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

differences of any kind.37 Aurora Innovation Finding performance regressions Gain insights and analytics about how your software is evolving over time.38 Aurora Innovation Reporting Results Subscribe root_type Messages;52 Aurora Innovation Data ingestion w/ async processing53 Aurora Innovation Data ingestion w/ on-demand processing54 Aurora Innovation Data Retention Local Filesystem Backup Con�gurable

use to solve the world’s most complex challenges • We offer solutions to apply location-based analytics to business practices - Visualize and analyze data more effectively - Collaborate and share maps

nlohmann/json ○ Build scripts for both build systemsCopy+Paste sharingStatic library sharing …sharing an analytics library● Event dispatch ● local caching ● opt-in management ● user-id management ● Reactive

Debugging, Cross-Platform & Source ControlBuild Insights Analyze and optimize your build • Detailed analytics about your C++ builds • Integrated into Visual Studio • Visualize your include tree • Identify

Scott are the CMake guys!Kitware Overview / Built on open source Computer Vision Data and Analytics Scientific Computing Medical Computing Software Solutions6 Where did CMake come from? •

including systems, programming languages, software [tile] DB libraries, and large-scale graph analytics. Open-source software projects resulting from his work include the Matrix Template Library, the

small- scale parallel systems. Working on GBTL, a linear algebra-based C++ library for graph analytics.[DISTRIBUTION STATEMENT A] This material has been approved for public release and unlimited distribution

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

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