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This content will become publicly available on November 2, 2025

Title: Leviathan: A Unified System for General-Purpose Near-Data Computing
The rising cost of data movement poses a significant challenge to future computing systems. The call to arms for novel data-centric systems has spawned a wave of near-data computing (NDC) architectures that move compute closer to data. Despite large benefits promised by NDC, prior designs suffer from limited applicability and difficult programming. This paper identifies the commonalities and differences across NDC designs to develop Leviathan, a unified architecture and programming interface for near-cache NDC. We build a taxonomy of NDC and identify the key dimensions as what, where, and when to compute. Leviathan provides a simple reactive-programming interface and automatically executes actions near data at the right time and place. The ability to integrate multiple NDC paradigms makes Leviathan the only general-purpose system to support a variety of specialized NDC designs. Across a range of NDC-specialized applications, Leviathan improves performance by 1.5×±3.7× and reduces energy by 22%±77% vs. a baseline multicore, while adding only ≈6% area compared to the last-level cache.  more » « less
Award ID(s):
1845986
PAR ID:
10568669
Author(s) / Creator(s):
;
Publisher / Repository:
IEEE
Date Published:
ISBN:
979-8-3503-5057-9
Page Range / eLocation ID:
1278 to 1294
Subject(s) / Keyword(s):
near-data computing data-centric computing data locality cache hierarchy
Format(s):
Medium: X
Location:
Austin, TX, USA
Sponsoring Org:
National Science Foundation
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