All options contribute to potential bottlenecks in BCM pipelines. Insufficient CPU cores hamper preprocessing. Incorrect BCM configuration ruins pipelining. Driver incompatibilities cause performance hits or failures. Network latency delays data ingestion.

Segmentation faults related to driver code usually indicate a bug within the driver itself. Reporting the issue with a core dump allows NVIDIA engineers to investigate and provide a fix. Trying to debug the driver yourself (recompiling) or disabling the NVSwitch are less effective solutions for this type of problem. Different versions of CUDA also can cause problems, but first report with the core dump.

Real-time object detection requires both the ability to ingest incoming video streams quickly (high write throughput) and access frames with minimal delay (low latency) for inference. Long-term archival and deduplication are less critical for real-time performance. Cold storage is not relevant in the operational data flow.

Scale-out object storage systems are designed for massive scalability and cost-effectiveness. They often support erasure coding, which provides data redundancy with lower overhead than traditional RAID. SANs are expensive and less scalable. Parallel file systems are optimized for performance, not cost. Distributed databases are not designed for unstructured data. JBODs present management challenges and lack inherent redundancy.

NumPy's 'np.load' function typically reads the entire file sequentially into memory. Therefore, high sequential read throughput is crucial. GPUDirect Storage further optimizes performance by enabling direct memory access between the storage and the GPU, bypassing the CPU and system memory bottlenecks. Using a HDD will drastically reduce performance due to its low throughput and high latency.