Detailed Information on Publication Record
2023
Umpalumpa: a framework for efficient execution of complex image processing workloads on heterogeneous nodes
STŘELÁK, David, David MYŠKA, Filip PETROVIČ, Jan POLÁK, Jaroslav OĽHA et. al.Basic information
Original name
Umpalumpa: a framework for efficient execution of complex image processing workloads on heterogeneous nodes
Authors
STŘELÁK, David (203 Czech Republic, belonging to the institution), David MYŠKA (203 Czech Republic, belonging to the institution), Filip PETROVIČ (703 Slovakia, belonging to the institution), Jan POLÁK (203 Czech Republic, belonging to the institution), Jaroslav OĽHA (703 Slovakia, belonging to the institution) and Jiří FILIPOVIČ (203 Czech Republic, belonging to the institution)
Edition
Computing, Springer, 2023, 0010-485X
Other information
Language
English
Type of outcome
Článek v odborném periodiku
Field of Study
10201 Computer sciences, information science, bioinformatics
Country of publisher
Austria
Confidentiality degree
není předmětem státního či obchodního tajemství
References:
Impact factor
Impact factor: 3.700 in 2022
RIV identification code
RIV/00216224:14610/23:00131054
Organization unit
Institute of Computer Science
UT WoS
001010699200001
Keywords in English
Image processing; task-based systems; auto-tuning; data-aware architecture; CUDA
Tags
International impact, Reviewed
Změněno: 21/3/2024 08:53, doc. RNDr. Jiří Filipovič, Ph.D.
Abstract
V originále
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.
Links
| LM2018140, research and development project |
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