Pack Expansion in Lambda Captures  constexpr Changes  virtual functions  union, try/catch, dynamic_cast, typeid  allocations  constexpr string & vector  Concurrency Changes  Atomic Smart Pointers constexpr  constexpr virtual functions  constexpr functions can now:  use dynamic_cast() and typeid  do dynamic memory allocations, new / delete  contain try/catch blocks  But still cannot std::span  Can be dynamic-sized (run time) or fixed-sized (compile time)  Examples: int data[42]; span a {data}; // fixed-size: 42 ints span b {data}; // dynamic-size: 42 ints span

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Business 1 Business 2 Business 3 Business N … How to ensure load balancing of cluster nodes ？ Dynamic-Scheduler Node1 Node2 Kube-scheduler Pod Request Load Level Request Load Level Real Load Level requests but low load. Dynamic-Scheduler Node1 Node2 Kube-scheduler Pod Request Load Level Request Load Level Real Load Level Real Load Level Assigned to Node1 Dynamic-scheduler Node1 has a lower load Dynamic-Scheduler Node1 Node2 Dynamic-scheduler- node-annotator 5m Load Prometheus 1h Load 1d Load 5m Load 1h Load 1d Load telegraf Record to node annotation telegraf Dynamic-Scheduler

虚函数与纯虚函数 3. 拷贝如何作为虚函数 4. std::unique_ptr::release() 5. std::enable_shared_from_this 6. dynamic_cast 7. std::dynamic_pointer_cast 8. 运算符重载 9. 右值引用 && 10. std::shared_ptr 和 std::any • 只提供了关键字，详细信息请善用搜索引擎：

FlexClass Classes with dynamic size for everyone “C++ Flexible Array Members” Breno Guimarães brenorg@gmail.com Twitter @brenorg https://github.com/brenoguim/flexclassstd::make_shared(n) s

Consumer Consumer • Synchronization latency • Memory/Cache latency • CPU cycles latency DLB ： Dynamic Load Balance DLB Enqueue Logic Head and Tail pointers Dequeue Logic & Load Balancer Producer

ms/cpp/linter • Clang-tidy https://aka.ms/cpp/clangtidy • MSVC Code Analysis https://aka.ms/cpp/ca/bg ⚡ Dynamic Analysis • Address Sanitizer https://aka.ms/asan • Fuzzing with libFuzzer https://aka.ms/cpp/libfuzzer

Because of the nested scopes, it’s safe to pass locals by reference to callees... … no dynamic allocation or reference counting needed.64 SENDER/RECEIVER IS ALSO STRUCTURED CONCURRENCY

CUDA 特有的能力。 常用于这种情况：需要从 GPU 端动态计算出 blockDim 和 gridDim ，而又不希望导回数据到 CPU 导致强制同步影响性能。 这种模式被称为动态并行（ dynamic parallelism ）， OpenGL 有一 个 glDispatchComputeIndirect 的 API 和这个很像，但毕竟没有 CUDA 可以直接在核函数里调用核函数并指定参数这么方便……