Mixed-Precision Tomographic Reconstructor Computations on Hardware Accelerators

Doucet, Nicolas; Ltaief, Hatem; Gratadour, Damien; Keyes, David E.

doi:10.1109/ia349570.2019.00011

Cited by 11 publications

(9 citation statements)

References 24 publications

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“…We compare the proposed mixed-precision Cholesky against two state-of-the-art mixed-precision applications on shared- and distributed-memory, i.e., a computational astronomy (i.e., MOAO_StarPU [58]) and a geostatistics applications (i.e., ExaGeoStat_StarPU [4]), with 20S:80H and 10D:90S mixed precision configurations, respectively. We only report on these two configurations since they maintain sufficient accuracy for both applications.…”

Section: Performance Comparisonsmentioning

confidence: 99%

Accelerating Geostatistical Modeling and Prediction With Mixed-Precision Computations: A High-Productivity Approach With PaRSEC

Abdulah

Cao

Pei

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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Geostatistical modeling, one of the prime motivating applications for exascale computing, is a technique for predicting desired quantities from geographically distributed data, based on statistical models and optimization of parameters. Spatial data is assumed to possess properties of stationarity or non-stationarity via a kernel fitted to a covariance matrix. A primary workhorse of stationary spatial statistics is Gaussian maximum log-likelihood estimation (MLE), whose central data structure is a dense, symmetric positive definite covariance matrix of dimension of the number of correlated observations. Two essential operations in MLE are the application of the inverse and evaluation of the determinant of the covariance matrix. These can be rendered through the Cholesky decomposition and triangular solution. In this contribution, we reduce the precision of weakly correlated locations to single-or half-precision based on distance. We thus exploit mathematical structure to migrate MLE to a three-precision approximation that takes advantage of contemporary architectures offering BLAS3-like operations in a single instruction that are extremely fast for reduced precision. We illustrate application-expected accuracy worthy of doubleprecision from a majority half-precision computation, in a context where uniform single precision is by itself insufficient. In tackling the complexity and imbalance caused by the mixing of three precisions, we deploy the PaRSEC runtime system. PaRSEC delivers on-demand casting of precisions while orchestrating tasks and data movement in a multi-GPU distributed-memory environment within a tile-based Cholesky factorization. Application-expected accuracy is maintained while achieving up to 1.59X by mixing FP64/FP32 operations on 1536 nodes of HAWK or 4096 nodes of Shaheen II, and up to 2.64X by mixing FP64/FP32/FP16 operations on 128 nodes of Summit, relative to FP64-only operations, This translates into up to 4.5, 4.7, and 9.1 (mixed) PFlop/s sustained performance, respectively, demonstrating a synergistic combination of exascale architecture, dynamic runtime software, and algorithmic adaptation applied to challenging environmental problems.

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Section: Performance Comparisonsmentioning

confidence: 99%

Accelerating Geostatistical Modeling and Prediction With Mixed-Precision Computations: A High-Productivity Approach With PaRSEC

Abdulah

Cao

Pei

et al. 2022

IEEE Trans. Parallel Distrib. Syst.

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“…In particular, TLR has been successful in solving dense/sparse linear algebra problems at scale [2-4, 6, 15, 17, 18, 38] on a broad range of hardware architectures (i.e., x86, accelerators, shared/distributedmemory systems). As far as leveraging Adaptive Optics (AO) workloads with HPC, there are mostly works in supporting Soft-RTC (SRTC) [19,25,28,32,40] based on dense linear algebra algorithms powered by dynamic runtime systems [1,10,12] in the context of the MOSAIC instrument for the European Extremely Large Telescopes [35] and the Subaru Coronagraphic Extreme-AO (SCExAO) system of the Japanese Subaru telescope [41].…”

Section: Related Workmentioning

confidence: 99%

Meeting the real-time challenges of ground-based telescopes using low-rank matrix computations

Ltaief

Cranney

Gratadour

et al. 2021

Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis

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Adaptive Optics (AO) is a technology that permits to measure and mitigate the distortion effects of atmospheric turbulence on optical beams. AO must operate in real-time by controlling thousands of actuators to shape the surface of deformable mirrors deployed on ground-based telescopes to compensate for these distortions. The command vectors that trigger how each individual actuator should act to bend a portion of the mirror are obtained from Matrix-Vector Multiplications (MVM). We identify and leverage the data sparsity structure of these control matrices coming from the MAVIS instruments for the European Southern Observatory's Very Large Telescope. We provide performance evaluation on x86 and acceleratorbased systems. We present the impact of tile low-rank (TLR) matrix approximations on time-to-solution for the MVM and assess the produced image quality. We achieve performance improvement up to two orders of magnitude for TLR-MVM compared to regular dense MVM, while maintaining the image quality.

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“…It has been widely applied in different application domains, especially, in deep learning applications [13,14]. Existing studies propose the use of reduced precision also for the deconvolution kernel [15], apply mixed precision to other steps of the radio-astronomical imaging acquisition pipeline, e.g., correlator [16], or other radio-astronomy domains, e.g., computation of tomographic reconstructors [17]. However, the works mentioned above employ standard data types supported by CPUs and GPUs, such as double-, single-and half-precision floating-point, and do not evaluate custom data types.…”

Section: Introductionmentioning

confidence: 99%

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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Mixed-Precision Tomographic Reconstructor Computations on Hardware Accelerators

Cited by 11 publications

References 24 publications

Accelerating Geostatistical Modeling and Prediction With Mixed-Precision Computations: A High-Productivity Approach With PaRSEC

Accelerating Geostatistical Modeling and Prediction With Mixed-Precision Computations: A High-Productivity Approach With PaRSEC

Meeting the real-time challenges of ground-based telescopes using low-rank matrix computations

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

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