To monitor GPU performance effectively in an AI data center, the focusshould be on metrics directly

tied to GPU health and efficiency:
GPU temperature and power consumption(C) are critical to prevent overheating and power-related
failures, which can disrupt workloads or damage hardware. High temperatures or excessive power
draw indicate potential issues requiring intervention.
GPU memory utilization(D) reflects how much of the GPU’s memory is being used by workloads.
High utilization can lead to memory bottlenecks, while low utilization might indicate underuse, both
affecting efficiency.
Disk I/O rates(A) relate to storage performance, not GPU operation directly.
CPU clock speed(B) is a CPU metric, irrelevant to GPU monitoring in this context.
Network bandwidth usage(E) is important for distributed systems but doesn’t directly assess GPU
performance or health.
NVIDIA tools like NVIDIA System Management Interface (nvidia-smi) provide these metrics (C and D),
making them essential for monitoring.
Reference:NVIDIA Data Center GPU Management documentation; nvidia-smi usage guide on
nvidia.com.

In a distributed environment with multiple NVIDIA GPUs, optimizing workload distribution and GPU

utilization requires tools that enable efficient computation and communication:
NVIDIA CUDA(A) is a foundational parallel computing platform that allows developers to harness
GPU power for general-purpose computing, including machine learning. It’s essential for
programming GPUs and optimizing workloads in a distributed setup.
NVIDIA NCCL(D) (NVIDIA Collective Communications Library) is designed for multi-GPU and multinode communication, providing optimized primitives (e.g., all-reduce, broadcast) for collective
operations in deep learning. It ensures efficient data exchange between GPUs, maximizing utilization
in distributed training.
NVIDIA NGC(B) is a hub for GPU-optimized containers and models, useful for deployment but not
directly responsible for workload distribution or GPU utilization optimization.
TensorFlow Serving(C) is a framework for deploying machine learning models for inference, not for
optimizing distributed training or GPU utilization during model development.
Keras(E) is a high-level API for building neural networks, but it lacks the low-level control needed for
distributed workload optimization, it relies on backends like TensorFlow or CUDA.
Thus, CUDA (A) and NCCL (D) are the best choices for this scenario.
Reference: NVIDIA CUDA Toolkit documentation; NVIDIA NCCL documentation on nvidia.com