I started with the textbook answer (separate address spaces for processes, shared heap for threads within the same process) and that landed fine.
Start by defining a process as an independent execution unit with its own address space, and a thread as a lightweight unit of execution within a process. Then clearly contrast their memory sharing: processes are isolated, while threads share code, data, and heap but have private stacks and registers. Finally, connect this to practical implications like context-switching cost, communication, and fault isolation.
Pro tip: Emphasize that threads share the heap, which means they can easily share data but also introduce race conditions, while processes require IPC. This shows you understand the trade-offs in real-world system design.
Clearly state that a process is an independent program in execution with its own memory space, while a thread is a smaller unit of execution within a process that shares resources.
Detail what is shared: threads share code, data, heap, and open files; processes share nothing by default. Mention that each thread has its own stack, registers, and program counter.
Highlight that processes are isolated, requiring IPC (pipes, sockets, shared memory) to communicate, while threads communicate directly via shared memory, which is faster but riskier.
Note that thread context switching is cheaper than process switching because it avoids TLB and cache flushes. Processes are heavier but more robust.
Connect to when to use threads vs processes: threads for concurrency within an app, processes for isolation and fault tolerance. Mention examples like web servers or databases.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
I rattled off race conditions, deadlocks, priority inversion without much trouble, but when they asked me to get concrete about mitigation I kind of defaulted to 'use a mutex' for everything.
Start by defining shared memory and its role in concurrent programming, then systematically outline the main risks such as data races, deadlocks, and memory consistency issues. For each risk, pair it with mitigation techniques like synchronization primitives, lock-free structures, and careful design patterns, emphasizing trade-offs and practical experience.
Pro tip: Demonstrate depth by discussing not just the risks but also the performance implications of mitigations, and mention how you've applied these in real systems to balance safety and scalability.
Briefly explain what shared memory across threads is and why it's used, highlighting benefits like performance and simplicity for data sharing.
Enumerate key risks: data races, deadlocks, livelocks, memory consistency errors, and false sharing, with concise examples.
For each risk, outline techniques such as mutexes, semaphores, atomic operations, lock-free data structures, and memory barriers.
Explain the trade-offs between performance and safety, and share best practices like minimizing shared state and using higher-level abstractions.
Illustrate with a personal experience or a common scenario where you applied these techniques to solve a concurrency issue.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.