Exploring high bandwidth memory for PET image reconstruction

Dai Yang, Tilman Küstner, Rami Al-Rihawi, Martin Schulz

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

Abstract

Memory bandwidth plays an essential role in high performance computing. Its impact on system performance is evident when running applications with a low arithmetic intensity. Therefore, high bandwidth memory is on the agenda of many vendors. However, depending on the memory architecture, other optimizations are required to exploit the performance gain from high bandwidth memory technology. In this paper, we present our optimizations for the Maximum Likelihood Expectation-Maximization (MLEM) algorithm, a method for positron emission tomography (PET) image reconstruction, with a sparse matrix-vector (SpMV) kernel. The results show significant improvement in performance when executing the code on an Intel Xeon Phi processor with MCDRAM when compared to multi-channel DRAM. We further identify that the latency of the MCDRAM becomes a new limiting factor, requiring further optimization. Ultimately, after implementing cache-blocking optimization, we achieved a total memory bandwidth of up to 180 GB/s for the SpMV operation.

OriginalspracheEnglisch
TitelParallel Computing
UntertitelTechnology Trends
Redakteure/-innenIan Foster, Gerhard R. Joubert, Ludek Kucera, Wolfgang E. Nagel, Frans Peters
Herausgeber (Verlag)IOS Press BV
Seiten219-228
Seitenumfang10
ISBN (elektronisch)9781643680705
DOIs
PublikationsstatusVeröffentlicht - 2020

Publikationsreihe

NameAdvances in Parallel Computing
Band36
ISSN (Print)0927-5452
ISSN (elektronisch)1879-808X

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