Padding input tensors (C) to multiples of 8 is crucial for optimal Tensor Core performance, as Tensor Cores operate most efficiently on data with these dimensions. Using FP16 (B) is important, but proper alignment is key for full utilization. Increasing batch size (A) can improve overall throughput but doesn't directly address Tensor Core utilization. CIJDA graph capture (D) reduces kernel launch overhead, not Tensor Core utilization directly. Decreasing learning rate (E) is unrelated to Tensor Core performance.

Improving inter-GPU communication involves optimizing the network used for transferring data between GPUs. PCle peer-to-peer, InfiniBand/RoCE, and proper NCCL configuration all contribute to faster communication. Increasing RAM size helps with data caching but doesn't directly affect inter-GPU communication speed. Disabling CPU frequency scaling is about CPU performance stability, not inter-GPU communication directly.

When running containers that need GPU access, it's essential to explicitly request the GPU resources. The '-gpus all' or '-gpus device=..: flag passed to 'docker run' with the NVIDIA runtime allows the container access to the available GPUs. Without this flag, the container operates as if no GPUs are available. Options A, B, C and D, while potentially problematic, are not the most likely cause if 'nvidia-docker run' was used previously.

Verifying pump speed and flow rate (A) is crucial for liquid cooling systems. Reduced flow can lead to inadequate cooling and thermal issues. Analyzing system logs for GPU-related errors (C) will directly indicate whether thermal throttling or power capping are occurring, which are common symptoms of cooling problems.

A distributed file system with data striping (option B) is the most effective because it parallelizes I/O operations across multiple storage nodes, reducing the load on any single storage device and improving overall throughput for many small files. RAID 0 (A) improves read/write speeds but offers no redundancy. Compression (C) can reduce storage space but adds overhead. Increasing block size (D) is beneficial for large files, but not necessarily for numerous small files. Tiered storage (E) can help, but distributing the file system is the priority for numerous small files.