Retrospective: A Scalable Processing-in-Memory Accelerator for Parallel Graph Processing

TL;DR

This paper reviews the design and impact of Tesseract, a programmable near-memory processing system for graph workloads, highlighting its innovative architecture and influence on subsequent research.

Contribution

It introduced a fully programmable, customizable near-memory accelerator system, Tesseract, for graph processing, combining 3D-stacked memory with general-purpose cores and a message-passing model.

Findings

First to design a general-purpose near-memory accelerator system

Influenced subsequent academic and industry work on PIM architectures

Demonstrated significant acceleration for graph workloads

Abstract

Our ISCA 2015 paper provides a new programmable processing-in-memory (PIM) architecture and system design that can accelerate key data-intensive applications, with a focus on graph processing workloads. Our major idea was to completely rethink the system, including the programming model, data partitioning mechanisms, system support, instruction set architecture, along with near-memory execution units and their communication architecture, such that an important workload can be accelerated at a maximum level using a distributed system of well-connected near-memory accelerators. We built our accelerator system, Tesseract, using 3D-stacked memories with logic layers, where each logic layer contains general-purpose processing cores and cores communicate with each other using a message-passing programming model. Cores could be specialized for graph processing (or any other application to be accelerated). To our knowledge, our paper was the first to completely design a near-memory accelerator system from scratch such that it is both generally programmable and specifically customizable to accelerate important applications, with a case study on major graph processing workloads. Ensuing work in academia and industry showed that similar approaches to system design can greatly benefit both graph processing workloads and other applications, such as machine learning, for which ideas from Tesseract seem to have been influential. This short retrospective provides a brief analysis of our ISCA 2015 paper and its impact. We briefly describe the major ideas and contributions of the work, discuss later works that built on it or were influenced by it, and make some educated guesses on what the future may bring on PIM and accelerator systems.