1. **GPU Architecture Understanding**: - What are the key components of a GPU, and how do they interact? - How is parallel processing structured within a GPU? - What makes a GPU architecture efficient for certain types of computations? 2. **Data Flow in GPUs**: - How is data transferred between memory and processing units within a GPU? - What role does the memory hierarchy play in GPU performance? - How do different types of memory (e.g., GDDR, HBM) impact data flow? 3. **Algorithmic Efficiency**: - What types of algorithms are GPUs particularly good at, and why? - How can algorithms be optimized for parallel processing on GPUs? - What modifications to GPU hardware could improve the efficiency of specific algorithms? 4. **Power and Energy Implications**: - How does power consumption relate to GPU performance? - What strategies are used to balance performance with energy efficiency? - How do hardware design decisions affect the power consumption of a GPU? 5. **Mechanical Design**: - What are the mechanical considerations for housing and cooling GPUs? - How does the form factor of a GPU affect its integration into systems? - What innovations in mechanical design contribute to GPU performance? 6. **Low-level Software**: - How do drivers and firmware interact with GPU hardware? - What are the challenges in programming for GPUs? - How do software optimizations take advantage of GPU architecture? #ai #llm #hardware