Recent Posts

C++17: Efficiently Returning std::vector from Functions

The discussion centers on returning std::vector from C++ functions, highlighting Return Value Optimization (RVO) introduced in C++17. RVO allows the compiler to avoid copying vectors by constructing them in place when there’s a single return path. For multiple return paths, std::move is used to transfer ownership efficiently. Exceptions exist, particularly with the conditional operator, which requires copying. Returning references from member functions is safer than from free functions since the object’s lifetime ensures validity.

When std::shared_mutex Outperforms std::mutex: A Google Benchmark Study

In multi-threaded programming, protecting shared resources is crucial. std::mutex is common, but may bottleneck performance when many readers access data. std::shared_mutex can optimize scenarios with many readers and few writers, though its advantages depend on thread concurrency. Understanding the workload’s read/write balance is key for effective synchronization.

std::span C++20: When to Use (and NOT Use) for Safe Buffer Passing

This post discusses the importance of efficient data handling in modern C++, exploring methods of passing contiguous data buffers to functions, including raw pointers, std::vector, std::array, and the C++20 feature std::span. It highlights the strengths and weaknesses of each method, emphasizing std::span’s benefits for safe, flexible data management without ownership.

Optimal C++ Containers for Performance Efficiency

Choosing an appropriate C++ container impacts memory layout, cache efficiency, and access patterns, vital for performance. Common comparisons include std::vector, std::deque, std::array, std::list, std::map, and std::unordered_map. The choice should align with data access and modification requirements, ensuring optimal performance for diverse workloads, from iteration to key-based access.

BITS PILANI WILP Second Semester MTech in Cloud Computing Study Notes

The study notes cover AI, ML, and deep learning, highlighting key concepts such as pre-trained and fine-tuned models, transfer learning, and their interpretability. It explores cognitive services across platforms like Azure and Google Cloud, emphasizing their applications in AI workflows, NLP, and computer vision tasks, along with practical examples and tools.