set of sense amplifiers enables us to perform bitwise AND and OR operations. Second, the inverters present in each sense amplifier enables us to perform bitwise NOT operations, with modest changes to the DRAM array. These two components make Buddy functionally complete. Our implementation of Buddy largely exploits the existing DRAM structure and interface, and incurs low overhead (1% of DRAM chip area). Our evaluations based on SPICE simulations show that, across seven commonly-used bitwise operations, Buddy provides between 10.9X---25.6X improvement in raw throughput and 25.1X---59.5X reduction in energy consumption. We evaluate three real-world data-intensive applications that exploit bitwise operations: 1) bitmap indices, 2) BitWeaving, and 3) bitvector-based implementation of sets. Our evaluations show that Buddy significantly outperforms the state-of-the-art.