DeepSeek: How to deal with the popular AI that is congested with servers? As a hot AI in 2025, DeepSeek is free and open source and has a performance comparable to the official version of OpenAIo1, which shows its popularity. However, high concurrency also brings the problem of server busyness. This article will analyze the reasons and provide coping strategies. DeepSeek web version entrance: https://www.deepseek.com/DeepSeek server busy reason: High concurrent access: DeepSeek's free and powerful features attract a large number of users to use at the same time, resulting in excessive server load. Cyber ??Attack: It is reported that DeepSeek has an impact on the US financial industry.

The C++ concurrent programming framework features the following options: lightweight threads (std::thread); thread-safe Boost concurrency containers and algorithms; OpenMP for shared memory multiprocessors; high-performance ThreadBuildingBlocks (TBB); cross-platform C++ concurrency interaction Operation library (cpp-Concur).

Methods for inter-thread communication in C++ include: shared memory, synchronization mechanisms (mutex locks, condition variables), pipes, and message queues. For example, use a mutex lock to protect a shared counter: declare a mutex lock (m) and a shared variable (counter); each thread updates the counter by locking (lock_guard); ensure that only one thread updates the counter at a time to prevent race conditions.

Pitfalls in Go Language When Designing Distributed Systems Go is a popular language used for developing distributed systems. However, there are some pitfalls to be aware of when using Go, which can undermine the robustness, performance, and correctness of your system. This article will explore some common pitfalls and provide practical examples on how to avoid them. 1. Overuse of concurrency Go is a concurrency language that encourages developers to use goroutines to increase parallelism. However, excessive use of concurrency can lead to system instability because too many goroutines compete for resources and cause context switching overhead. Practical case: Excessive use of concurrency leads to service response delays and resource competition, which manifests as high CPU utilization and high garbage collection overhead.

Program performance optimization methods include: Algorithm optimization: Choose an algorithm with lower time complexity and reduce loops and conditional statements. Data structure selection: Select appropriate data structures based on data access patterns, such as lookup trees and hash tables. Memory optimization: avoid creating unnecessary objects, release memory that is no longer used, and use memory pool technology. Thread optimization: identify tasks that can be parallelized and optimize the thread synchronization mechanism. Database optimization: Create indexes to speed up data retrieval, optimize query statements, and use cache or NoSQL databases to improve performance.

C language multithreading programming guide: Creating threads: Use the pthread_create() function to specify thread ID, properties, and thread functions. Thread synchronization: Prevent data competition through mutexes, semaphores, and conditional variables. Practical case: Use multi-threading to calculate the Fibonacci number, assign tasks to multiple threads and synchronize the results. Troubleshooting: Solve problems such as program crashes, thread stop responses, and performance bottlenecks.

Lock granularity tips for optimizing Go concurrent cache performance: Global lock: Simple implementation, if the lock granularity is too large, unnecessary competition will occur. Key-level locking: The lock granularity is refined to each key, but it will introduce a large number of locks and increase overhead. Shard lock: Divide the cache into multiple shards, each shard has a separate lock, to achieve a balance between concurrency and lock contention.

Two ways to achieve thread safety in Java functions: pessimistic locking: acquire the lock before accessing data to prevent concurrent access by other threads to ensure data consistency. (synchronized keyword) Optimistic locking: Verify data at the end of the transaction and roll back the transaction if the data is modified to improve concurrency. (Atomic class in java.util.concurrent.atomic package)