When deploying SS-LLMs in multi-threaded environments, there are several core concepts of multi-threading to keep in mind to ensure applications run correctly

1. Race Conditions

• What it is: Two or more threads access shared data at the same time, and the result depends on the order of execution.
• Example: Two threads incrementing a shared counter without synchronization:
  
• # Thread 1 and Thread 2 both run this:
• counter = counter + 1
• Without locking, the final value may be incorrect because both threads might read the same value before writing.

2. Synchronization / Mutual Exclusion

What it is: Mechanisms (locks, mutexes, semaphores, synchronized blocks) that ensure only one thread accesses a resource at a time.

Example (Python threading with a lock):

import threading

lock = threading.Lock()
def safe_increment():

global counter

with lock:

counter += 1

3. Deadlocks

• What it is: When two or more threads wait indefinitely for resources locked by each other.
• Example:
• Thread A holds Lock1, waits for Lock2.
• Thread B holds Lock2, waits for Lock1.
  Both block forever.
• Mitigation: Use a consistent lock acquisition order, or try-lock with timeouts.

4. Starvation & Fairness

• What it is: A thread never gets CPU or resources because others keep acquiring them.
• Example: High-priority threads continually acquire a lock, starving a low-priority thread.
• Mitigation: Use fair locks, balanced thread pools, or scheduling strategies.

5. Thread Safety

• What it is: Code that behaves correctly when executed by multiple threads at the same time.
• Example: Java’s  StringBuffer  is thread-safe;  StringBuilder  is not.
• Mitigation: Use thread-safe collections or immutable objects when possible.

6. Concurrency vs. Parallelism

• Concurrency: Structuring a program to handle multiple tasks at once (e.g., overlapping I/O).
• Parallelism: Actually running tasks simultaneously on multiple cores.
• Example:
• Concurrency: Handling multiple client requests with async I/O.
• Parallelism: Running a data-processing algorithm split across CPU cores.

7. Context Switching & Overheads

• What it is: Switching between threads incurs cost (saving/restoring state). Too many threads can degrade performance.
• Example: Spawning thousands of threads may be worse than using a thread pool.

8. Memory Visibility & Ordering

• What it is: One thread’s updates to shared variables may not be visible immediately to others (due to CPU caches, compiler reordering).
• Mitigation: Use  volatile  (Java),  atomic  variables, or memory fences/barriers.
• Example: In Java:
• 
  
• private volatile boolean running = true;