Go runtime related problems in TiDB production environment About me ● Arthur Mao(毛康力), Senior Engineer@PingCAP ● TiDB core developer (top3 contributor) ● GitBook about golang internals (@tiancaiamao) case study: batching client requests ● Memory control ■ case study: transparent huge pages ● GC Related ■ case study: GC sweep caused latency jitter ■ case study: Lock and NUMA aware Agenda Part I - Compare with other nodes in the cluster with THP disabled Investigate ● So, the root cause must be related to THP (transparent huge pages) ● But … why? Analysis ● Go Runtime manage memory at 8K size granularity

深度学习-Transformer 黄海广 副教授 2 03 Transformer的训练 本章目录 01 Transformer介绍 02 Transformer的工作流程 04 BERT 3 1.Transformer介绍 01 Transformer介绍 03 Transformer的训练 02 Transformer的工作流程 4 1.Transformer介绍 为什么需要用transformer 其实在之前我们使用的是RNN（或者是其的单向或者双向变种LSTM/GRU等） 来 作为编解码器。RNN模块每次只能够吃进一个输入token和前一次的隐藏状态，然 后得到输出。它的时序结构使得这个模型能够得到长距离的依赖关系，但是这也 使得它不能够并行计算，模型效率十分低。 在没有transformer的时候，我们 5 1.Transformer介绍 Seq2Seq任务 Seq2Seq 任务指的是输入和输出都是 序列的任务，输出的长度不确定时采 用的模型，这种情况一般是在机器翻 译的任务中出现，将一句中文翻译成 英文，那么这句英文的长度有可能会 比中文短，也有可能会比中文长，所 以输出的长度就不确定了。 上图，输入的中文长度为4，输出的英文长度为2 6 1.Transformer介绍 Encoder-Decoder模型

1 2023年06月 深度学习-Vision Transformer (ViT) 黄海广 副教授 2 03 模型训练策略 本章目录 01 背景知识 02 模型介绍 04 模型的缺点与改进 05 模型的代码实现 3 1.背景知识 03 模型训练策略 01 背景知识 02 模型介绍 04 模型的缺点与改进 05 all you need的文章，开创性地提出了 在序列转录领域，完全抛弃 CNN和RNN，只依赖Attention-注 意力结构的简单的网络架构， 名为Transformer；论文实现的 任务是机器翻译。 Transformer结构 Multi-Head Attention Add & Norm Input Embedding Output Embedding Feed Inputs Outputs (shifted right) Positional Encoding Positional Encoding 1.背景知识 6 为什么需要用transformer Transformer原本是用来做 NLP的工作的，所以ViT的 首要任务是将图转换成词 的结构，这里采取的方法 是如上图左下角所示，将 图片分割成小块，每个小 块就相当于句子里的一个 词。这里把每个小块称作

Framework containers are no longer tested on Pascal GPU architectures. ‣ Transformer Engine is a library for accelerating Transformer models on NVIDIA GPUs. It includes support for 8-bit floating point (FP8) inference performance with lower memory utilization. Transformer Engine also includes a collection of highly optimized modules for popular Transformer architectures and an automatic mixed precision-like TransformerXL model: This transformer-based language model has a segment-level recurrence and a novel relative positional encoding. The enhancements that were introduced in Transformer-XL help capture better

High-level data flow graph and optimizations Directly generate optimized program for new operator workloads and hardware Hardware FrameworksWhy Automation is the Future Clear winner on emerging models BatchMatMul CuDNN w/ TensorCores tvm w/ TensorCores 1.4x better on emerging workloads Transformer related workloads Credit: Siyuan FengWhere are we goingUnified Runtime For Heterogeneous Devices remote_mod[“npufunction0"] func(remote_a, remote_b)Virtual Machine: Supporting Dynamic Workload Dynamic shape workloads More runtime objects: Arrays, Tuples, Trees, ADTs Minimum runtime for dynamic models Credit:

Normalization: bn, gn, ln?  激活函数: relu, leaky_relu, swish ?  Backbone: resnet, hrnet, mobilenet, transformer?  多任务模型: share-bottom, mmoe, ple?  特征选择/生成: Age, sex, comment, click… 解决方案: 超参搜索 效果提升 模型理解 Blade  推荐模型优化: 千亿特征 3. 工程优化 RingAllReduce + 层级级联 EasyVision 多机多卡性能对比 工程优化: 数据并行  M6模型  Transformer模型: RapidFormer  人脸分类模型: 超大softmax  3D卷积模型 M6模型 RapidFormer性能 工程优化: 模型并行(Whale)  FP16 / Op融合(Fusion Stitch)  MILR: Blade Disc 工程优化: Blade模型推理 Dynamic Shape Compiler for Machine Learning Workloads EmbeddingVariable [No Hash Conflict] 特征准入/淘汰 Adaptive Embedding 训练: 推理: Ring All-reduc同步训练

Compared to Open Source Greenplum Database 129 Server Documentation 130 Client Documentation 130 Related Documentation 131 VMware Greenplum 7.x Release Notes 132 Release 7.0 132 Key New Features 132 documentation describes how to install, configure, and use VMware Greenplum, and provides links to related VMware products that work with Greenplum Database. Welcome to VMware Greenplum 7 VMware Greenplum ODBC/JDBC Driver Documentation VMware Greenplum 7 Documentation VMware by Broadcom 130 Related Documentation Related VMware Greenplum documentation and utilities: Connector for VMware Greenplum and VMware

the checkbox. Show tips on startup Statistics Collection window: Lastly, you may see a window related to Statistics Collection. This feature helps you understand how you use DBeaver, which can enhance visualizes how the color coordinated database connections are used in and views as well as editors related to these Database Navigator Projects connections: Connection types Table of contents DBeaver it empty Embedded Enable it for server-less databases. This flag affects a few config options related to the network/connections management No Authentication This means that driver does not require

the checkbox. Show tips on startup Statistics Collection window: Lastly, you may see a window related to Statistics Collection. This feature helps you understand how you use DBeaver, which can enhance visualizes how the color coordinated database connections are used in and views as well as editors related to these Database Navigator Projects connections: Connection types Table of contents DBeaver it empty Embedded Enable it for server-less databases. This flag affects a few config options related to the network/connections management No Authentication This means that driver does not require

MoMs and other messaging concepts please see the Messaging Concepts. • If you have any questions related to the use or development of Apache ActiveMQ Artemis please use one of our mailing lists. • Official formatted typed properties on CLI producer command • New CLI command pwd for showing directories related to the current instance • Maven Bill of Materials (BOM) artemis-bom to simplify integration • "FirstMessage" general use 15.5.2. Upgrading from 2.32.0 • Due to ARTEMIS-4532 the names of addresses and queues related to MQTT topics and subscriptions respectively may change. This will impact MQTT use-cases if both