Auto-DOK: Compiler-Assisted Automatic Detection of Offload Kernels for FPGA-HBM Architectures

Publikation: Beitrag in Buch/Konferenzbericht/Sammelband/GutachtenBeitrag in KonferenzbandBeigetragenBegutachtung

Abstract

The bandwidth improvement provided by high-bandwidth memory (HBM), and the capability of FPGAs to customize the processing and memory hierarchy, results in a considerable performance increase for memory-intensive work-loads such as graph processing, sorting, machine learning, and database analytics. Modern systems integrating 3D-stacked DRAM memory can be leveraged to realize the Near-Memory Computing (NMC) paradigm by offloading some computations to accelerators placed near the HBM. Although numerous studies have investigated efficient accelerators for FPGA-HBM platforms, researchers have not proposed a systematic way for identifying which application kernels are suitable for execution near the HBM. In this article, we propose compiler support for recognizing offloading candidates without any burden on programmers. Auto-DOK analyzes an application code based on criteria derived from the hardware design goals of FPGA-HBM platforms, and automatically identifies kernels suitable for offloading. We evaluate Auto-DOK on benchmarks ranging from microbenchmarks to real-world kernels. Our results show that Auto-DOK can correctly identify kernels and input sizes suitable for execution near the HBM, and prevents slowdown caused by incorrect offloading decisions for other workloads. Moreover, Auto-DOK operates at compile time with negligible overhead and without the need for expensive profiling.

Details

OriginalspracheEnglisch
TitelProceedings - 2023 26th Euromicro Conference on Digital System Design, DSD 2023
Redakteure/-innenSmail Niar, Hamza Ouarnoughi, Amund Skavhaug
Herausgeber (Verlag)Institute of Electrical and Electronics Engineers Inc.
Seiten577-584
Seitenumfang8
ISBN (elektronisch)9798350344196
PublikationsstatusVeröffentlicht - 2023
Peer-Review-StatusJa

Konferenz

Titel26th Euromicro Conference on Digital System Design
KurztitelDSD 2023
Veranstaltungsnummer26
Dauer6 - 8 September 2023
Webseite
OrtGrand Blue Fafa Resort
StadtDurres
LandAlbanien

Externe IDs

ORCID /0000-0003-2571-8441/work/159607522

Schlagworte

Schlagwörter

  • Code Characterization, High-bandwidth Memory (HBM), Parallel Architectures