 Secure your microservices with istio step by stepwhich specifying above secret and define corresponding virtual service which configuring traffic routes Secure ingress gateway via TLS terminating Using ingress host and secure ingress port to send which specifying above secret and define corresponding virtual service which configuring traffic routes Secure ingress gateway via TLS terminating https http http http mTLS mTLS #IstioCon Secure0 码力 | 34 页 | 67.93 MB | 1 年前3 Secure your microservices with istio step by stepwhich specifying above secret and define corresponding virtual service which configuring traffic routes Secure ingress gateway via TLS terminating Using ingress host and secure ingress port to send which specifying above secret and define corresponding virtual service which configuring traffic routes Secure ingress gateway via TLS terminating https http http http mTLS mTLS #IstioCon Secure0 码力 | 34 页 | 67.93 MB | 1 年前3
 C++20: An (Almost) Complete OverviewPack 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 C++20: An (Almost) Complete OverviewPack 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 0 码力 | 85 页 | 512.18 KB | 6 月前3 KubeCon2020/大型Kubernetes集群的资源编排优化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-Scheduler0 码力 | 27 页 | 3.91 MB | 1 年前3 KubeCon2020/大型Kubernetes集群的资源编排优化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-Scheduler0 码力 | 27 页 | 3.91 MB | 1 年前3 C++高性能并行编程与优化 -  课件 - 02 现代 C++ 入门:RAII 内存管理虚函数与纯虚函数 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 C++高性能并行编程与优化 -  课件 - 02 现代 C++ 入门:RAII 内存管理虚函数与纯虚函数 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: 0 码力 | 96 页 | 16.28 MB | 1 年前3 FlexClassFlexClass Classes with dynamic size for everyone “C++ Flexible Array Members” Breno Guimarães brenorg@gmail.com Twitter @brenorg https://github.com/brenoguim/flexclassstd::make_shared FlexClassFlexClass 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 0 码力 | 8 页 | 957.56 KB | 6 月前3 使用硬件加速Tokio - 戴翔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 Producer0 码力 | 17 页 | 1.66 MB | 1 年前3 使用硬件加速Tokio - 戴翔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 Producer0 码力 | 17 页 | 1.66 MB | 1 年前3 whats new in visual studioms/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/libfuzzer0 码力 | 42 页 | 19.02 MB | 6 月前3 whats new in visual studioms/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/libfuzzer0 码力 | 42 页 | 19.02 MB | 6 月前3 Working with Asynchrony Generically: A Tour of C++ ExecutorsBecause 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 CONCURRENCY0 码力 | 121 页 | 7.73 MB | 6 月前3 Working with Asynchrony Generically: A Tour of C++ ExecutorsBecause 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 CONCURRENCY0 码力 | 121 页 | 7.73 MB | 6 月前3 C++高性能并行编程与优化 -  课件 - 08 CUDA 开启的 GPU 编程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……0 码力 | 142 页 | 13.52 MB | 1 年前3 C++高性能并行编程与优化 -  课件 - 08 CUDA 开启的 GPU 编程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……0 码力 | 142 页 | 13.52 MB | 1 年前3
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