mine, all mine! You can’t have them. Agenda 1. Current status of C++ safety: MISRA and C++ CG 2. Parallel Safety rules 3. Automotive Safety case Safety Critical API Evolution minimize API surface area no harm What is still missing? So far most only deal with Sequential code Very few deal with Parallel code Even fewer deal with Concurrent, event driven code None deal with Heterogeneous dispatch rules pulled from • C++CG • HIC++ • REphrase H2020 project • CERT C++ • JSF++ (no parallel rules) • WG23 (no parallel rules) • Added some from our own contributions • Many joined, average 5-8 per meeting

programs using parallel algorithms and executors Mark Hoemmen* mhoemmen@stellarscience.com CppCon 2020 * hoʊ’mən, or hœm’mən; he/himWho am I? • > 10 years post-PhD experience writing parallel C++ for for science and engineering • Background: Parallel algorithms for big linear algebra problems • 1st WG21: Nov 2017 • Started new job at Stellar Science in March Eschew raw pointersOutline • Parallelism Parallelism makes error handling harder… • …C++ parallel algorithms and tasks specifically • Message Passing Interface (MPI): 3 decades of distributed-memory parallel programming • MPI teaches design patterns

data-parallel types Dr. Matthias Kretz GSI Helmholtz Center for Heavy Ion Research CppCon ’23 @mkretz@floss.social github.com/mattkretzMotivation std::simd Overview Example: Image Processing Programming 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

Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 1/23: Stream Processing Fundamentals Vasiliki Kalavri | Boston University University 2020 What is a stream? • In traditional data processing applications, we know the entire dataset in advance, e.g. tables stored in a database. A data stream is a data set that is produced incrementally incrementally over time, rather than being available in full before its processing begins. • Data streams are high-volume, real-time data that might be unbounded • we cannot store the entire stream

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

Scalable Stream Processing - Spark Streaming and Flink Amir H. Payberah payberah@kth.se 05/10/2018 The Course Web Page https://id2221kth.github.io 1 / 79 Where Are We? 2 / 79 Stream Processing Systems Design Design Issues ▶ Continuous vs. micro-batch processing ▶ Record-at-a-Time vs. declarative APIs 3 / 79 Outline ▶ Spark streaming ▶ Flink 4 / 79 Spark Streaming 5 / 79 Contribution ▶ Design issues issues • Continuous vs. micro-batch processing • Record-at-a-Time vs. declarative APIs 6 / 79 Spark Streaming ▶ Run a streaming computation as a series of very small, deterministic batch jobs. • Chops

??? Vasiliki Kalavri | Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/16: Skew mitigation ??? Vasiliki Kalavri | Uddin Nasir et. al. The power of both choices: Practical load balancing for distributed stream processing engines. ICDE 2015. • Mitzenmacher, Michael. The power of two choices in randomized load balancing

Vasiliki Kalavri | Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 2/25: State Management Vasiliki Kalavri | Boston to an operator task, i.e. records processed by the same parallel task have access to the same state • It cannot be accessed by other parallel tasks of the same or different operators Keyed state is are distributed across all parallel tasks of the function’s operator. Keyed state can only be used by functions that are applied on a KeyedStream: • When the processing method of a function with

Boston University 2020 CS 591 K1: Data Stream Processing and Analytics Vasiliki (Vasia) Kalavri
 vkalavri@bu.edu Spring 2020 4/14: Stream processing optimizations ??? Vasiliki Kalavri | Boston University serialization cost • if operators are separate, throughput is bounded by either communication or processing cost • if fused, throughput is determined by operator cost only Operator fusion A B A B Filter2()) ??? Vasiliki Kalavri | Boston University 2020 32 Safety • Ensure partitioned state: each parallel operator maintains disjoint state based on a key attribute • Ensure ordering constraints: if