arXiv subjects

Yichen Guo

Publications and source records attributed to Yichen Guo.

27 records · Page 2Linked to original sources

arXiv2025

An Adaptive Mixed Precision and Dynamically Scaled Preconditioned Conjugate Gradient Algorithm

We propose an adaptive mixed precision and dynamically scaled preconditioned conjugate gradient algorithm (AMP-PCG). It dynamically adjusts the precision for storing vectors and computing, exploiting low precision when appropriate, while maintaining a convergence rate and accuracy comparable to that of double precision PCG. Our mixed precision strategy consists of three main components: (1) The residual and matrix-vector product are initially computed in double precision, and the algorithm switches these to single precision based on the chosen convergence tolerance and an estimate of the residual gap. (2) Depending on the eigenvalue distribution, the preconditioned residual and search direction are either in half precision throughout the iterations or initially in double precision and then stepwise reduced to single and half precision. (3) A dynamically scaled residual is used at every iteration to mitigate underflow in half precision. We provide theoretical support for our estimates and we demonstrate the effectiveness of AMP-PCG through numerical experiments, highlighting both its robustness and the significant performance gains (1.63x speedup) achieved compared to double precision PCG on a GPU.

math.NA↗

arXiv2024

Spectral Element Simulation of Liquid Metal Magnetohydrodynamics

A spectral-element-based formulation of incompressible MHD is presented in the context of the open-source fluid-thermal code, Nek5000/RS. The formulation supports magnetic fields in a solid domain that surrounds the fluid domain. Several steady-state and time-transient model problems are presented as part of the code verification process. Nek5000/RS is designed for large-scale turbulence simulations, which will be the next step with this new MHD capability.

cs.CE↗

arXiv2023

Uncertainty Guided Adaptive Warping for Robust and Efficient Stereo Matching

Correlation based stereo matching has achieved outstanding performance, which pursues cost volume between two feature maps. Unfortunately, current methods with a fixed model do not work uniformly well across various datasets, greatly limiting their real-world applicability. To tackle this issue, this paper proposes a new perspective to dynamically calculate correlation for robust stereo matching. A novel Uncertainty Guided Adaptive Correlation (UGAC) module is introduced to robustly adapt the same model for different scenarios. Specifically, a variance-based uncertainty estimation is employed to adaptively adjust the sampling area during warping operation. Additionally, we improve the traditional non-parametric warping with learnable parameters, such that the position-specific weights can be learned. We show that by empowering the recurrent network with the UGAC module, stereo matching can be exploited more robustly and effectively. Extensive experiments demonstrate that our method achieves state-of-the-art performance over the ETH3D, KITTI, and Middlebury datasets when employing the same fixed model over these datasets without any retraining procedure. To target real-time applications, we further design a lightweight model based on UGAC, which also outperforms other methods over KITTI benchmarks with only 0.6 M parameters.

cs.CV↗

arXiv2023

Stopping Criteria for the Conjugate Gradient Algorithm in High-Order Finite Element Methods

We consider stopping criteria that balance algebraic and discretization errors for the conjugate gradient algorithm applied to high-order finite element discretizations of Poisson problems. Firstly, we introduce a new stopping criterion that suggests stopping when the norm of the linear system residual is less than a small fraction of an error indicator derived directly from the residual. This indicator shares the same mesh size and polynomial degree scaling as the norm of the residual, resulting in a robust criterion regardless of the mesh size, the polynomial degree, and the shape regularity of the mesh. Secondly, for solving Poisson problems with highly variable piecewise constant coefficients, we introduce a subdomain-based criterion that recommends stopping when the norm of the linear system residual restricted to each subdomain is smaller than the corresponding indicator also restricted to that subdomain. Reliability and efficiency theorems for the first criterion are established. Numerical experiments, including tests with highly variable piecewise constant coefficients and a GPU-accelerated three-dimensional elliptic solver, demonstrate that the proposed criteria efficiently avoid both premature termination and over-solving.

math.NA↗

arXiv2023

Uniform error bound of an exponential wave integrator for the long-time dynamics of the nonlinear Schr\"odinger equation with wave operator

We establish the uniform error bound of an exponential wave integrator Fourier pseudospectral (EWI-FP) method for the long-time dynamics of the nonlinear Schr\"odinger equation with wave operator (NLSW), in which the strength of the nonlinearity is characterized by $\varepsilon^{2p}$ with $\varepsilon \in (0, 1]$ a dimensionless parameter and $p \in \mathbb{N}^+$. When $0 < \varepsilon \ll 1$, the long-time dynamics of the problem is equivalent to that of the NLSW with $O(1)$-nonlinearity and $O(\varepsilon)$-initial data. The NLSW is numerically solved by the EWI-FP method which combines an exponential wave integrator for temporal discretization with the Fourier pseudospectral method in space. We rigorously establish the uniform $H^1$-error bound of the EWI-FP method at $O(h^{m-1}+\varepsilon^{2p-\beta}\tau^2)$ up to the time at $O(1/\varepsilon^{\beta})$ with $0 \leq \beta \leq 2p$, the mesh size $h$, time step $\tau$ and $m \geq 2$ an integer depending on the regularity of the exact solution. Finally, numerical results are provided to confirm our error estimates of the EWI-FP method and show that the convergence rate is sharp.

math.NA↗

arXiv2022

DAQE: Enhancing the Quality of Compressed Images by Exploiting the Inherent Characteristic of Defocus

Image defocus is inherent in the physics of image formation caused by the optical aberration of lenses, providing plentiful information on image quality. Unfortunately, existing quality enhancement approaches for compressed images neglect the inherent characteristic of defocus, resulting in inferior performance. This paper finds that in compressed images, significantly defocused regions have better compression quality, and two regions with different defocus values possess diverse texture patterns. These observations motivate our defocus-aware quality enhancement (DAQE) approach. Specifically, we propose a novel dynamic region-based deep learning architecture of the DAQE approach, which considers the regionwise defocus difference of compressed images in two aspects. (1) The DAQE approach employs fewer computational resources to enhance the quality of significantly defocused regions and more resources to enhance the quality of other regions; (2) The DAQE approach learns to separately enhance diverse texture patterns for regions with different defocus values, such that texture-specific enhancement can be achieved. Extensive experiments validate the superiority of our DAQE approach over state-of-the-art approaches in terms of quality enhancement and resource savings.

eess.IV↗

arXiv2021

Blind VQA on 360{\deg} Video via Progressively Learning from Pixels, Frames and Video

Blind visual quality assessment (BVQA) on 360{\textdegree} video plays a key role in optimizing immersive multimedia systems. When assessing the quality of 360{\textdegree} video, human tends to perceive its quality degradation from the viewport-based spatial distortion of each spherical frame to motion artifact across adjacent frames, ending with the video-level quality score, i.e., a progressive quality assessment paradigm. However, the existing BVQA approaches for 360{\textdegree} video neglect this paradigm. In this paper, we take into account the progressive paradigm of human perception towards spherical video quality, and thus propose a novel BVQA approach (namely ProVQA) for 360{\textdegree} video via progressively learning from pixels, frames and video. Corresponding to the progressive learning of pixels, frames and video, three sub-nets are designed in our ProVQA approach, i.e., the spherical perception aware quality prediction (SPAQ), motion perception aware quality prediction (MPAQ) and multi-frame temporal non-local (MFTN) sub-nets. The SPAQ sub-net first models the spatial quality degradation based on spherical perception mechanism of human. Then, by exploiting motion cues across adjacent frames, the MPAQ sub-net properly incorporates motion contextual information for quality assessment on 360{\textdegree} video. Finally, the MFTN sub-net aggregates multi-frame quality degradation to yield the final quality score, via exploring long-term quality correlation from multiple frames. The experiments validate that our approach significantly advances the state-of-the-art BVQA performance on 360{\textdegree} video over two datasets, the code of which has been public in \url{https://github.com/yanglixiaoshen/ProVQA.}

cs.CV↗

arXiv2019

TS Cache: A Fast Cache with Timing-speculation Mechanism Under Low Supply Voltages

To mitigate the ever-worsening Power Wall problem, more and more applications need to expand their power supply to the wide-voltage range including the near-threshold region. However, the read delay distribution of the SRAM cells under the near-threshold voltage shows a more serious long-tail characteristic than that under the nominal voltage due to the process fluctuation. Such degradation of SRAM delay makes the SRAM-based cache a performance bottleneck of systems as well. To avoid the unreliable data reading, circuit-level studies use larger/more transistors in a bitcell by scarifying chip area and the static power of cache arrays. Architectural studies propose the auxiliary error correction or block disabling/remapping methods in fault-tolerant caches, which worsen both the hit latency and energy efficiency due to the complex accessing logic. This paper proposes the Timing-Speculation (TS) cache to boost the cache frequency and improve energy efficiency under low supply voltages. In the TS cache, the voltage differences of bitlines are continuously evaluated twice by a sense amplifier (SA), and the access timing error can be detected much earlier than that in prior methods. According to the measurement results from the fabricated chips, the TS L1 cache aggressively increases its frequency to 1.62X and 1.92X compared with the conventional scheme at 0.5V and 0.6V supply voltages, respectively.

cs.AR↗

arXiv2017

A Robust Fiber-based Frequency Synchronization System Immune to Dramatic Temperature Fluctuation

Fiber-based frequency synchronization system is sensitive to temperature change because of the limited isolation and nonlinear effect of RF components in the system. In order to make it suitable for the use of large-scale scientific and engineering projects in which the ambient temperature of the fiber link changes dramatically, we designed a non-harmonic frequency dissemination system immune to temperature fluctuation. After the lab tests in which the ambient temperature of fiber fluctuates 40 degrees centigrade per day and 20 degrees centigrade per hour respectively, the relative frequency stabilities of this system reached 4.0 E-14/s and 3.0 E-16/1E4 s. It is demonstrated that the proposed non-harmonic scheme shows a strong robustness to the complicated working environment with dramatic temperature fluctuation.

physics.ins-det↗