Going green: optimizing GPUs for energy efficiency through model-steered auto-tuning

Schoonhoven, Richard; Veenboer, Bram; Werkhoven, Ben van; Batenburg, Kees Joost

doi:10.1109/pmbs56514.2022.00010

Cited by 4 publications

(1 citation statement)

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“…This is termed kernel tuning, and entails maximizing the performance of GPU computing by optimizing free parameters of kernels, such as block sizes and algorithmic constants [24]. Tuned algorithms achieve better run-times, reduced energy consumption [10,46], or utilize less resources, in particular, GPU memory. In high-throughput applications, such as in-line CT scanning, a kernel can be tuned toward a fixed measurement protocol and dedicated GPU architecture.…”

Section: 2mentioning

confidence: 99%

ASTRA kernelkit: GPU-accelerated projectors for computed tomography using cupy

Graas,

Jan Palenstijn,

van Werkhoven

et al. 2024

AMMC

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New computed tomography (CT) algorithms are commonly developed in high-level programming languages, such as Python or MATLAB, while low-level languages are used to support their computation-intensive operations. In the past decade, graphics processing units (GPUs) have become the de-facto standard for large parallel computations in areas such as computational imaging, image processing, and machine learning. Our fast-and-flexible CT reconstruction software, ASTRA Toolbox, therefore already implemented tomographic projectors, i.e., the core computational operations modeling the X-ray physics, using NVIDIA CUDA (Compute Unified Device Architecture), a low-level platform for computation on GPUs. However, the Python-C++ language barrier prevents high-level Python users from modifying these lowlevel projectors, and, as a consequence, research into new tomographic algorithms is more complex and time-consuming than necessary. With the ASTRA KernelKit, we lifted tomographic projectors to Python and leveraged CuPy, a numerical software like NumPy and SciPy that exposes CUDA to Python, to obtain a fine-grained control over their efficiency and implementation. In this article, we introduced our software and illustrated its importance for highperformance and data-driven applications using examples from deep learning, real-time X-ray CT, and kernel tuning.

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Section: 2mentioning

confidence: 99%