Radio-Astronomical Imaging: FPGAs vs GPUs

Veenboer, Bram; Romein, John W.

doi:10.1007/978-3-030-29400-7_36

Cited by 15 publications

(16 citation statements)

References 12 publications

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“…Then the subgrids are processed by the adder to obtain a grid. The gridder and FFT, red boxes, are the focus of this and the related work [11].…”

Section: A Image-domain Griddingmentioning

confidence: 99%

“…This makes FPGAs an attractive accelerator platform [10]. However, the prior art shows that FPGAs are less energy-efficient than GPUs for the radio astronomy application domain [11]. Reduced precision computing is a technique where smaller data types are used to reduce area usage, execution time, and power consumption within noise-tolerant applications without losing information [12].…”

Section: Introductionmentioning

confidence: 99%

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Reduced-Precision Acceleration of Radio-Astronomical Imaging on Reconfigurable Hardware

et al. 2022

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Radio telescopes produce large volumes of data that need to be processed to obtain high-resolution sky images. This is a complex task that requires computing systems that provide both high performance and high energy efficiency. Hardware accelerators such as GPUs (Graphics Processing Units) and FPGAs (Field Programmable Gate Arrays) can provide these two features and are thus an appealing option for this application. Most HPC (High-Performance Computing) systems operate in double precision (64-bit) or in single precision (32-bit), and radio-astronomical imaging is no exception. With reduced precision computing, smaller data types (e.g., 16-bit) are used to improve energy efficiency and throughput performance in noise-tolerant applications. We demonstrate that reduced precision can also be used to produce high-quality sky images. To this end, we analyze the gridding component (Image-Domain Gridding) of the widely-used WSClean imaging application. Gridding is typically one of the most time-consuming steps in the imaging process and, therefore, an excellent candidate for acceleration. We identify the minimum required exponent and mantissa bits for a custom floating-point data type. Then, we propose the first custom floating-point accelerator on a Xilinx Alveo U50 FPGA using High-Level Synthesis. Our reduced-precision implementation improves the throughput and energy efficiency of respectively 1.84x and 2.03x compared to the single-precision floating-point baseline on the same FPGA. Our solution is also 2.12x faster and 3.46x more energy-efficient than an Intel i9 9900k CPU (Central Processing Unit) and manages to keep up in throughput with an AMD RX 550 GPU.

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“…Then the subgrids are processed by the adder to obtain a grid. The gridder and FFT, red boxes, are the focus of this and the related work [11].…”

Section: A Image-domain Griddingmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Reduced-Precision Acceleration of Radio-Astronomical Imaging on Reconfigurable Hardware

et al. 2022

Self Cite

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“…These works all concentrate on CPU/GPU based accelerators of gridding/degridding approaches. Veenboer and Romein [26] researched on FPGA-based implementation of IDG (Image-Domain Gridding) gridding/degridding, and this work compares the performance and energy efficiency of FPGA-, GPU-and CPU-based implementations of IDG. The results show that with the identical theoretical peakperformance, the FPGA and GPU perform much better than the CPU and consume significantly less power.…”

Section: B Related Workmentioning

confidence: 99%

FPGA-Based Scale-Out Prototyping of Degridding Algorithm for Accelerating Square Kilometre Array Telescope Data Processing

Hou

Zhu

et al. 2020

IEEE Access

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The SKA (Square Kilometre Array) radio telescope will become the most sensitive telescope by correlating a large number of antenna nodes to form a giant antenna array. The data generated from such a large number of antenna nodes will pose a huge storage problem and require real-time data processing to make the best use of data, and the SKA Scientific Data Processing becomes the bottleneck of the whole processing flow. However, the existing high-performance CPU-and GPU (Graphics Processing Unit)-based solutions cannot satisfy the performance requirements and power budget requirements well [1]. Due to the consideration of the high energy efficiency of hardware accelerators and the flexibility and cost of prototype design, in this paper, we explore the FPGA(Field Programmable Gate Array)-based prototype of one of the most computationally demanding procedures in SKA scientific data processing: degridding. Through the analysis of algorithm behavior and bottlenecks, we design and optimize the memory architecture and computing logic of an FPGA-based prototype. Besides, with the consideration of the relations between the required data of processing multiple spectral channels, we reuse the shared data in processing neighboring spectral channels, and the performance further improves. The functionality and performance of our design have been verified on the target FPGA board, and the software-based benchmarks were also measured on comparable CPU and GPU platforms, indicating that the FPGA-based prototype achieves 2.74 times and 2.03 times speedup, 7.64 times and 7.42 times energy efficiency than the MPI(Message Passing Interface)-based CPU benchmark and the CUDA (Compute Unified Device Architecture)-based GPU benchmark, respectively. INDEX TERMS FPGA, gridding/degridding, scientific data processing, square kilometre array.

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“…These works all concentrate on hardware accelerators of imaging approaches based on CPU or GPU. Veenboer and Romein [28] implemented and optimized a radio-astronomical imaging application on a target FPGA. They compare architectures, programming models, optimizations, performance, energy efficiency, and programming effort to highly optimized GPU and CPU implementations.…”

Section: Related Workmentioning

confidence: 99%

Accelerating Faceting Wide-Field Imaging Algorithm with FPGA for SKA Radio Telescope as a Vast Sensor Array

Song

Zhu

Nan

et al. 2020

Sensors

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The SKA (Square Kilometer Array) radio telescope will become the most sensitive telescope by correlating a huge number of antenna nodes to form a vast array of sensors in a region over one hundred kilometers. Faceting, the wide-field imaging algorithm, is a novel approach towards solving image construction from sensing data where earth surface curves cannot be ignored. However, the traditional processor of cloud computing, even if the most sophisticated supercomputer is used, cannot meet the extremely high computation performance requirement. In this paper, we propose the design and implementation of high-efficiency FPGA (Field Programmable Gate Array) -based hardware acceleration of the key algorithm, faceting in SKA by focusing on phase rotation and gridding, which are the most time-consuming phases in the faceting algorithm. Through the analysis of algorithm behavior and bottleneck, we design and optimize the memory architecture and computing logic of the FPGA-based accelerator. The simulation and tests on FPGA are done to confirm the acceleration result of our design and it is shown that the acceleration performance we achieved on phase rotation is 20× the result of the previous work. We then further designed and optimized an efficient microstructure of loop unrolling and pipeline for the gridding accelerator, and the designed system simulation was done to confirm the performance of our structure. The result shows that the acceleration ratio is 5.48 compared to the result tested on software in gridding parts. Hence, our approach enables efficient acceleration of the faceting algorithm on FPGAs with high performance to meet the computational constraints of SKA as a representative vast sensor array.

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Radio-Astronomical Imaging: FPGAs vs GPUs

Cited by 15 publications

References 12 publications

Reduced-Precision Acceleration of Radio-Astronomical Imaging on Reconfigurable Hardware

Reduced-Precision Acceleration of Radio-Astronomical Imaging on Reconfigurable Hardware

FPGA-Based Scale-Out Prototyping of Degridding Algorithm for Accelerating Square Kilometre Array Telescope Data Processing

Accelerating Faceting Wide-Field Imaging Algorithm with FPGA for SKA Radio Telescope as a Vast Sensor Array

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