Processing XML and Spreadsheet in Go 续 日 Gopher China Conference Beijing 2021 6/26 - 6/27 Self Introduction The author of the Excelize - Go language spreadsheet library. Familiar with Go language Complex XML 02 • Partial Load • Namespace & Entity • Ser/Deserialize Idempotence High Performance Processing 03 • XML Schema Definition • DOM or SAX OOXML Spreadsheets 04 • Excel XML Specification • work:addr="WORK"> High Performance Processing XML Components Data Model Tom

CppCon ’23 @mkretz@floss.social github.com/mattkretzMotivation std::simd Overview Example: Image Processing Programming Models Outlook Summary Goals and non-goals for this talk • This is not a tutorial CppCon ’23 3 GSI Helmholtz Center for Heavy Ion ResearchMotivation std::simd Overview Example: Image Processing Programming Models Outlook Summary Motivation Motivation © by Matthias Kretz Matthias Kretz CppCon CppCon ’23 GSI Helmholtz Center for Heavy Ion ResearchMotivation std::simd Overview Example: Image Processing Programming Models Outlook Summary std::simd is for you! Matthias Kretz CppCon ’23 4 GSI Helmholtz

Design Goals Methodology Performance Results Live Demonstration A Persistent Hash Map for Graph Processing Workloads and a Methodology for Persistent Transactional Data Structures 2IntroductionIntroduction commercially available through Intel® OptaneTM DC Persistent Memory A Persistent Hash Map for Graph Processing Workloads and a Methodology for Persistent Transactional Data Structures 4Introduction Persistent Block Granularity Figure 1: Traditional Memory Hierarchy [1] A Persistent Hash Map for Graph Processing Workloads and a Methodology for Persistent Transactional Data Structures 5Introduction Persistent

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.1.5. Processing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 1.6.3. Processing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 121 1.7.2. Processing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .

parallel code Writing systems as singletons Assuming only one instance is needed Assuming frame processing is happening Filling frame related buffers - crash when full Assuming all game systems are initialised data into global shared system - like object graph, ECS, or database Good for the game - makes processing files individually more difficult Speaker notesOPTIMISATION GOAL Minimise the time taken to process hash ▶ Use caches - for everything possible Processing caches Used internally during data building Store frequently read values - save on IO & processing Output caches Store built data - Can copy rather

438 439 445 450 455 466 489 498 507 513 5 Tools Documenting Kotlin Code Annotation Processing with Kotlin Kotlin Compiler Options Using Gradle Using Maven Using Ant Kotlin and OSGi Compiler allowing to use all existing Android libraries in a Kotlin application. This includes annotation processing, so databinding and Dagger work too. Footprint: Kotlin has a very compact runtime library, which Zeppelin is provided by interpreters - plugins that enable users to use a speci�c language or data-processing-backend. There are numerous community-maintained interpreters for di�erent programming languages

iterators Ranges and progressions Progression Sequences Construct Sequence operations Sequence processing example Collection operations overview Extension and member functions Common operations Write source code Compile Kotlin and Java sources Enable incremental compilation Configure annotation processing Create JAR file Create self-contained JAR file Specify compiler options Use BOM Generate documentation Improve the speed of builds that use kapt Compile avoidance for kapt Incremental annotation processing Java compiler options Non-existent type correction Use in Maven Use in IntelliJ build system

types Serialization — storing data permanently on external storage Streams and files String processing Arrays and sequences Random numbers Timers Hash tables Hash sets Operating system services will finally introduce advanced concepts like asynchronous operations, threading and parallel processing, macros and meta- programming, and, last but not least, Nim’s concept implementation. Some sections detail in the next section. The most important part of a digital computer is the CPU, the Central Processing Unit. This tiny device, built of digital electronic circuits, can perform very basic mathematical

types Serialization — storing data permanently on external storage Streams and files String processing Arrays and sequences Random numbers Timers Hash tables Hash sets Operating system services will finally introduce advanced concepts like asynchronous operations, threading and parallel processing, macros and meta- programming, and, last but not least, Nim’s concept implementation. Some sections detail in the next section. The most important part of a digital computer is the CPU, the Central Processing Unit. This tiny device, built of digital electronic circuits, can perform very basic mathematical

. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 5.9 Downloading and processing files and images . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 5.10 Ubuntu packages structured data which can be used for a wide range of useful applications, like data mining, information processing or historical archival. Even though Scrapy was originally designed for web scraping, it can also com/questions/11227809/why-is-processing-a-sorted- ˓→array-faster-than-an-unsorted-array", "tags": ["java", "c++", "performance", "optimization"], "title": "Why is processing a sorted array faster than