CppCon 2026

What does it take to build a real-time embedded control system in modern C++, simulate its environment, command it from a browser, and watch it fly in 3D — all from the same open source ecosystem? This talk follows that pipeline end-to-end: from a C++23 real-time framework that schedules deterministic control loops across POSIX hosts and bare-metal microcontrollers, to GPU-accelerated simulation with CUDA, to web-based operations and telemetry, to live 3D visualization in Unreal Engine.

Attendees will see how a unified runtime architecture can span embedded control, simulation, diagnostics, operations, and visualization without fragmenting into separate software stacks. The talk explores deterministic scheduling, zero-allocation real-time design, cross-platform deployment, and integrating CUDA compute kernels directly into scheduled control loops without blocking execution. It also covers tooling for validating deterministic real-time behavior, along with techniques for streaming telemetry and sensor data between simulation and visualization layers in real time.

The presentation includes live demonstrations of a quadcopter simulation flying a programmed trajectory with lidar feedback, and a full-fidelity aircraft simulation with closed-loop autopilot, engine dynamics, and atmospheric turbulence — both running through the same real-time framework and rendered live in Unreal Engine. Whether you build flight software, robotics systems, industrial controllers, or simulation infrastructure, this talk presents practical architectural patterns for modern real-time systems in C++ that bridge embedded devices, GPU compute, and interactive visualization.

Embedded systems have to operate within real limits. We care about timing, memory usage, reliability, and what happens when something fails. But storage is often treated differently. We write a file, call an API, and assume the operation will finish when it finishes. That works for a lot of systems, but it becomes harder to accept when storage is part of a real-time workload.

That led me to a fairly simple question: what would change if storage had to be predictable too?

In this talk, I'll use an embedded filesystem I built in C++20 to explore that question. We'll start with the system requirements and work our way down into the filesystem, looking at where unpredictable work can come from and what design choices can make that work easier to understand and bound.

Instead of focusing only on average execution time, we'll look at what an operation actually has to do: searching for space, accessing metadata, reading and writing blocks, handling failures, and recovering from interrupted operations. We'll also look at how those costs change as the filesystem fills up or becomes fragmented. This kind of reasoning is familiar in real-time systems—we routinely think about bounded work in schedulers, queues, synchronization, and memory management. Storage deserves the same scrutiny.

The filesystem is intentionally constrained. It uses fixed resources and bounded searches where practical, explicit ownership, and a block-device interface that keeps the filesystem separate from the underlying storage hardware. I'll also show how fault injection and instrumentation can be used to exercise failure paths and measure the work being performed instead of relying only on timing measurements.

Modern C++ is useful here, but not because using C++ automatically makes a system deterministic. It gives us tools for making some of these design decisions explicit. We'll look at std::span, std::string_view, fixed-size containers, RAII, compile-time configuration, and small abstractions that can still make sense on a resource-constrained microcontroller.

There are tradeoffs. Fixed limits give up some flexibility. Simpler allocation strategies may use storage less efficiently. Recovery requires additional work and writes. In some systems those costs are worth paying for behavior that is easier to reason about. In others, a general-purpose filesystem is the better choice. We'll look at both sides.

We'll also separate the work performed by the filesystem from the timing behavior of the storage device itself. That gives us a way to take the same filesystem design and evaluate it across different storage backends and embedded targets. Rather than asking only, “How fast did this run?”, we can start asking, “How much work did the software perform, what did the hardware contribute, and did the system behave the way we expected?”

By the end of the talk, attendees should have a practical way to think about predictable storage in embedded C++ systems and, more broadly, how to reason about resource limits, failure handling, ownership, hardware interfaces, and timing when predictability matters more than peak performance.

Building on the recent work of improving the quality of Sea of Thieves' codebase by upgrading from C++14 to C++20, this talk will focus on the work that has went into enabling warnings as errors on the game, and more.

Rare will discuss the motivations behind wanting to crank up the warning level, and to flick the "warnings as errors" switch after 10 years of development in their multi-million line Unreal Engine code base.

What were the challenges? How much effort did it take? Was it worth it? Did we find any bugs? Did we stop at just "/W4 /WX"? What warnings did we find the most useful? What warnings were deemed unhelpful? All of these questions and probably more will be answered throughout this session.

Modern C++ development has embraced package managers like Conan and vcpkg for dependency management, gaining reproducibility and productivity. However, a significant weak point remains: the build toolchain. Unlike standard dependencies, the disparate set of tools comprising the compiler, linker, standard libraries, and OS-specific headers often remains outside this managed ecosystem. This gap leads to frequent complications, where misaligned versions between local environments and CI cause build errors and subtle ABI incompatibilities.

The operational cost of maintaining these toolchains is high. Updating a compiler version often necessitates the creation and deployment of new VM images or manual infrastructure updates, a process that can be even more complex if specialized environments like CUDA are involved. These hurdles directly impact engineering productivity and organizational alignment. Docker is a popular solution to isolate toolchains, but it does not support all the mainstream platforms.

This talk proposes a shift in perspective: treating the build toolchain as just another dependency. We will explore how leveraging toolchain isolation allows developers to express specific toolchain versions within their project manifests—using cross-platform tools like Conan and vcpkg to ensure strict repeatability across local developer environments and CI machines.By automating the fetching and configuration of the entire compiler suite, teams can ensure perfect alignment across all environments, simplify the upgrade path, and ultimately boost engineering velocity.

Tooling Track sessions are sponsored by Optiver.

Reverse-mode automatic differentiation (AD) computes exact gradients with respect to thousands of inputs in roughly the time of a single function evaluation. If you write numerical C++ for finance, scientific computing, or ML and you care about performance, this talk will show you where AD overhead actually comes from and what to do about it.

A straightforward operator-overloading implementation records every arithmetic operation onto a tape and replays it backward to propagate derivatives. On real numerical code this easily introduces 50-100x overhead, and most of that has nothing to do with calculus. It is heap allocation on every operation. It is cache misses walking the tape backward. It is branch mispredictions at memory chunk boundaries. It is recording tape entries for operations where none of the operands are even being differentiated.

We work through a series of C++ techniques that bring this down to around 4x on production code in finance. The two that matter most: a chunked arena allocator with cache-line alignment and branch-prediction hints that turns the recording hot path into a pointer bump and a store, and expression templates that collapse an entire right-hand side into one tape push with all derivatives accumulated in a compile-time-sized stack buffer. We pull up compiler output to show the abstraction really does compile away.

After the big two, we get into the smaller wins that are still worth knowing about: skipping zero adjoints during the backward sweep to exploit sparsity, picking derivative formulas at compile time that reuse the forward result instead of recomputing it, and demoting active-times-passive operations from binary to unary to halve their tape footprint. We also show how a should-record check propagated through the expression tree lets the tape skip entire statements when no operand is active, and how aggressive inlining lets the compiler eliminate these checks entirely for passive subexpressions, so the cost of asking is zero when the answer is no. We benchmark each one in isolation on four numerical workloads ranging from 8 to 161 sensitivities, so you can see what each technique is actually worth.

Recruiters Pri Trivedi and Brian Leone of Bloomberg will spend their lunch answering your career-related questions. Bring all of them.
Which college classes build a strong first CV? How should you handle a technical interview? How can you learn and grow by participating in and contributing to open source? What does a first job actually look like? Why is C++ still deeply valued, and where can your experience with it get you hired? What role will AI tools play in building a career that matters?
Bloomberg is hiring, and Pri and Brian will happily talk about that. However. this is a lunch, not a pitch, so the agenda will follow whatever the room asks - no question is too basic. Students are especially welcome to attend.

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.

The C++ Core Guidelines is quickly gaining popularity as the main C++ coding standard in the industry. The official introduction of the standard states the following: "Many of the rules are designed to be supported by an analysis tool. One way of thinking about these guidelines is as a specification for tools that happens to be readable by humans."

We took up this challenge: we built the tools, implemented a subset of the C++ Core Guidelines, and applied them to more than 8,000 industrial embedded software projects. That was not an easy undertaking, because the defined “rule enforcements” contain all kinds of caveats, shortcomings, unclarities, exceptions etc.

During this talk, we will explore the conceptual obstacles we encountered during implementation, how we have addressed them, and what remains to be done to make the C++ Core Guidelines the de facto standard in C++ programming.

Tooling Track sessions are sponsored by Optiver.

Clang is the C++ compiler used by hundreds of millions of developers every day, yet most of those developers have never looked inside it. The codebase can feel intimidating, with millions of lines of C++, an unfamiliar architecture, and a review process with its own customs. But once you know the map, contributing is surprisingly accessible.

This session is a live, code-first walkthrough of the full contribution cycle for Clang. Starting from a clone of the LLVM monorepo, we will configure a development build, locate the right subsystem for a real reported bug, write a regression test, fix the bug, and verify the fix. We will then extend that foundation to implement a small but complete language feature: adding a new diagnostic, walking through Sema, the AST, and the diagnostic engine as we go. Every step attendees see on screen can be replicated on their own laptops during and after the session.

Attendees will leave with a mental model of Clang's layered architecture (driver, frontend, Sema, CodeGen), a workflow for finding the code responsible for any given compiler behavior, and enough familiarity with the test infrastructure and review process to open their first pull request.

Tooling Track sessions are sponsored by Optiver.

Modern storage hardware has evolved at a breakneck pace. PCIe Gen 5 NVMe drives can push data at 10 GB/s, yet standard C++ file abstractions often leave them starving for data. Many developers think they need to abandon portability in favor of unmaintainable, OS-specific kernel bypasses to achieve the throughput necessary to saturate these drives. This session is for developers writing high-throughput, performance-critical applications who want to hit bare-metal speeds without sacrificing clean architecture, cross-platform support, or type safety.

Using a high-throughput cryptographic hashing engine as a concrete case study, this presentation demonstrates how to design a hardware-saturating data pipeline built entirely on the idioms of C++26. We will explore how the mathematical design of an algorithm - specifically BLAKE3's binary Merkle tree structure - can be mapped directly to wide SIMD vector lanes and concurrent CPU cores. We will walk through an execution model that completely bypasses the OS page cache, orchestrates memory without heap allocations on the hot path, and unifies OS kernel quirks in a portable way.

By the end of this presentation, you will learn how to replace rigid thread pools with lock-free, asynchronous execution graphs using Sender/Receiver paradigms (std::execution) and vectorization (std::simd). Crucially, we will focus on the build engineering required to make this work today. We will cover how to use advanced CMake tooling to safely compile multi-architecture vector binaries from a single source of truth, how to prevent LTO cross-contamination, and how to structure your pipeline today to seamlessly absorb upcoming C++ features in a world of trailing vendor toolchains.

C++ is used in many fields. One that sticks out is the embedded domain. You're often working with tight constraints. Writing software is challenging and fun at the same time.

What has C++ done to support this field? What new options do you have to avoid undefined behavior and write more efficient and robust code?

In this talk, I will present various library elements as well as language improvements that make writing embedded software better.

We'll look at real-world tasks like turning raw byte blobs into usable data structures, aka type punning, of course, without triggering undefined behavior. C++23 supports you in a new way with std::start_lifetime_as .

Transferring data via a network comes with its challenges. You have to care about the byte order. Oh, and how can you make sure that there are no padding bytes included? Well, I have an answer for you.

And then there is a brand-new feature in C++26: static reflection. I will present examples of how and where reflection in C++ eases embedded development.

By the end of this talk, you know the most important improvements in C++ for embedded or similar environments.

Today's world runs on C++. That's because, in domains requiring scale, performance, low latency, and fine-grained control over concurrency, C++ is second to none! Yet in recent years, a growing concern has emerged: C++ programs are often considered unsafe — not because of programmer negligence, but because the language itself provides insufficient mechanisms to prevent or reliably detect undefined behavior (UB).

For those financially and technically invested in C++, its current lack of language safety raises a fundamental question: How can we evolve ISO C++ to be UB-safe without necessarily sacrificing runtime performance, and without breaking backward compatibility with existing, valid C++ code?

In this talk, we begin by motivating an overall approach to UB-safety that starts from a simple but uncompromising premise: each individual potential source of UB must be able to be detected via runtime checking. Crucially, this approach eliminates the notion of "safe" and "unsafe" regions of a program — there is no place where UB can hide, and no need to switch languages, subsets, or tools to achieve comprehensive coverage.

We then examine how the C++26 contracts facility provides the essential foundation for transforming optional runtime checks into the practical, scalable mechanisms to eliminate UB bugs in production software. Contracts give developers fine-grained control over where and when checking occurs, allowing runtime overhead to be spent where it is affordable or most valuable — such as in new, rarely executed, or security-critical code. These decisions naturally evolve over time, as long-running checks that never fire may no longer justify their cost.

Finally, we outline the concrete steps of an incremental journey toward reducing undefined behavior in ISO C++. The result is a model of UB-safety — rooted in C++26 contracts and optional runtime checking — that is competitive with, and in key respects stronger than, approaches taken by other high-performance languages. Most importantly, this model applies not only to new code, but to the vast body of existing C++ software already in the wild.

Working with C++ code can be difficult, and it's made even more difficult when the code isn't your own. Everyone agrees that documentation is necessary when sharing a library. But, as a user of a library, the documentation may not help when you want to know the current state of your program. If you're a veteran C++ programmer then you may easily find your way around memory readouts, putting print statements in exactly the right spots, or reading "between the lines" of the documentation. Some of us are not very experienced, or we aren't able to recompile the code with print statements, or the documentation may not help with the problem.

Hopefully, the author had written a GDB extension to pretty-print their data types. But odds are, they didn't. As library writers, or as people writing classes for others to use and reuse, we can and should be writing debugger visualizers. We can empower our users by lowering the barrier to using the library successfully. Let's make our libraries more accessible. Software is about more than just the source code.

This talk will help you make the most of GDB extensions. After briefly discussing how to get started with GDB assuming zero prior knowledge, we will discuss how to write a GDB pretty-printer, how to set up your system to use the pretty-printer, how to embed it into your compiled binary, and how to distribute it with auto-loading scripts. By the end of the talk, the user will not only know the basics of writing a simple "to_string()", but will also understand how to effectively display containers and how to implement custom debugger-side user-facing extension methods. Let's get excited about debuggers!

There is no classical treatise on the design and implementation build systems; no faithful textbook every toolchain engineer has on their bookshelf. If there were we would need to throw it out anyway, because C++20 modules required a significant rethink of how build systems orchestrate compilation of C++ software.

It's been said over and over again that C++20 modules complicate the classic compile and link steps for C++. However, few have dived into how exactly build systems are dealing with this step-change in complexity. Talks will demo a few raw compiler invocations and say no more about the subject. This leaves engineers unequipped to deal with problems that arise in their builds; frustrated by opaque error messages and baffling build logs.

In this talk, we're going to fill in the rest of the blanks and explore exactly how build systems are tackling modules.

Many codebases contain several spots of type punning, and unfortunately a whole lot of those are incorrect and undefined behavior. While many current versions of compilers seem to do the correct thing, they might no longer do that tomorrow. Safety considerations wants to reduce/eliminate UB.

It might be worthwhile to inspect your reinterpret_cast constructs, most probably they are wrong. In this talk we will inspect what is wrong about those, we will learn about alignment, strict aliasing, object lifetime. 3 areas which might flag a red card on our type punning constructions.

Luckily the language evolved and gave us more tools to do it correctly, things like memcpy, memmove, bit cast, start lifetime_as, launder. It does however remain a dark corner and a dangerous territory to wander in. Because let's face it, zero copy is something we love in C++, and those bytes that came from the network, really are an array of integers, array of coordinates, ... Compiler, trust me, I know what I am doing. Am I?

Much of the public criticism of C++ safety comes from vulnerabilities rooted in C patterns: raw pointer arithmetic, unchecked buffer indices, manual memory management. Modern C++ offers real answers to those problems through RAII, smart pointers, containers, and type-safe abstractions. But C++ also has its own distinct safety issues that have nothing to do with its heritage. Temporary lifetime rules that silently create dangling references. Iterator invalidation semantics that differ across containers in ways even experienced developers get wrong. Smart pointer ownership pitfalls that RAII cannot prevent. Coroutine frames that outlive their captured locals. Implicit conversions that turn a safe string into a dangling view between one line and the next. These are purely C++ problems. They compile cleanly, look correct in code review, and ship to production.

A survey of public CVE reports and security-fix commits across prominent open-source C++ projects shows the same patterns recurring again and again. This talk walks through those patterns, in code that looks correct in review, compiles cleanly, and then breaks at runtime. The session is aimed at intermediate-to-experienced C++ developers who already know modern C++ but want a working catalog of the subtle patterns that ship past typical code review.

Recognizing these patterns has long been the domain of library authors and security researchers. This talk distills a catalog from real-world fixes for everyone else: not a prescriptive standard like the Core Guidelines, but a pattern-recognition toolkit any C++ developer can apply in their next code review. The session also makes the case for expanding the community-level safety conversation to give these C++-unique patterns the same attention as the C-heritage debates.

Every software engineer needs to understand the fundamentals of how your computer works. My mantra for teaching computer systems is 'no more magic', and in this talk I'll do my best to dispel what feels like 'magic' in our hardware in a single hour. In this talk, we will investigate '3' pictures of the 'the compilation process', 'program stack', and a 'cpu' to understand how these components work together to ultimately compile and execute code. An emphasis during this talk will focus on the 'CPU' and the hardware components (e.g. registers, cache memory, main memory, page table) interact with the operating system to efficiently execute a program. Hardware will be presented side-by-side with code so we can understand what happens in the hardware as software executes. Attendees will leave this talk with a better mental model of how their hardware works, and the implications their software design choices have on performance.

The C++ standard is moving more and more in the direction of safety. In the process, the committee is providing us with tools to help make our own code safer.

Many of these tools are specifically around numerics and none of them are enabled by default! To make matters worse, most C++ programmers don't even know these library features exist! Even if they did know, they wouldn't use them, because they are far too wordy.

We're going to do a survey of the relatively recently added new numeric safety related language features and see how they might come together in a real-ish project.

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.

Modern programming languages offer powerful tools for creating intuitive, expressive code, but few are as frequently debated as function and operator overloading. While these features allow developers to write code that mimics natural mathematical notation, such as A+B for complex numbers or vectors, they also introduce significant risks of ambiguity, hidden performance costs, and unintuitive behavior if misused.

This talk explores the balance between expressive power and code maintainability. We will dive into the mechanics of compile-time polymorphism, examining how compilers resolve overloaded calls and the strict rules governing arity, precedence, and associativity. Beyond the what and the how, we will talk about the when and the why with examples.

How far can modern C++ push an emulator before performance stops being a C++ problem and becomes a systems problem? This talk is the final part of a CppCon trilogy about building an extremely fast Game Boy emulator in modern C++, moving from high-performance emulation techniques to the limits imposed by hardware, compilers, and CPU architecture.

The target is intentionally absurd but not arbitrary: a Game Boy emulator running Tetris at roughly 100 times original speed, about 6,000 frames per second. However, the goal is not to support every Game Boy cartridge ever made. This project deliberately specializes for one iconic game first, using the code paths Tetris actually exercises as a guide, while keeping enough design discipline to test other games later. That means questioning the architecture of the emulator itself: instruction dispatch, memory access, generated code size, cache behavior, branch prediction, compile-time computation, benchmarking methodology, and the tension between accuracy, maintainability, flexibility, and raw throughput.

This is not a victory-lap performance talk. It is a measurement-driven engineering investigation. Rather than treating 6,000 FPS as just a headline number, this talk treats it as a stress test: a way to force difficult design decisions into the open. Attendees will leave with concrete lessons about performance-oriented C++: how to measure methodically, how to specialize without losing control, how to reason about modern CPU behavior, and how to decide when an optimization is worth its cost.