CppCon 2026

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++ 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.

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.

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<expected<T, E>> separates async completion from domain failure into orthogonal axes: errors short-circuit through monadic composition (and_then/or_else), and each step has a pure-functional signature that developers on the team can read, test, and extend. The combination‚ coroutines for structure, expected for error flow‚ is what makes the workflow genuinely readable.

Attendees will leave with: (1) the two-axis decomposition (async completion vs. domain outcome) and why separating them changes how you design every step, (2) a four-question framework for evaluating any async architecture, and (3) concrete patterns for partial-failure handling in coroutine fan-out.

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.

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.