1 Quickly Testing Qt Desktop Applications with Approval Tests Clare Macrae (She/her) clare@claremacrae.co.uk 16 September 2020 CppCon (Online)2 Audience: Developers testing Desktop GUIs, including and readable with fixtures • ApprovalTests.Cpp.Qt feedback welcome!80 Quickly Test Qt Desktop Applications • All links from this talk, and more, via: – bit.ly/TestingQt – github.com/claremacrae/talks

console.log(object.foo) Jonathan Müller A fast, compliant JSON pull parser for writing robust applications undefined Jonathan Müller — @foonathan Express your expectations CppCon 2023-10-03 10JSON parsing std::print("{}\n", object.at("foo")); Jonathan Müller A fast, compliant JSON pull parser for writing robust applications Uncaught exception. Jonathan Müller — @foonathan Express your expectations CppCon 2023-10-03

wonderful academic papers about compilers. I'll illustrate how our view of compilers is really monolithic, and how compilers through time, and still today, are actually a continuum.Papers in this talk implementations of these abstractions is inappropriate for three main reasons: 1. it denies applications the advantages of domain-specific optimizations, 2. it discourages changes to the implementations the world’s fastest processors, many applications, especially those requiring high processor performance, have been ported to it. However, ☹ many other applications are available only under the x86 architecture

must be resilient in unanticipated situations • It’s often assumed that error handling inside applications is a good way to provides resiliency • This needs to be questioned • Common error handling examples Language Designer • Can we talk about determinism in C++? • But also … • C++ exceptions are a monolithic sledgehammer • There are no error management primitives that could be used to create domain-appropriate

Depends on eigen (we already use) ● Adopting Native Instrumentsʼ auth library ○ Split out from monolithic nilibs library ○ Replace custom vocab types with std:: ○ Align on 3rd-party libraries/versions

projects: remotely operated underwater inspection vehicles, autonomous mobile base for agriculture applications, and more ● Have worked at General Dynamics Electric Boat, MIT Lincoln Laboratory ● Interested of MoveIt ○ MoveIt is an open source robotics manipulation platform for developing commercial applications, prototyping designs, and benchmarking algorithmsAbout PickNik Robotics ● The Unstructured Robotics of MoveIt ○ MoveIt is an open source robotics manipulation platform for developing commercial applications, prototyping designs, and benchmarking algorithms ● Developing MoveIt Studio ○ MoveIt Studio

responsible for 12% of all fleet cycles across all applications at Google. • 80-90% • According to another study by Palkar et al., modern big data applications can spend 80–90% of CPU time parsing data. Structures in Memory • This high level of performance has significant implications for practical applications. Due to its excellent read and write speeds, XOffsetDatastructure can be used directly as base without sacrificing serialization capabilities, is particularly valuable in performance-critical applications. • It allows for rapid data access and modification while still maintaining the ability to quickly

responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering, and early testbed platforms, in support of the nation’s directly compiled and accelerated SYCL is ideal for accelerating larger C++-based engines and applications with performance portability C++ Kernel Fusion can give better performance on complex apps performance Based on C++17, backwards compatible with SYCL 1.2.1 Simplify porting of standard C++ applications to SYCL Closer alignment and integration with ISO C++ Multiple Backend acceleration and API

robust solution for integrating machine learning capabilities directly into C++-based systems and applications. I. INTRODUCTION Traditionally, all machine learning libraries are extremely bulky and very implemented in Python, often face performance limitations when applied to large-scale, real-time applications in C++. micrograd++ addresses these challenges by offering a pure C++ implementation that ensures those new to machine learning concepts, while also pro- viding the necessary tools for advanced applications. III. DISCUSSION A. Technical Description micrograd++ is built using modern C++ standards (C++17)

▶ Method calls not conserved ▶ Timing is critical to correctness yet can only be estimated by applications ▶ Intermediate system state is opaque (i.e. denotational, not small step operational) ▶ Correctness concurrent system models prompting the search for reductions ▶ Growth of distributed software applications: blockchain, distributed file systems, network apps Design and Implementation of Highly Scalable correctness, independent of sequential history ▶ Timing and semantics in the type system available to applications ▶ Defined operationally on system state, not by a combinatorial search of the results ▶ Compositional*