We present an efficient algorithm for regularized optimal transport. In
contrast to previous methods, we use the Douglas-Rachford splitting technique
to develop an efficient solver that can handle a broad class of regularizers.
The algorithm has strong global convergence guarantees, low per-iteration cost,
and can exploit GPU parallelization, making it considerably faster than the
state-of-the-art for many problems. We illustrate its competitiveness in
several applications, including domain adaptation and learning of generative
models.

本文提出了一种高效的正则化最优输运算法，采用 Douglas-Rachford 分裂技术，具有强大的全局收敛性保证和低迭代成本，可利用 GPU 并行化加速，在诸多问题方面明显快于现有技术，包括领域自适应和生成模型学习。

将正则化的最优输运带入光速：一种适用于 GPU 的分裂方法

Bringing regularized optimal transport to lightspeed: a splitting method  adapted for GPUs

The article introduces a new method for applying Quantum Clustering to graph
structures. Quantum Clustering (QC) is a novel density-based unsupervised
learning method that determines cluster centers by constructing a potential
function. In this method, we use the Graph Gradient Descent algorithm to find
the centers of clusters. GPU parallelization is utilized for computing
potential values. We also conducted experiments on five widely used datasets
and evaluated using four indicators. The results show superior performance of
the method. Finally, we discuss the influence of $\sigma$ on the experimental
results.

本文介绍了一种新的方法，将量子聚类应用于图结构中，通过构建潜函数采用图梯度下降算法找到聚类中心，利用 GPU 并行计算势能值来评估方法性能，实验结果表明该方法具有优异的性能，最后还研究了 σ 对实验结果的影响。

基于 GPU 并行算法的图分析：量子聚类

Graphy Analysis Using a GPU-based Parallel Algorithm: Quantum Clustering

Despite that convolution neural networks (CNN) have recently demonstrated
high-quality reconstruction for video super-resolution (VSR), efficiently
training competitive VSR models remains a challenging problem. It usually takes
an order of magnitude more time than training their counterpart image models,
leading to long research cycles. Existing VSR methods typically train models
with fixed spatial and temporal sizes from beginning to end. The fixed sizes
are usually set to large values for good performance, resulting to slow
training. However, is such a rigid training strategy necessary for VSR? In this
work, we show that it is possible to gradually train video models from small to
large spatial/temporal sizes, i.e., in an easy-to-hard manner. In particular,
the whole training is divided into several stages and the earlier stage has
smaller training spatial shape. Inside each stage, the temporal size also
varies from short to long while the spatial size remains unchanged. Training is
accelerated by such a multigrid training strategy, as most of computation is
performed on smaller spatial and shorter temporal shapes. For further
acceleration with GPU parallelization, we also investigate the large minibatch
training without the loss in accuracy. Extensive experiments demonstrate that
our method is capable of largely speeding up training (up to $6.2\times$
speedup in wall-clock training time) without performance drop for various VSR
models. The code is available at
this https URL.

本文提出采用多级培训策略来加速视频超分辨率模型的训练，早期阶段的模型尺寸更小。同时，使用 GPU 并行化加快培训速度。实验证明，该策略可以在保持准确率的情况下加速培训达 6.2 倍。

加速视频超分辨率模型的训练

Accelerating the Training of Video Super-Resolution Models

We introduce CuLE (CUDA Learning Environment), a CUDA port of the Atari
Learning Environment (ALE) which is used for the development of deep
reinforcement algorithms. CuLE overcomes many limitations of existing CPU-based
emulators and scales naturally to multiple GPUs. It leverages GPU
parallelization to run thousands of games simultaneously and it renders frames
directly on the GPU, to avoid the bottleneck arising from the limited CPU-GPU
communication bandwidth. CuLE generates up to 155M frames per hour on a single
GPU, a finding previously achieved only through a cluster of CPUs. Beyond
highlighting the differences between CPU and GPU emulators in the context of
reinforcement learning, we show how to leverage the high throughput of CuLE by
effective batching of the training data, and show accelerated convergence for
A2C+V-trace. CuLE is available at this https URL .

CuLE 是一种用于开发深度强化学习算法的 CUDA 版 Atari Learning Environment，通过 GPU 并行化运行数千个游戏，直接将帧渲染到 GPU，以规避有限的 CPU-GPU 通信带宽瓶颈，最高可在单个 GPU 上每小时生成 155M 帧。