析 • 对⽐测试 • ⽂件访问测试直接访问ext4 • 通过FUSE访问passthrough_ll底层ext4 • 内核调⽤延迟测试 • 与FUSE Daemon通讯120us左右，FUSE Daemon⼤概10us以内 • 瓶颈在/dev/fuse通讯开销基于FUSE可能的优化点 • 降低内核与libfuse通讯延迟 • 基于⽂件属性的操作内核直接返回？ • vpp / f-stack / DirectFUSE • Kernel版本实现 • BentoFS 基于rust的实现采⽤LD_Preload⽅式瓶颈分析 • 环境 • FUSE daemon使⽤ passthrough_ll 调⽤底层ext4 • 进程共享内存通信延迟10us+ • others 开销 10us+ • fuse_ll_ops开销10us-基于FUSE的优化框架

所以容易移植现有代码到PFS存储引擎 ●CurveBS对文件系统元数据的操作非常少，对文件系统的要求不高， 所以不需要元数据高性能，这方面PFS也合适10/17/22 6 对PFS的修改 ●基于阿里开源的PFS ●不再基于daemon模式，而是直接使用pfs core api ●依然向外提供管理工具, 例如 pfs ls、cp、rm等 ●增加spdk驱动10/17/22 7 新增PFS接口 ●增加pfs_pwritev和pfs_preadv接口 ●网络部分依赖内核tcp，不是零copy10/17/22 18 进展 ●还在测试CurveBS ●布置、监控等工具需要更新10/17/22 19 性能测试 ●使用pfs daemon测试 ●估计非daemon模式的会更快一点，因为没有跨进程开销10/17/22 20 Write,DMA write,Write-zero测试10/17/22 21 fio 4k 1个并发+单深度10/17/22

semi-borderless capital…all driving massive change. Sport provides a good analogy for AI’s constant improvements. As athletes continue to wow us and break records, their talent is increasingly enhanced by better Breakthroughs in large models, cost-per-token declines, open-source proliferation and chip performance improvements are making new tech advances increasingly more powerful, accessible, and economically viable algorithms, based on how much computing power you'd need to reach top performance without any improvements. Source: Epoch AI (3/24) Impact of Improved Algorithms on AI Model Performance – 2014-2023, per

后台刷数据线程 本地磁盘缓存 关键数据结构 详细设计 Write流程 Read流程 ReleaseCache流程 Flush流程 FsSync流程 后台流程 poc测试验证 背景 基于s3的daemon版本基于基本的性能测试发现性能非常差。具体数据如下： 通过日志初步分析有2点原因© XXX Page 3 of 9 1.append接口目前采用先从s3 get，在内存中合并完后再put的方式，对s3操作过多 更新元数据，清理DataCache缓存，DataCacheNum_减1。 5.遍历完一轮DataCache后，获取DataCacheNum值，如果不为0，则继续遍历，如果为0则回到1步骤。 poc测试验证 根据上述设计，完成初步daemon，测试结果如下图 目前看写性能有明显的提升，但时延仍然很高， 。 需要进一步分析

contribute to TVML. ee Today we'ltouch on a few of those contribution areas: o Core Infrastructure Improvements to TVM o_uTVM: support for microcontrollers in TVM o_ Virtual Machine and dynamic NNs support High-Level 人 ORGREEE Te Conv2D mized RE -一 一 QQ octoML Transformer Improvements Transformer based models such as BERT have recently become very Popular and require first class many reshape operations, which are currently implemented using copy, 10 Virtual Machine e Many improvements from contributors at UW, AWS, and OctoML. e Initial implementation is quickly moving towards

complex, interconnected workflows and systems. We’re seeing AI deliver significant, measurable improvements on three fronts: 01 Workforce performance Helping people deliver higher-quality outputs in shorter deployment to learn quickly from customer use cases and use that information to accelerate product improvements. That means shipping updates regularly, getting feedback, and improving performance and safety through iteration. The earlier you start, the more your organization benefits from compounding improvements. Klarna, a global payments network and shopping platform, introduced a new AI assistant to

of Constraints “Eliyahu M. Goldratt, who created the Theory of Constraints, showed us how any improvements made anywhere besides the bottleneck are an illusion . Astonishing, but true useless, because it will always remain starved, waiting for work from the bottleneck. And any improvements made before the bottleneck merely results in more inventory piling up at the bottleneck.” Resource

characters outside the Latin alphabet). 6.9.2 What’s new in Tornado 6.5.0 May 15, 2025 Security Improvements • Previously, malformed multipart-form-data requests could log multiple warnings and constitute on_message_callback is not deprecated. 6.9.3 What’s new in Tornado 6.4.2 Nov 21, 2024 Security Improvements • Parsing of the cookie header is now much more efficient. The older algorithm sometimes had 167Tornado Documentation, Release 6.5.1 6.9.4 What’s new in Tornado 6.4.1 Jun 6, 2024 Security Improvements • Parsing of the Transfer-Encoding header is now stricter. Unexpected transfer-encoding values

ideas don’t work and reinforcing those that do3. local learnings are rapidly turned into global improvements, so that new techniques and practices can be used by the entire organization iii. ENABLING THE IMPROVEMENT OF DAILY WORK 1. Mike Rother observed in Toyota Kata that in the absence of improvements, processes don’t stay the same—due to chaos and entropy, processes actually degrade over time

resilient systems with higher degrees of assurance 3. Ch. 20 – Convert Local Discoveries into Global Improvements a. USE CHAT ROOMS AND CHAT BOTS TO AUTOMATE AND CAPTURE ORGANIZATIONAL KNOWLEDGE i. ChatOps Goal is focused improvement on daily work, not experimentation and innovation iii. Demo back improvements at the completion of the blitz iv. Empower those closest to the work to continually identify