Taking Cryptography Out of the Data Path via Near-Memory Processing in DRAM

TL;DR

This paper investigates the potential of real-world Processing-in-Memory (PIM) architectures, specifically UPMEM, to accelerate cryptographic algorithms like AES-128 and SHA-256 by reducing data movement and leveraging multiple memory ranks.

Contribution

It provides an empirical assessment of PIM's scalability and effectiveness in accelerating cryptographic algorithms using the UPMEM architecture.

Findings

Performance improves significantly when distributing computation across multiple ranks.

Real-world PIM can outperform modern CPUs when fully utilizing available memory ranks.

PIM reduces data movement, leading to better energy efficiency.

Abstract

Cryptographic algorithms such as AES-128 and SHA-256 are fundamental to ensuring data security and integrity. Although these algorithms are computationally efficient, their performance is often constrained by the processor-centric architectures (e.g., CPUs, GPUs), primarily due to the memory bottleneck. This constraint leads to increased latency and higher energy consumption, particularly when handling large volumes of data. To overcome these challenges, Processing-in-Memory (PIM) has emerged as a promising architectural paradigm, allowing computation to occur directly within or near memory units. By minimizing data movement between the processor and memory units, PIM can significantly accelerate cryptographic algorithms while improving energy efficiency. Several pieces of prior work have demonstrated the effectiveness of PIM at fundamentally accelerating cryptographic algorithms. However, none of the prior works have extensively demonstrated the potential of a real-world PIM system. In this paper, we want to investigate the potential and limitations of real-world PIM in accelerating cryptographic algorithms. As part of our methodology, the UPMEM PIM architecture is used to assess the scalability of cryptographic algorithms. When these algorithms operate on a single rank, their performance remains below that of modern CPUs. However, distributing the computation across multiple ranks significantly enhances performance. When all available ranks are utilized, real-world PIM can accelerate cryptographic algorithms more effectively.