CppCon 2026

Floating point has a reputation for betrayal. Change the thread count, vector width, compiler flags, reduction tree, or target architecture, and the low bits can move. Parallel algorithms make this worse: the standard often specifies the operation, but not the numerical expression whose result must be reproduced. This talk asks a provocative question: what if reproducible numerics did not have to be slow?

We will show reproducible, deterministic implementations of reduce and scan that exhibit better error behavior on hostile floating-point workloads and can match or beat conventional standard-library implementations on realistic workloads. The trick is not to freeze the execution schedule. It is to specify the expression being computed, then let the implementation use SIMD, threading, blocking, tiling, and platform-specific strategies to compute that expression efficiently.

The key idea, developed through C++ standardization work such as P4016R0 and P4229R0, is reproducibility by reproducing the computation. Instead of asking the implementation to promise a particular schedule, we give the calculation a named expression. Once that expression is chosen, changing the thread count, vector width, chunking, or blocking strategy does not silently change the answer.

A reproducible scan makes this harder than reduce because it does not expose only one final value. It exposes every prefix. A reproducible final sum is not enough if the intermediate results still drift. We will show how expression and observation contracts make those prefixes reproducible without forcing the computation back into a slow sequential order.

Then we go below the algorithm layer, to the places where bits usually escape: FMA contraction, denormals, floating-point environment choices, math-library approximations, and vectorized transcendental functions. The goal is not to get the same answer by turning off the hardware. We will show reproducible vectorized primitives, including transcendental functions, running at speeds comparable to established vector math libraries while preserving a cross-platform numerical contract.

Finally, we put the whole stack under stress: a heterogeneous numerical pipeline across x86-64, Apple Silicon, and CUDA. The data is deliberately hostile, with high cancellation rates and fragile intermediate states. The aim is not to pass friendly benchmark cases, but to reproduce the specified computation, including the same intermediate failures, not just the same final answer, bit for bit, across CPUs, GPUs, and toolchains.

Some of the most expensive C++ bugs are not caused by obscure language features. Instead, they emerge from code that looks reasonable: shared ownership that quietly extends lifetimes, singletons that become invisible dependencies, and performance-driven decisions that harden into architecture.

This talk examines these common design-level failure patterns in large-scale C++ systems. We cover three concrete design shifts:

Lifetimes: Shifting from shared ownership to explicit lifetime boundaries. Coupling: Shifting from implicit global coupling to injected dependencies. Interfaces: Replacing permissive APIs with constrained interfaces using std::span, std::optional, std::variant, and strong types.

Each shift is presented with the failure pattern it addresses, the solution, and the design rule it yields. Attendees will leave with practical heuristics for designing systems that are easier to reason about, test, and evolve: all grounded in real-world failures and the redesigns that fixed them.

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.

For C++ developers building modular applications or performance-critical loops, modern architecture often forces a painful compromise: you either build heavily decoupled systems that thrash the CPU cache, or you write rigid, tightly coupled code. Distributed builds mask the compile-time symptom — but the underlying coupling bleeds into the runtime hot path.

This talk presents the Capability Routing Grid (CRG), an architecture that refuses this compromise. CRG enables a zero-registry plugin system where modules self-register at link time, and polymorphic dispatch is reduced to an O(1) branchless array lookup — with no central registry, no Init() function, and no runtime search.

Three independent pillars, each usable standalone:

Pillar 1 — Linker-Driven Discovery: Build a fully decoupled plugin system without central registries or Init() boilerplate. Modules self-register via standard C++ static initialization — entirely automatic in monolithic builds, and requiring a single explicit sync-point call at DLL load time.

Pillar 2 — State and Behavior Separation: Enforce a strict architectural boundary between pure data structs and stateless capability objects. This separation — not a framework — is what keeps the hot path flat. Type erasure is available as an optional cold-path utility for cross-boundary routing, but is never required for performance.

Pillar 3 — O(1) Branchless Dispatch: Map multi-dimensional contextual states into a single flat lookup table using basic polynomial math. Because this layout never changes, the CPU branch predictor and hardware prefetcher maintain peak efficiency.

The final payoff: by collapsing capabilities into raw function pointers, the system hits the memory bandwidth ceiling — 33 GiB/s sustained throughput, with a per-dispatch tax of approximately 1.5 nanoseconds.

Data-Oriented Design is defined from scratch. A brief hardware cache primer precedes every performance claim. The entire architecture compiles on C++17 — no language extensions, no experimental flags, on any mainstream toolchain. The audience will leave thinking, "I could have written this" — because they can.

Attendees will learn how to: - Build self-registering plugins with zero shared headers, using standard static initialization across both monolithic and DLL builds - Apply state/behavior separation as an architectural discipline — keeping capabilities stateless and the hot path free of virtual overhead - Replace vtable dispatch with a flat array lookup across N behavioral dimensions — O(1) regardless of dimensionality - Cache resolved logic as raw function pointers and call them directly, reaching memory-bound throughput

Application developers sometimes hit a limitation of a 3rd-party library or framework. The Type Erasure design pattern can help us overcome such limitations without the need to change the 3rd-party API.

Type Erasure is a relatively complex design pattern that allows us to treat a set of unrelated classes as if they shared a common base class, while preserving value semantics. The downside is increased code bloat and code complexity as the pattern requires a significant amount of additional code.

There have been quite a few talks explaining HOW to implement Type Erasure, while the topic of WHEN has been rarely discussed. Should we always use type erasure instead of virtual polymorphism? And if not, then what are the criteria?

This talk will show a concrete example from the audio programming industry of how Type Erasure allowed adding new functionalities to the parameter class system of the JUCE C++ framework without changing its API. As such, the talk will be useful for application and library developers who use 3rd party libraries but need an extra degree of flexibility.

You will come out of the talk understanding

• what Type Erasure is,
• when to use it, and
• how to implement it.

You don't need to understand Type Erasure, audio development, or JUCE to attend the talk; the necessary minimum will be explained during the talk.

Large-scale robotics organizations operate diverse fleets of manipulators — 4-DOF arms on rails, 6-DOF standard manipulators on pedestals and fully mobile manipulators — each with different kinematics, hardware vendors, and software teams and researchers.

And engineers and researchers are opinionated.

As a team eventually you have one goal. Re-use as many abstractions; ideas and algorithms as you can while still allowing you the flexibility to explore new algorithms; ideas and approaches.

This talk presents a layered C++ architecture that solves this at scale. We present our core abstrcactions; a header-only Lie group library provides shared spatial math types (SE(3) poses, twists, wrenches); a trajectory library for motion planners and controllers; and a collision detection library from the same.

We will discuss how we moved from virtual functions to C++ concepts with zero overhead and the learnings along the way that allow us to write planning and control algorithms for various robots morphologies and sensing paradigms.

Critically, this architecture balances the needs of production at scale while enabling experimentation: researchers can prototype new collision algorithms, try a new physics engine, or test a novel trajectory representation — all without disrupting production code. The backend boundaries are where new ideas enter the system. You'll leave with three distinct C++ patterns for backend abstraction — compile-time traits, runtime interfaces, and type erasure — and an understanding of when to use each based on performance requirements and the need for experimentation.

According to VGInsights, about 10% of games released on Steam in 2024 used a custom engine, but those games represent 41% of units sold. Most "how an engine works" talks come from the AAA end of that distribution. This one comes from the other end of that distribution: a teaching-scale engine, where every system has to be small enough to understand from first principles. The problems are the same as in a big engine, but the smaller surface is what makes the architecture legible.

In this talk, we'll walk the spine of the engine in modern C++: allocators and handles in the core, the job system, the graphics abstraction question, the asset pipeline split between pipeline-time and runtime, and scene representation including ECS. We finish on serialization, where C++26 reflection (P2996) offers a path past today's reliance on macros and external code generators.

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.

Applications that present rapidly changing market data to human users face a practical challenge: updates may arrive thousands of times per second, but humans only benefit from periodic refreshes. Efficiently coalescing and scheduling that work without overwhelming CPU resources becomes a concurrency problem rather than simply a rendering problem.

This talk demonstrates how modern C++ atomics can be used to build a lock-free timer scheduler optimized for extremely high update rates. The scheduler follows a single-producer, multiple-consumer design in which work items represent recurring tasks that must execute periodically. Consumers process scheduled work and re-schedule it for future execution without relying on traditional locks or centralized coordination.

The most unusual aspect of the design is its “reverse work stealing” behavior: consumers can proactively give away work to other consumers and become idle themselves. Rather than distributing work evenly across all threads, the scheduler attempts to pack work onto as few cores as possible, reducing idle spinning, lowering CPU utilization, and improving cache locality. Because tasks may have highly variable execution times, the scheduler must also prevent pathological cases where work is endlessly redistributed between consumers.

Attendees will learn how atomics, lock-free forward lists, and memory ordering can be combined to implement high-throughput schedulers with predictable latency characteristics. The talk also discusses practical tradeoffs, implementation challenges, and performance measurements from production-inspired workloads, including producer throughput of approximately 50 million scheduled tasks per second and consumer processing throughput approaching 700 million tasks per second, while keeping migrated work items below 1% in typical workloads.

Your system has interchangeable strategies chosen at runtime, but the hot path runs millions of times per second. Runtime polymorphism has virtual dispatch overhead. Templates give you performance but lock you at compile time.

This workshop shows how to leverage inheritance for extensibility while avoiding vtable overhead, using a layered architecture of compile-time patterns. We'll build them up through OpenJDK's GC barrier system, where different GC algorithms compose different barriers on one of the hottest paths in the JVM.

We will also discuss trade-offs against alternatives like std::variant, function pointers, and plain virtual dispatch throughout. No JVM knowledge required.

C++20 coroutines come with a reputation for complexity — and writing your own coroutine return type deserves that reputation. But here is the good news: you probably don't have to. With std::generator in C++23, high-quality libraries like Asio and cppcoro today, and std::task on its way in C++26, most developers can simply write co_await and move on.

There is one gap, however. Sooner or later, every developer using coroutines needs to bridge an existing asynchronous interface — a timer, an I/O operation, a thread pool callback, a legacy future — into the coroutine world. That bridge is built with awaiters and awaitables, and that is exactly what this session teaches.

We will start from first principles: what does co await actually do? We will demystify the three functions that form the awaiter protocol — await ready, await suspend, and await resume — and build a clear mental model for each. We will implement a real awaiter from scratch, then explore how symmetric control transfer keeps the call stack flat across deeply chained async operations. Next, we will learn how to make any existing type co await-able without modifying it, using operator co await, and how await transform lets you restrict or adapt awaitable behavior to a specific coroutine context. Finally, we will look at how to write cancellation-aware awaiters using std::stop token, so your coroutines respond promptly when work is no longer needed.

No prior coroutines experience is assumed. If you know what co await looks like on the surface but not what happens underneath it, this session is for you. You will leave with both the knowledge and the code to wrap any asynchronous operation into a first-class co await expression.

Range views enable a “no raw loops” style of programming — not as a matter of taste, but as a pragmatic way to reuse well-tested algorithms to write code faster, express intent clearly, and avoid common bugs. In practice, however, developers often run into issues that may lead them to abandon ranges altogether:

• unintuitive behavior and surprising limitations
• slower compilation and runtime performance compared to raw loops
• high complexity when implementing custom views

In this talk, we’ll distill the core design choices behind these problems — and the alternatives that avoid them. In particular, we’ll compare:

• iterators and indices
• external and internal iteration
• transformations of nested views that overcome limitations of internal iteration

Attendees will leave with practical insights for designing and using range abstractions, along with examples of libraries that embody these ideas.

What makes an async workflow readable? Not just linear control flow — you also need to see where errors go, what each step depends on, and how failures compose. This talk takes a single production workflow — multiple async RPC calls with conditional branching, parallel fan-out, and partial-failure handling — and shows it implemented in three paradigms: callbacks with thread pools, a workflow orchestration graph over futures, and coroutines with std::expected. We evaluate each through four questions: where does the control flow live, the error flow, the coupling, and the migration risk?

Coroutines restore linear control flow, but without structured error composition, the workflow drowns in manual failure checks at every step, that is more error-handling code than business logic. Task

Sean Parent's "no raw pointers" guideline is often misunderstood as "avoid T* , use smart pointers instead", even though Sean explicitly included smart pointers in his definition of a raw pointer: "anything with implied ownership and reference semantics".

This talk presents a refinement of that guideline:

Pointers - smart or not - shall not appear in function signatures, neither as argument nor as return type.

The focus of this talk is how to implement classes as regular types while still supporting dynamic allocation, polymorphism, and sharing internally.

It shows how such value types can be implemented manually, without dedicated library support. It then follows the evolution of the language and library in gradually simplifying these abstractions: C++11 smart pointers ( unique_ptr and shared_ptr ), C++26 vocabulary types ( indirect and polymorphic ), as well as in-progress proposals ( copy_on_write and protocol ) are presented as tools to simplify writing safe abstractions, not as safe abstractions themselves.

Along the way, the talk revisits the Rules of 3/5/0, exception safety guidelines, const propagation, aliasing, and local reasoning.

The talk aims to make you stop passing pointers around.

Python's Global Interpreter Lock, which determines which single thread can execute native Python code and call C API functions, simplifies writing multithreaded code. By default, the most popular C++ bindings to this API, pybind11 and Cython, implicitly enables the GIL by default. However, sticking with this execution model leaves out extra performance afforded by modern multicore CPUs with hyperthreading, as automatic locking and unlocking of the GIL does not scale well with thread counts, especially in performance-sensitive workloads.

The newfangled free-threaded interpreter promises salvation when running either pure Python code or with compiled extensions. General multithreading rules apply (prefer thread-local variables, using locks to prevent simultaneous access of shared data), but when dealing with projects containing compiled extensions that directly or indirectly interface with Python's C API, more porting rules also apply.

Key porting tips, including projects using the Limited API, include: port native code away from C API functions that avoid borrowed references because they aren't thread-safe; modify unit tests to catch concurrency bugs arising from assuming the presence of the GIL; and extend CI coverage of Python interpreters both for testing and to build free-threaded compatible wheels.

In C++, monadic operations are a commonly utilized API for interacting with result types like std::optional and std::expected. By chaining the computations, these abstractions allow you to manipulate the underlying values using less boilerplate code. Monadic operations are a powerful tool suitable for a variety of usage scenarios. They enhance code robustness and significantly lower the risk of accidental errors.

Despite its benefits, many users consider the syntax of monadic operations to be overly verbose, and the underlying concept often seems complicated. There are also the cases where the restrictions placed on functions require using extra statements, specific return and arguments types, and reduce code readability. However, are there other options available? Do we have promising alternatives? The short answer is: yes. While C++ does not yet include pattern matching as a native language feature, the standard library still offers tools for achieving many useful pattern-matching-like functionalities. We will explore how to apply std::visit to this problem and evaluate if an abstraction, which we might call std::match, could serve as an effective substitute for monadic operations in various situations.

Based on several years of practical production experience, this talk will explore the use of monadic result type interfaces and comparable alternatives. This information will benefit practicing software engineers looking to deepen their understanding of these features and integrate them into their codebases.

Every Boost library that graduated to the C++ Standard became invisible. shared_ptr doesn't say "Boost" anywhere in . 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. Beman Dawes. Howard Hinnant. Marshall Clow. 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 — not as a benchmark, but as a character study. Joaquín López Muñoz sat down, studied SIMD probing, and built something faster than a team at Google. We won't just show a bar chart; we'll show the design decisions — the one that made it fast and the one that almost kept it from shipping. Boost.Intrusive — presented as a magic trick before an explanation. One object. Three containers. Zero allocations. We'll let the audience sit in "wait, that can't work" for ten seconds before we reveal the mechanism. 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 cover Boost.JSON, Boost.URL, Boost.Scope, Boost.Cobalt, and the design thinking behind each.

Networking gets its own act. Beast, Corosio, Capy — presented as a list, they're alphabet soup. Presented as what they are, they become 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, and how this connects to active WG21 proposals for C++29.

We close not with a mailing list URL but with a name. One person — a contributor who submitted their first Boost PR this year, went through review, and is now preparing a library for formal consideration. 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.

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.

C++20 modules are finally usable end to end, from your own modules to 'import std;' The functional build-system support with real diagnostics. This talk teaches modules the way #include was once taught: with small files, a build, and concrete use cases. Modules stopped being experimental somewhere around 2025. This session builds the mental model from scratch: what a primary module interface unit is, how partitions and implementation units fit together, and, crucially, how import differs from #include in ways that matter day to day. It uses a small library, exposes it as a module, splits it across partitions, consumes import std; and observes the compile-time effect. Then it answers the questions every team hits in week one: how modules interact with macros, with templates in headers, with header-only dependencies, and with a mixed codebase that can't convert everything at once. The examples will be using CMake, clang and recent gcc. You will leave able to structure a small library as a module, explain why a macro didn't cross a module boundary, and plan an incremental adoption that doesn't require converting the whole tree.

Production C++ code often hides small but deeply complex algorithms inside layers of engineering: APIs, lifetimes, error handling, integration, logging, and glue code. We test classes and systems, but rarely the algorithm itself in isolation.

Off-by-one errors and broken or missing invariants can survive extensive pre-production testing. Line coverage does not imply branch coverage. Branch coverage does not imply coverage of algorithmic corner cases. Testing through a wide public API often obscures the mathematical structure of the problem, making subtle bugs difficult to discover through multiple layers of abstraction.

This talk explores how to extract an "algorithmic core" from a larger component. Its correctness is fundamentally mathematical and largely language-agnostic rather than C++-specific.

Using examples such as substring matching, topological sorting variations, and lazy evaluation on trees, we will examine how problem corner cases differ from implementation corner cases, and how easily some of them are skipped.

We will discuss:

• Recognizing when an algorithm is entangled with boilerplate
• Extracting core logic without introducing accidental complexity
• What it takes to test algorithmic code in isolation
• Raising the level of abstraction to make reasoning easier without merely relocating complexity
• Using LLMs to assist in exploring and validating implementations
• Gradual rollout and comparison of competing implementations

This talk takes you through the research and optimization of radix sort, culminating in its integration into libc++.

You will learn : common pitfalls of radix sort implementations (non-binary digits, dynamic memory allocation, fragile interfaces), how to design a strict and high-performance interface (fixed digit size, external buffer, projection to integers), and how to overcome radix sort's main drawback — its lack of naturalness — with two counter-based optimizations and a hybrid merge scheme.

The result : on arrays of 60 int32 elements (18 for int8), radix sort starts beating std::sort ; on large arrays, it's up to 10x faster. Floating-point types (float/double/float16_t) are also supported via IEEE 754 bit transformations (sign and exponent inversion for negative numbers).

The key takeaway : in modern libc++, for integers and floats, std::stable_sort on random data is noticeably faster than std::sort . Life has become better, but also more complicated — choose your sorting algorithm wisely.

Years ago while working on a service that would be deployed in Kubernetes, we decided to use the simplest possible TCP based healthcheck. Not because TCP was the right choice, but because implementing a simple HTTP endpoint was more code than it was worth. For our Go services the equivalent HTTP endpoint was only a couple of lines. In fact, we eventually ended up replacing our TCP endpoint with a small Go wrapper around our C++ service.

The Go standard library has done a great job at designing a very ergonomic HTTP library. C++ now has all the right building blocks to express the same design concepts: coroutines, concepts, std::expected, std::jthread. The question is, do these patterns transfer from Go to C++? How do we evaluate the effectiveness of the resulting design?

In this talk I will present a small HTTP library inspired by Go's net/http, built with the tools available in C++23. To evaluate the resulting design I will draw on ideas from Felienne Hermans' The Programmer's Brain and the principle, from Structure and Interpretation of Computer Programs, that API design is language design.

Along the way we'll see which patterns transfer cleanly, which need translation, and why telling the difference is a skill worth developing even if you only work with C++.

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.

How to Choose and Use the Right Container in Modern C++: Part 1, Classic Containers

Choosing the right container and using it correctly can have a profound impact on the performance of a program, but doing so is harder than you might think. In Part 1 we will survey the containers and adaptors in the C++17 Standard Library, and discuss how to choose the best tool for the job and extract the best performance from it. We will consider the abstractions they model and the practical limitations they impose, and find that choosing between them requires an understanding not only of the speed and size tradeoffs of each container, but also of the difference between algorithmic complexity and actual behavior. And we will flag aspects of the standard containers that, without this understanding, can lead you to the wrong choice.

How to Choose and Use the Right Container in Modern C++: Part 2, New and Future Containers

Choosing the right container and using it correctly can have a profound impact on the performance of a program, but doing so is harder than you might think. In Part 2 we will survey the new containers and adaptors introduced to the Standard Library in C++23/26, and discuss how to choose the best tool for the job and extract the best performance from it. We will consider the abstractions they model and the practical limitations they impose, and find that choosing between them requires an understanding not only of the speed and size tradeoffs of each container, but also of the difference between algorithmic complexity and actual behavior. And we will examine a proposal for a future container that diverges from the historical STL design principles and ask: what makes for a good container design?

To navigate safely, a robot must simultaneously ingest high-frequency sensor data, compute complex spatial algorithms, and publish control commands. Historically, frameworks like ROS 2 have handled this via asynchronous callbacks. However, as autonomous stacks scale, relying heavily on unstructured callbacks can obscure control flow, making state synchronization difficult and robust task cancellation notoriously complex.

This talk explores how to transform unstructured, callback-based architectures in a robotics navigation stack into declarative pipelines using structured concurrency (specifically, C++ Senders and Receivers via stdexec). Attendees will dive into two real-world architectural examples: a collision monitor and a waypoint follower, showcasing readable logic pipelines, thread-hopping between an event loop and a static thread pool, and clean task cancellation.

Why does 0.1 + 0.2 not equal 0.3? Because binary floating-point cannot exactly represent most decimal fractions, the value 0.1 simply does not exist in IEEE 754 binary. For applications where rounding errors are unacceptable, finance, billing, regulatory reporting, scientific data interchange, this is a structural problem, not a precision setting that can be tuned away. IEEE 754-2008 introduced a decimal floating-point alternative that stores the significand in base 10, and ISO/IEC TR 24733 sketched a C++ binding for it. Compiler support, however, has remained uneven across vendors and architectures.

Boost.Decimal is a header-only, dependency-free, C++14 implementation of IEEE 754-2008 and TR 24733 decimal floating-point. It provides three IEEE-conformant types, decimal32 t, decimal64 t, and decimal128 t, and three companion decimal fast* t types that trade strict bit-layout conformance for speed where you don't need on-the-wire interoperability. All six types behave like built-in floating-point: they're constexpr-friendly throughout, support mixed arithmetic and promotion, and ship with their own implementations of , , , , , hashing, , and Boost.Math integration. The library is tested natively on x86 64, ARM64, and s390x, and under emulation on PPC64LE and ARM Cortex-M.

This talk is the introduction to decimal floating-point that most C++ programmers never got. We'll cover what decimal floating-point actually is at the bit level (BID vs. DPD encodings, the cohort concept that has no analogue in binary), why the standard library's defaults are what they are (e.g. Rounding), and how Boost.Decimal's API maps onto familiar and patterns. We'll work through worked examples, parsing a price feed, computing financial summary statistics through Boost.Math, round-tripping values through while preserving cohort information, and walk through the library's deliberate deviations from both IEEE 754 and the C++ standard, including why floating-point exception flags were sacrificed to keep constexpr and how from_chars was extended to distinguish overflow from underflow as well as preserve cohorts. We'll close with reviews of the benchmarks versus binary floating point, as well as other existing libraries.

By the end, attendees will know when reaching for decimal is the right call, which of the six types fits their workload, and what trade-offs the library made on their behalf.

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.

Every mature codebase carries history and technical debt. The challenge is modernizing without stopping the world or introducing big re-write failure risks.

In this talk, we’ll explore how to modernize legacy C++ codebases incrementally using a mix of deterministic code transformations and AI-assisted refactoring .

After delving into some of the common problems with legacy modernization, we'll start tackling the simple "boring" stuff that deliver huge leverage of changes via clang tooling . These represent repeatable, deterministic reviewable upgrades that can be automated and applied at scale.

Then we can look at more difficult transformations that can be AI assisted with more aggressive transformations including API improvements and how to prevent it going off the rails. This will be backed by Compiler diagnostics, static analysis and testing to keep changes maintainable and correct .

The goal is not to replace engineering judgment, but to accelerate it: turning modernization into a continuous, low-risk workflow instead of a disruptive project.

Attendees will leave with a repeatable playbook for modernizing legacy codebases incrementally, safely, and at scale—using the right tool for each class of change

The software in most embedded projects written in 2026 looks like the software in embedded projects written in 1996. This isn't because 1996 was the peak of innovation and quality but rather has more to do with biases and FUD. Unfortunately, much of the world suffers from these choices: users, developers, managers, and shareholders.

There are better ways to write firmware for embedded systems; proven techniques and implementation strategies that I will describe in this talk. We will explore techniques based on abstraction and composition that maximize understanding while reducing the codegen footprint. We will apply these strategies at all levels of the firmware stack: interrupts, register manipulation, peripheral communications, and the application glue.

Join me and leave with open-source libraries, an open-source starter project, and techniques that you can apply to your greenfield and existing projects.