Umpalumpa: a framework for efficient execution of complex image processing workloads on heterogeneous nodes

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216224%3A14610%2F23%3A00131054" target="_blank" >RIV/00216224:14610/23:00131054 - isvavai.cz</a>

Result on the web

<a href="https://doi.org/10.1007/s00607-023-01190-w" target="_blank" >https://doi.org/10.1007/s00607-023-01190-w</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s00607-023-01190-w" target="_blank" >10.1007/s00607-023-01190-w</a>

Result language

angličtina

Original language name

Umpalumpa: a framework for efficient execution of complex image processing workloads on heterogeneous nodes

Original language description

Modern computers are typically heterogeneous devices—besides the standard central processing unit (CPU), they commonly include an accelerator such as a graphics processing unit (GPU). However, exploiting the full potential of such computers is challenging, especially when complex workloads consisting of multiple computationally demanding tasks are to be processed. This paper proposes a framework called Umpalumpa, which aims to manage complex workloads on heterogeneous computers. Umpalumpa combines three aspects that ease programming and optimize code performance. Firstly, it implements a data-centric design, where data are described by their physical properties (e. g., location in memory, size) and logical properties (e. g., dimensionality, shape, padding). Secondly, Umpalumpa utilizes task-based parallelism to schedule tasks on heterogeneous nodes. Thirdly, tasks can be dynamically autotuned on a source code level according to the hardware where the task is executed and the processed data. Altogether, Umpalumpa allows for implementing a complex workload, which is automatically executed on CPUs and accelerators, and allows autotuning to maximize the performance with the given hardware and data input. Umpalumpa focuses on image processing workloads, but the concept is generic and can be extended to different types of workloads. We demonstrate the usability of the proposed framework on two previously accelerated applications from cryogenic electron microscopy: 3D Fourier reconstruction and Movie alignment. We show that, compared to the original implementations, Umpalumpa reduces the complexity and improves the maintainability of the main applications’ loops while improving performance through automatic memory management and autotuning of the GPU kernels.

Czech name

—

Czech description

—

Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

CEP classification

—

OECD FORD branch

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Project

<a href="/en/project/LM2018140" target="_blank" >LM2018140: e-Infrastructure CZ</a><br>

Continuities

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Publication year

2023

Confidentiality

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Name of the periodical

Computing

ISSN

0010-485X

e-ISSN

1436-5057

Volume of the periodical

105

Issue of the periodical within the volume

11

Country of publishing house

AT - AUSTRIA

Number of pages

29

Pages from-to

2389-2417

UT code for WoS article

001010699200001

EID of the result in the Scopus database

2-s2.0-85161984169