CppCon 2026

Move semantics and perfect forwarding, introduced in C++11, are quite impactful features that still remain a source of confusion even for some experienced developers. This talk builds understanding from the ground up: what problem move semantics solves, how rvalue references enable it, and why the rules work the way they do.

We start with the cost of unnecessary copies and how move constructors and move assignment operators let us transfer resources instead of duplicating them. We then look at how std::move doesn't actually move anything — and what it really does. From there we tackle the forwarding problem: why writing a single function that preserves the value category of its arguments is necessary, how forwarding references (also known as "universal reference" in the past) solve it, and what std::forward does under the hood.

Along the way we cover the practical details that trip people up: reference collapsing rules, the interaction between overload resolution and reference binding, when the compiler generates move operations and when it doesn't, and the cases where std::move can actually pessimize your code. We also discuss trade-offs in parameter passing — by value, by const reference, or by forwarding reference — so attendees should have a clearer model for making these decisions in their own code.

At the end we will look into std::forward_like and explore how it is different from std::forward .

No prior knowledge of rvalue references is assumed. Familiarity with copy constructors, destructors, and basic templates is helpful.

C++ templates are one of the language’s most powerful yet often misunderstood features. This talk walks the audience through the entire template landscape, starting with the basic syntax and definition, moving through function and class templates, and culminating in advanced techniques.

Attendees will learn how template argument deduction works, why implicit requirements matter, and how function overloading can replace error-prone macro tricks. Real-world examples, including a flexible register abstraction used in production code, show how generic programming can deliver type-safe, high-performance solutions without sacrificing readability and performance (zero cost abstraction).

By the end of the session, participants will feel confident writing their own generic components, understand the trade-offs of different template features, and have a toolbox of best-practice patterns they can apply immediately to their projects.

Memory leaks are very hard to treat. We have reliable tools like valgrind or AddressSanitizer to tell us whether or not an application has leaked memory, but current tools cannot tell us where the leak happens (they can tell us where the allocation happened,which is not the same).

I show that it is possible to run an analysis that is equivalent to running a garbage detection pass after every instruction and use that to find the actual cause of memory leaks in open source applications.

Using a garbage detection algorithm on record-and-replay recording, one can overcome the most obvious obstacle: stopping a program a program after every instruction or running garbage detection algorithms is very slow, but we can effectively run a bisection search on the recorded timeline to avoid having to evaluate after every instruction.

The goal is attribution to source code. Are we limited to reporting the instruction that overwrites the last reference to allocated memory or can we actually diagnose a specific error for a concrete function?

Tooling Track sessions are sponsored by Optiver.

Scripting in a C++ game engine should not cost you the engine's native performance. This talk shows how C++26 static reflection and std::meta::substitute in particular can lift a scripting language's bytecode into C++ template structures that the compiler optimizes away entirely, collapsing the interpreter dispatch loop into the same machine code you'd write by hand. Scripting languages like AngelScript and Lua are invaluable in C++ engines: they give designers a fast iteration loop without rebuilding the engine. But they come with a paradox. The engine you chose for raw performance now spends cycles on every frame interpreting a slower language, juggling a software stack and checking types at runtime. We will walk through the technique step by step, starting from a plain bytecode interpreter and ending at a fully reflected program where a scripted sum(0..10) compiles to mov eax, 45; ret. Along the way you'll learn the core C++26 reflection primitives (^^, [: :], std::meta::substitute), how to assemble bytecode into structural templates like block<> and loop<>, and how these techniques generalize to embedding any stack or register based scripting language in your engine with zero runtime overhead.

Every Boost library that graduated to the C++ Standard became invisible. shared_ptr doesn't say "Boost" anywhere. Boost's greatest successes carry no signature, and that invisibility is killing recruitment. This talk is not a status report. It is an answer to the question: why would I give my best work to a project that erases my name?

We begin with the people. Peter Dimov. Howard Hinnant. Marshall Clow. Beman Dawes. These engineers didn't just write libraries, they defined what production-quality C++ means. We will say their names, show their faces, and then ask the only question that matters for Boost's future: who replaces them? Every technical detail in this talk serves that question.

The answer starts with the peer review process, not as a quality gate, but as an apprenticeship. Boost review doesn't just catch bugs; it forges people. It is the mechanism by which a competent developer becomes a Dimov-class designer, and we will show how that pipeline works today.

Then the technical core. Boost.Unordered's unordered flat map. Joaquín López Muñoz sat down, studied SIMD probing, and built something faster than a team at Google. Boost.Intrusive — presented as a magic trick before an explanation. One object, three containers, and zero extra allocations. Boost.Decimal, IEEE 754 decimal floating-point that feels like a native type, with portable performance rivaling GCC's non-portable _Decimal extensions. We will also survey other recent additions.

Networking gets its own act. Beast, Corosio, Capy are a statement of intent: Boost is building the networking layer the committee has been unable to standardize for fifteen years, and we believe we can ship it. We will show how buffer sequences, stream concepts, and type-erased I/O deliver ABI-stable networking compiled once against an abstract transport.

We spend some time with related C++ Alliance initiatives to make the ecosystem safer and more robust. And a quick mention of the upcoming Boost Documentary.

We close not with a mailing list URL but with a name. One person, a contributor who used Boost for a decade, then brought their own work, took the reviews, and was mentored. They now help maintain Boost. A person is a door; a URL is a wall. The thesis of this talk is not "Boost is alive." The thesis is that Boost is where you become the engineer you want to be. And every section, from the review process to the mentorship lineage to the fact that one person can build the fastest hash map in the industry, is proof.

Atomic memory ordering is often taught operationally: Use acquire here, release there, and perhaps add a fence “to be safe.” This approach tends to produce cargo-cult synchronization, where atomic operations are selected mechanically without a clear understanding of what information is actually being propagated between threads. In practice, memory ordering is not about memorizing enum values, but about establishing which facts become visible to which observers, and when.

This talk approaches atomic synchronization from first principles. Beginning with the fundamental ideas of publication, visibility, ownership transfer, and synchronization edges, the talk incrementally develops several concurrent structures drawn from real asynchronous systems. Case studies include outstanding-work reference counting, a multi-producer singly-linked publication structure, a concurrently-mutated doubly-linked intrusive list, and the coordination machinery underlying a concurrent operation with multiple concurrent completion modalities. Each structure is used to derive the synchronization requirements imposed by its invariants, rather than selecting memory orderings mechanically.

Along the way, the talk explores the practical meaning of relaxed operations, acquire/release synchronization, and atomic thread fences. Particular attention is paid to understanding what each actually does, when it is necessary, and when it has become a decorative synchronization cargo cult. The goal is not simply to present lock-free algorithms, but to develop a principled way of reasoning about memory visibility and synchronization in real concurrent systems.

Most bosses aren’t stupid, they just seem like it sometimes! This can be especially so when a great programmer first becomes a manager – suddenly the very attributes that made them good at their job become a problem. Whether you’ve just been ‘promoted’ to a management position, you hope to be some day, or you want to know how to deal with a difficult manager – it’s very useful to understand the world from a boss’s perspective. Not all leaders are bosses (and not all bosses are truly leaders): if you want to get something meaningful done then you’ll need to influence other people.

Whether or not you're a boss, leadership is important. To do meaningful/large things, we need to work with other people. Encouraging and persuading peers in the right way will make your and your peers' working lives better.

Lots of war stories (anonymised to protect the guilty) and tips and tricks, this talk is basically 'how to win friends and influence people' for programmers.

Practical HPC — Forcing the Compiler's Hand with Modern C++

Achieving near-peak FLOPs on modern CPUs requires exploiting SIMD, register files, and cache hierarchies — yet the levers that matter most (loop unrolling, vectorization width, tile sizes, register tiling) are largely outside the programmer's direct control. Autovectorization is fragile, #pragma unroll is advisory and non-portable, and the information the compiler needs most — loop bounds, problem shapes, working-set sizes — is typically only available at runtime. The result is a familiar gap between theoretical peak and delivered performance, bridged in practice only by hand-written intrinsics or inline assembly, fragile macro forests, or external code generators that sacrifice maintainability and type safety.

Modern C++ is, sometimes surprisingly, an excellent language for closing that gap without leaving the host language. We walk through the concrete features that make this possible and how to use them in practice — templates and constexpr to promote runtime values into compile-time constants, if constexpr and parameter packs for shape-specialized kernels, generic and template lambdas for loop bodies that are unrolled and inlined by construction, and concepts to turn silent performance cliffs into compile-time errors. Together, these give the programmer precise control over what the compiler emits: compile-time dispatch that specializes kernels per shape and target, guaranteed static unrolling that unlocks ILP and register tiling without relying on optimizer heuristics, and hardware-aware specialization parameterized on register count, SIMD width, and cache sizes.

Looking forward, C++26 and static reflection push this approach considerably further, turning today's disciplined metaprogramming into something closer to first-class, in-language code generation — and squarely aligning the language with the goal of making inline assembly unnecessary, even at the highest performance tiers.

These patterns are powerful but verbose and reappear across kernels, so we briefly introduce POET (Performance Optimized Excessive Templates), a header-only library usable from C++17 onward that packages the most common cases as ready-to-use building blocks, alongside xsimd for portable SIMD.

As a concrete case study, we walk through a real scientific-computing kernel — a workload of the kind that has historically demanded hand-tuned assembly — and show how these abstractions get within a small fraction of theoretical peak throughput while keeping the source readable, portable, and maintainable, with no inline assembly, no compiler builtins, and no external code-generation step. Scientific computing does not have to choose between performance and engineering quality, and with the right abstractions, development becomes dramatically more effective.

The architecture of legacy compilers presents limitations for modern software development. As codebases scale, developers face increased compilation times. While language complexity is often cited, key architectural bottlenecks include pointer-chasing across deeply nested ASTs (cache misses), single-threaded type resolution, and significant thread-lock contention (RwLock) during parallel semantic analysis. What happens when we discard the Abstract Syntax Tree entirely and apply strict Data-Oriented Design to the compiler itself?

In this session, we will explore the internal architecture of the Vx compiler frontend—a heterogeneous systems programming language to safely maximize utilization of available CPU cores. We will dissect how to translate complex, tree-like program semantics into flat, contiguous arrays of 256-bit bit-packed Global Identifiers (GIDs).

By stepping away from traditional recursive tree-walking and object-oriented compiler design, attendees will learn how to implement high-throughput parallel pipelines.

This talk is not just for language designers. The architectural patterns used to build the Vx compiler—flattening graphs into arrays, deferred identity, and lock-free synchronization boundaries—are directly applicable to any C++ developer building high-performance, multithreaded systems.

Making native code fast is a team sport: efficient algorithms, high-performance libraries, memory-conscious design, and modern hardware all play a role. But the critical link between your code and the hardware it runs on is the optimizing compiler.

This talk explores new & improved compiler optimizations in Visual Studio 2026, and how these improve the performance of existing C++ code with minimal source changes. We will examine both architecture-independent optimizations (such as improved scalar replacement, vectorization, and loop transformations) and architecture-specific enhancements (such as AVX-512 and NEON specific optimizations). Along the way, we will connect high-level C++ constructs to the generated assembly and CPU behavior to build an intuition for how performance improvements are realized. While the talk discusses Visual Studio code generation & optimizations, the tools and analysis apply to all native platforms & compilers.

We will also introduce sample-based profile-guided optimizations (SPGO) which uses the runtime characteristics of your application to guide optimization decisions, WITHOUT requiring traditional instrumentation. You will learn how SPGO works, how to apply it in practice, and where it delivers measurable gains: typically in the 5-15% range across real world native codebases.

Attendees will leave with a practical understanding of how to reason about native code performance, and how to leverage SPGO to turbocharge the performance of their code.

C++ Views were introduced with C++20 and extended with C++23 and C++26. They are lightweight objects that specify how to use (specific) elements of collections and their values. By composed into pipelines, programmers can easily define complex processing of collections of data.

However, the use of views also has pitfalls and traps. Therefore you should know about * The consequences of reference semantics * The problems of caching Views * The problems of using const with views * The Filter View Fiasco

This talk gives an overview about both the technology and where to pay special attention.

Time handling in C++ looks simple — but it has some caveats. Between system_clock , steady_clock , high_resolution_clock , and a few new friends from C++20, it’s easy to pick the wrong one and end up with flaky tests, wrong timestamps, or confusing results.

This talk demystifies how time works in C++. We’ll explore what a "clock" really is, how std::chrono models it, and why not all clocks tick the same way. You'll learn when to use each standard clock, how to reason about monotonicity and precision, and how to build your own custom or fake clocks to make testing reliable.

By the end, you'll not only understand the difference between wall time and steady time — you'll know how to use them confidently in your production and test code.

C++ gives us at least four different ways to pass "a thing that can be called" from one piece of code to another: function pointers, function objects, lambdas, and type-erased wrappers like std::function . Modern C++ has added more — generic lambdas, deducing-this lambdas, std::move_only_function , and a healthy ecosystem of concept-constrained callable parameters. Each of these has a job it is good at and several jobs it is bad at, but they are often used interchangeably until something breaks.

We will start with the basics: what a callable actually is, how function pointers, function objects, and lambdas relate to one another, and how the compiler thinks about each of them. We will look at lambda capture rules in detail — by-value, by-reference, init captures, dangling captures, and the cases where well-meaning developers reach for [=] or [&] and ship a bug — and we will see how generic lambdas (C++14) and deducing-this lambdas (C++23) extend the model without giving up clarity.

From there we will turn to the question of how to store and pass callables. We will compare function pointers, std::function , std::move_only_function (C++23), and concept-templated callable parameters along the axes that actually matter: ownership, allocation, move-only support, performance, and what the API tells the reader. Examples will be drawn from issues that have come up writing production code and mentoring developers — including the kind of subtle ABI and lifetime bugs that hide behind a perfectly reasonable-looking std::function<void()> parameter.

We will conclude with recommendations for which tool to reach for in which situation, with reference to the C++ Core Guidelines where useful. Attendees will leave with a clearer mental model of callables in modern C++ and a defensible default for the next time they write a function that takes one.

Teams writing modern C++ libraries with concepts, niebloids, deduction guides, and SFINAE-driven overload sets either accept wrong reference documentation or maintain a Doxygen + XSLT + Sphinx pipeline of workarounds. A different approach is to read C++ from the Clang/LLVM AST, separate corpus extraction from output templates, and recognize common idioms as first-class. This allows the source to stay clean from workarounds.

Doxygen-based pipelines all share these failures on modern C++. Constraint, noexcept, and explicit specifier expressions get truncated or dropped. Niebloids and function objects show as wrappers, not callables. Overloads distinguished by SFINAE or concepts scatter across the output, and CRTP base classes render with the wrong members. All of this disappears when the tool reads the compiler's AST and is able to instantiate specializations.

The talk then covers the architecture of MrDocs, an open-source documentation generator built on Clang/LLVM. Corpus extraction handles the redeclaration, template instantiation, and overload resolution problems that break naive AST traversal. Idiom recognition treats modern patterns as first-class metadata: niebloids, overload sets, concept constraints, requires-clauses preserved verbatim, function-object auto-detection, SFINAE overload sets, and deduction guides. It also captures the specifiers: explicit, noexcept, consteval, storage class, defaulted and deleted functions, and attributes. The templating layer gives full control over the output (HTML, AsciiDoc, Markdown, XML, or custom formats) without forking the tool.

The session is heavy on code from real C++ template libraries, with examples drawn from fmt, nlohmann/json, mp-units, and several Boost and Beman libraries. By the end, you will know why partial AST tools break on modern C++, what an alternative architecture looks like, and which idioms it has to recognize.

From our very first algorithms class, we are taught a simple rule: a better Big-O complexity means a faster algorithm. We spend years mastering asymptotic analysis, memorizing complexity tables, and losing sleep over worst-case scenarios. Big-O becomes a mental shortcut for “good” and “bad” code.

But the real world doesn’t run on whiteboards.

Modern performance is shaped far more by hardware realities than by asymptotic notation alone. CPUs have deep cache hierarchies, wide vector units, speculative execution, and memory systems that punish the “theoretically optimal” solution. GPUs thrive on massive parallelism where simple linear work can outperform asymptotically superior algorithms. Even on regular CPUs, cache-friendly linear scans often beat clever sub-linear approaches that fight memory latency.

In this talk, we will challenge the traditional Big-O mindset. We’ll look at classic algorithms through a modern lens and explore how hardware-aware designs cache-efficient layouts, SIMD/AVX vectorization, and parallel execution models can outperform algorithms with “worse” theoretical complexity. You’ll see why a higher Big-O algorithm can be faster, more scalable, and more predictable in practice.

The goal is not to dismiss Big-O, but to put it back in its proper place: as a tool, not a truth. By the end of this talk, you’ll think differently about performance and start writing code that works with the hardware, not against it.

C++ library maintainers are often in the dark as to everyone who actually depends on their code. Without that knowledge, deprecation decisions are guesses, the downstream impact of any API change is hard to predict, and the case for funding or continued investment is difficult to make rigorously. This talk demonstrates how mapping your downstream ecosystem changes the way you maintain and evolve a library, using dependent-audit, an open-source tool that discovers real-world dependents by searching public source code for #include directives and CMake integration files, and cross-referencing published academic citations. We'll show concrete examples of what this data reveals: where your true API surface lies, which users are candidates for upstream contribution, and how to make the case for your project's real-world significance. Attendees will leave with a lens for library stewardship built on data and ecosystem analysis

To write the best C++ application code, you must think like a library author. This talk reframes application development as a form of library design, where every component is a potential asset for future maintenance and extension.

We'll survey the C++ language features that empower library authors to create powerful, flexible, and safe abstractions. From there, we'll distill the 'library author mindset.’ You will leave with concrete principles and techniques to make your application code more modular, maintainable, composable, reusable, testable, and ultimately, more valuable.

As legacy C++ codebases evolve to meet new requirements, fix architectural issues, and address performance bottlenecks, refactoring becomes inevitable — but full rewrites are risky, costly, and rarely practical. This talk explores how modern C++ features — especially functional programming techniques and type erasure — can be used to refactor and extend legacy systems. We will present a concrete, low-risk, and measurable four-step technical framework for incremental modernization, by leveraging pure functions, discriminated union types, and type-erased callables to achieve safer, more flexible code with compile-time guarantees, even in traditionally object-oriented codebases.

The core of the talk will walk through the entire process on a concrete code example, illustrating (1) how to isolate existing logic, (2) define safer data models using immutable types, (3) deploy type erasure to create a robust, non-disruptive adapter boundary between old and new code, and (4) measurably execute the migration with safe testing practices. We will also share lessons learned from real-world experience and conclude by showing how these foundational patterns can be extended into future-thinking designs, leveraging examples with accepted C++26 features. Every step is grounded in real production constraints and trade-offs, and the techniques transfer directly to any domain where full rewrites are not an option. Attendees will walk away equipped with practical strategies to incrementally refactor your legacy systems without sacrificing stability or velocity.

C++ lifetime management is fundamentally built around synchronous scope exit. Constructors establish invariants, destructors release resources, and RAII permits ownership and cleanup to compose naturally with ordinary control flow. Asynchronous systems disrupt this model. Destruction may itself require asynchronous work, and “just launch another task in the destructor” quickly turns deterministic lifetime management into unstructured background activity.

This talk explores the implementation of async lifetime management in std::execution, based on the enter/exit scope sender framework proposed in P3955. Rather than treating async construction and destruction as special cases, the model reframes them as composable asynchronous protocols built around explicit async scope entry and exit operations. The talk follows the process of turning these ideas into working code, beginning from the low-level enter/exit sender abstractions and progressively assembling higher-level lifetime facilities on top. Along the way, the implementation uncovers an important self-similarity in the problem domain: Higher-level async lifetime facilities can themselves be expressed in terms of the same lower-level async lifetime primitives.

The implementation discussion focuses on the machinery required to make these guarantees real: Coordinating async teardown within structured concurrency, managing partially-entered scopes, and preserving deterministic cleanup semantics even when destruction itself becomes asynchronous. The resulting design serves both as a practical exploration of async lifetime management and as a case study in how implementing an abstraction can reveal deeper structural properties hiding inside the model itself.

In this talk we will explore "Concepts," a powerful feature for expressing template requirements added in C++20. Participants will learn how concepts improve code readability, provide clearer compiler diagnostics, and help enforce correct usage of templates and function overloading. Through simple, hands-on examples, we will explore the syntax, common use cases, and best practices for integrating concepts into modern C++ code. By the end of the session, attendees will understand how to write safer, more maintainable template code and leverage concepts to communicate intent clearly.