Description
Can you tell me about a time when you had to run parallel inference on a CPU? How did you approach the situation, and what were the results?
1. Technical Knowledge : Understanding of parallel computing concepts and experience with CPU inference.
2. Problem-Solving : Ability to tackle complexities associated with parallel processing and optimize CPU performance.
3. Attention to Detail : Precision in diagnosing performance bottlenecks and implementing solutions in a parallel environment.
4. Experience with Tools/Frameworks : Familiarity with the tools and frameworks used for facilitating parallel inference, such as OpenMP, TBB, or proprietary Nvidia libraries.
1. Assessing Technical Expertise : To gauge your technical foundation and hands-on experience with parallel computation on CPUs.
2. Understanding Problem-Solving Approach : To understand how you tackle technical challenges and think through problems.
3. Evaluating Project Impact : To measure the direct impact your work has had on project outcomes, especially performance improvements.
4. Gauging Familiarity with Industry Practices : To determine whether you are experienced with modern practices in high-performance computing and parallelization.
1. Discuss Specific Tools : Mention any specific tools or libraries you used for parallelization on CPUs.
2. Detail the Process : Explain the steps you took to implement and optimize parallel inference.
3. Highlight Problem Complexity : Talk about the complexity of the problems you solved and how you measured improvements.