linux block io: introducing multi-queue ssd access on multi-core systems

Linux block IO（块输入输出）是Linux操作系统的IO子系统，用于管理块设备（例如硬盘和SSD）的访问。在多核系统上引入多队列SSD访问是一种优化措施。

传统上，Linux操作系统在处理块设备访问时，使用单个队列（queue）来处理所有IO请求。这种单队列设计对于单核系统来说是合适的，因为只有一个CPU核心可以处理IO请求。然而，在多核系统中，这种设计却成为了性能瓶颈，因为所有的IO请求都必须经过单个队列，即使有多个CPU核心是可用的。

为了解决这个问题，Linux引入了多队列SSD访问功能。这意味着在多核系统上，每个CPU核心都有一个独立的队列来处理IO请求。每个队列可以独立处理IO请求，而不会受到其他队列的干扰。这种设计可以提高系统的并发性和吞吐量。

多队列SSD访问还可以充分利用SSD设备的性能特点。SSD设备通常具有多个通道（channel）和多个闪存芯片（chip），每个通道和芯片都可以同时处理IO请求。通过将IO请求分配给多个队列，可以同时利用多个通道和芯片，从而提高SSD的性能。

在Linux中实现多队列SSD访问需要对内核进行相应的修改和配置。用户可以通过命令和配置文件来设置每个队列的属性和参数，以满足特定场景下的需求。

总之，通过引入多队列SSD访问，Linux在多核系统上可以更好地利用硬件资源，提高系统的性能和吞吐量。这是一个重要的优化措施，可以提高块设备访问的效率和响应速度。

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cvt: introducing convolutions to vision transformers

cvt是一种将卷积引入视觉变换器的方法。传统的视觉变换器使用自注意力机制来捕捉图像中的空间关系，但是这种方法在处理大尺寸图像时会变得非常缓慢。cvt通过引入卷积操作来加速这个过程，同时保持了自注意力机制的优点。这种方法可以提高视觉变换器的效率和准确性，特别是在处理大型图像时。

Transformer-Based Visual Segmentation: A Survey

Visual segmentation is one of the most important tasks in computer vision, which involves dividing an image into multiple segments, each of which corresponds to a different object or region of interest in the image. In recent years, transformer-based methods have emerged as a promising approach for visual segmentation, leveraging the self-attention mechanism to capture long-range dependencies in the image.

This survey paper provides a comprehensive overview of transformer-based visual segmentation methods, covering their underlying principles, architecture, training strategies, and applications. The paper starts by introducing the basic concepts of visual segmentation and transformer-based models, followed by a discussion of the key challenges and opportunities in applying transformers to visual segmentation.

The paper then reviews the state-of-the-art transformer-based segmentation methods, including both fully transformer-based approaches and hybrid approaches that combine transformers with other techniques such as convolutional neural networks (CNNs). For each method, the paper provides a detailed description of its architecture and training strategy, as well as its performance on benchmark datasets.

Finally, the paper concludes with a discussion of the future directions of transformer-based visual segmentation, including potential improvements in model design, training methods, and applications. Overall, this survey paper provides a valuable resource for researchers and practitioners interested in the field of transformer-based visual segmentation.