The Bitlet Model: A Parameterized Analytical Model to Compare PIM and CPU Systems

TL;DR

The paper introduces Bitlet, an analytical model that estimates performance and energy tradeoffs in PIM versus CPU systems, providing insights into workload affinity and system design.

Contribution

It presents a novel parameterized analytical tool, Bitlet, for evaluating performance and power in PIM systems, aiding in workload and system design decisions.

Findings

Bitlet reveals tradeoffs between PIM complexity, memory, and bandwidth.

Application to real systems demonstrates its effectiveness.

Insights into PIM-CPU integration are provided.

Abstract

Nowadays, data-intensive applications are gaining popularity and, together with this trend, processing-in-memory (PIM)-based systems are being given more attention and have become more relevant. This paper describes an analytical modeling tool called Bitlet that can be used, in a parameterized fashion, to estimate the performance and the power/energy of a PIM-based system and thereby assess the affinity of workloads for PIM as opposed to traditional computing. The tool uncovers interesting tradeoffs between, mainly, the PIM computation complexity (cycles required to perform a computation through PIM), the amount of memory used for PIM, the system memory bandwidth, and the data transfer size. Despite its simplicity, the model reveals new insights when applied to real-life examples. The model is demonstrated for several synthetic examples and then applied to explore the influence of different parameters on two systems - IMAGING and FloatPIM. Based on the demonstrations, insights about PIM and its combination with CPU are concluded.