A Workload and Programming Ease Driven Perspective of Processing-in-Memory

TL;DR

This paper explores processing-in-memory (PIM) architectures, focusing on workload opportunities, programming challenges, and adoption hurdles to improve data processing efficiency for large-scale applications.

Contribution

It systematically identifies PIM opportunities in real applications, addresses programming challenges, and discusses adoption barriers for practical PIM deployment.

Findings

Quantified PIM gains for machine learning and data analytics.

Identified key programming issues for PIM application development.

Outlined remaining challenges for PIM widespread adoption.

Abstract

Many modern and emerging applications must process increasingly large volumes of data. Unfortunately, prevalent computing paradigms are not designed to efficiently handle such large-scale data: the energy and performance costs to move this data between the memory subsystem and the CPU now dominate the total costs of computation. This forces system architects and designers to fundamentally rethink how to design computers. Processing-in-memory (PIM) is a computing paradigm that avoids most data movement costs by bringing computation to the data. New opportunities in modern memory systems are enabling architectures that can perform varying degrees of processing inside the memory subsystem. However, there are many practical system-level issues that must be tackled to construct PIM architectures, including enabling workloads and programmers to easily take advantage of PIM. This article examines three key domains of work towards the practical construction and widespread adoption of PIM architectures. First, we describe our work on systematically identifying opportunities for PIM in real applications, and quantify potential gains for popular emerging applications (e.g., machine learning, data analytics, genome analysis). Second, we aim to solve several key issues on programming these applications for PIM architectures. Third, we describe challenges that remain for the widespread adoption of PIM.