Evaluating Latency and Throughput Bound Acceleration of FPGAs and GPUs for Adaptive Optics Algorithms. Venugopalan, V. In IEEE High Performance Extreme Computing Conference (HPEC), pages 1-6, September, 2014. abstract bibtex General purpose extremely large aperture optical/infrared telescopes are instrumental in vastly advancing the astrophysical knowledge on a variety of subjects such as star formation, super-massive black holes, solar magnetic atmosphere, exoplanets and protoplanetary systems. The computational requirements for data processing and real-time control are not feasible using conventional computer and cluster architectures. This paper investigates the latency and throughput bound hardware acceleration of wavefront reconstruction and real-time control algorithms using GPUs and FPGAs. Two different correlation methods are studied and targeted on the hardware accelerators for optimal performance. The GPU-based implementations exhibit lower latency due to its superior floating point capability and supporting libraries. The FPGA-based implementation is slower and requires more fine-tuning to yield more throughput than the GPU implementation.
@inproceedings{Venugopalan2014Evaluating,
abstract = {General purpose extremely large aperture optical/infrared telescopes are instrumental in vastly advancing the astrophysical knowledge on a variety of subjects such as star formation, super-massive black holes, solar magnetic atmosphere, exoplanets and protoplanetary systems. The computational requirements for data processing and real-time control are not feasible using conventional computer and cluster architectures. This paper investigates the latency and throughput bound hardware acceleration of wavefront reconstruction and real-time control algorithms using GPUs and FPGAs. Two different correlation methods are studied and targeted on the hardware accelerators for optimal performance. The GPU-based implementations exhibit lower latency due to its superior floating point capability and supporting libraries. The FPGA-based implementation is slower and requires more fine-tuning to yield more throughput than the GPU implementation.},
author = {Venugopalan, Vivek},
booktitle = {IEEE High Performance Extreme Computing Conference (HPEC)},
date-added = {2020-01-15 12:02:05 -0500},
date-modified = {2020-01-15 12:02:05 -0500},
keywords = {adaptive optics;field programmable gate arrays;graphics processing units;optical computing;FPGA;GPU;adaptive optics algorithm;astrophysical knowledge;data processing;extremely large aperture optical telescopes;field programmable gate array;floating point capability;graphics processing unit;infrared telescopes;latency bound hardware acceleration;realtime control algorithm;throughput bound hardware acceleration;wavefront reconstruction;Actuators;Correlation;Field programmable gate arrays;Graphics processing units;Image reconstruction;Mathematical model;Real-time systems;Adaptive optics systems;field programmable gate arrays;graphics processing units;parallel processing;real-time systems;wavefront correction},
month = sep,
pages = {1-6},
title = {{Evaluating Latency and Throughput Bound Acceleration of FPGAs and GPUs for Adaptive Optics Algorithms}},
year = {2014},
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