This paper presents GPFC, a novel Graphics Processing Unit (GPU) Phase
Folding and Convolutional Neural Network (CNN) system to detect exoplanets
using the transit method. We devise a fast folding algorithm parallelized on a
GPU to amplify low signal-to-noise ratio transit signals, allowing a search at
high precision and speed. A CNN trained on two million synthetic light curves
reports a score indicating the likelihood of a planetary signal at each period.
GPFC improves on speed by three orders of magnitude over the predominant
Box-fitting Least Squares (BLS) method. Our simulation results show GPFC
achieves 97% training accuracy, higher true positive rate at the same false
positive rate of detection, and higher precision at the same recall rate when
compared to BLS. GPFC recovers 100% of known ultra-short-period planets in
Kepler light curves from a blind search. These results highlight the promise of
GPFC as an alternative approach to the traditional BLS algorithm for finding
new transiting exoplanets in data taken with Kepler and other space transit
missions such as K2, TESS and future PLATO and Earth 2.0.

GPFC 是一种新颖的图形处理单元（GPU）相位折叠和卷积神经网络（CNN）系统，用于利用凌日法检测系外行星。该系统采用在 GPU 上并行化的快速折叠算法来放大信噪比低的凌日信号，从而以高精度和速度进行搜索。经过对两百万个合成光曲线进行训练的 CNN 报告了每个周期可能性的得分，GPFC 在速度上比现有的盒形拟合最小二乘（BLS）方法快了三个数量级。模拟结果显示，与 BLS 相比，GPFC 实现了 97% 的训练准确性，在相同的误报率下具有更高的真阳性率和在相同的检测率下具有更高的准确性。在 Kepler 光曲线中进行盲搜索时，GPFC 可以完全恢复已知的超短周期行星。这些结果突显了 GPFC 作为替代传统 BLS 算法在 Kepler 和其他空间凌日任务（如 K2、TESS 和未来的 PLATO 和 Earth 2.0）的数据中寻找新的凌日行星的潜力。

用于探测系外行星凌日的 GPU 相位折叠和深度学习方法

The GPU Phase Folding and Deep Learning Method for Detecting Exoplanet  Transits

Although state-of-the-art (SOTA) SAT solvers based on conflict-driven clause
learning (CDCL) have achieved remarkable engineering success, their sequential
nature limits the parallelism that may be extracted for acceleration on
platforms such as the graphics processing unit (GPU). In this work, we propose
FastFourierSAT, a highly parallel hybrid SAT solver based on gradient-driven
continuous local search (CLS). This is realized by a novel parallel algorithm
inspired by the Fast Fourier Transform (FFT)-based convolution for computing
the elementary symmetric polynomials (ESPs), which is the major computational
task in previous CLS methods. The complexity of our algorithm matches the best
previous result. Furthermore, the substantial parallelism inherent in our
algorithm can leverage the GPU for acceleration, demonstrating significant
improvement over the previous CLS approaches. We also propose to incorporate
the restart heuristics in CLS to improve search efficiency. We compare our
approach with the SOTA parallel SAT solvers on several benchmarks. Our results
show that FastFourierSAT computes the gradient 100+ times faster than previous
prototypes implemented on CPU. Moreover, FastFourierSAT solves most instances
and demonstrates promising performance on larger-size instances.

提出了基于梯度驱动连续局部搜索（CLS）的高度并行混合 SAT 求解器 FastFourierSAT，该算法利用图形处理单元（GPU）的并行性能加速计算，可以比之前的方法快 100 + 倍，并在更大规模的案例上表现出良好的性能。

基于 GPU 的混合 SAT 求解的大规模并行连续局部搜索

Massively Parallel Continuous Local Search for Hybrid SAT Solving on  GPUs

The alternating direction method of multipliers (ADMM) is a powerful operator
splitting technique for solving structured convex optimization problems. Due to
its relatively low per-iteration computational cost and ability to exploit
sparsity in the problem data, it is particularly suitable for large-scale
optimization. However, the method may still take prohibitively long to compute
solutions to very large problem instances. Although ADMM is known to be
parallelizable, this feature is rarely exploited in real implementations. In
this paper we exploit the parallel computing architecture of a graphics
processing unit (GPU) to accelerate ADMM. We build our solver on top of OSQP, a
state-of-the-art implementation of ADMM for quadratic programming. Our
open-source CUDA C implementation has been tested on many large-scale problems
and was shown to be up to two orders of magnitude faster than the CPU
implementation.

本文通过在 GPU 上建立 ADMM 的求解器来加速大规模优化问题的求解，实现了高效地利用算力提高求解速度，并与 CPU 实现相比，速度提高了数十倍。