Performance improvement: Avoid unnecessary GPU detail queries in _monitor_power()

[vishali-mp]

Description

The _monitor_power() hot path invokes get_gpu_details() every second, but only gpu_index and gpu_utilization are consumed by the caller. get_gpu_details() retrieves memory info, temperature, compute processes, graphics processes, compute mode, and other metadata — all discarded in the monitoring loop. On multi-GPU systems this triggers dozens of unnecessary NVML calls per second, with process enumeration APIs being the most expensive.

Changes:

• codecarbon/core/gpu_device.py: New get_gpu_utilization_lightweight() that returns only gpu_index and gpu_utilization, skipping memory, temperature, process lists, compute mode, and other unused attributes.
• codecarbon/core/gpu.py: New get_gpu_utilization_list() that iterates devices using the lightweight method.
• codecarbon/emissions_tracker.py: Updated _monitor_power() to call get_gpu_utilization_list() instead of get_gpu_details().
• tests/test_gpu_nvidia.py: Added tests for get_gpu_utilization_lightweight(), get_gpu_utilization_list(), and exception handling (returns [] on NVML error).
• **tests/test_emissions_tracker.py: Added 4 edge case tests covering all guard conditions in _monitor_power(): gpu_index is None, GPU not in monitored IDs, missing gpu_utilization key, and empty result list.
• scripts/benchmark_gpu_monitoring.py: Benchmark harness to measure performance delta between old and new paths.

Related Issue

Closes #1237

Motivation and Context

_monitor_power() runs every second and only needs GPU utilization to compute a rolling average. The full get_gpu_details() call retrieves memory (nvmlDeviceGetMemoryInfo), temperature (nvmlDeviceGetTemperature), compute processes (nvmlDeviceGetComputeRunningProcesses), graphics processes (nvmlDeviceGetGraphicsRunningProcesses), and compute mode — none of which are used in this hot path. The process enumeration APIs are especially expensive as they iterate active GPU processes and collect PID-level information.

The lightweight path reduces NVML calls per GPU per second from ~7 to just 1 (only nvmlDeviceGetUtilizationRates), preserving the existing get_gpu_details() for code paths that need full metadata (e.g. __repr__, static info queries).

How Has This Been Tested?

• Unit tests in tests/test_gpu_nvidia.py: test_gpu_utilization_list, test_gpu_utilization_lightweight, test_gpu_utilization_list_empty_on_exception.
• Integration tests in tests/test_emissions_tracker.py: test_monitor_power_collects_gpu_utilization_lightweight (happy path), test_monitor_power_skips_gpu_when_index_is_none, test_monitor_power_skips_gpu_not_in_monitored_ids, test_monitor_power_skips_gpu_when_utilization_key_missing, test_monitor_power_handles_empty_gpu_utilization_list.
• Benchmark script in scripts/benchmark_gpu_monitoring.py: compares calls/sec of lightweight vs full path.
• All existing tests pass, pre-commit checks (autoflake, isort, black, flake8) pass.

Types of changes

• Bug fix (non-breaking change which fixes an issue)
• New feature (non-breaking change which adds functionality)
• Breaking change (fix or feature that would cause existing functionality to change)

AI Usage Disclosure

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Checklist

• My code follows the code style of this project.
• My change requires a change to the documentation.
• I have updated the documentation accordingly.
• I have read the docs/how-to/contributing.md document.
• I have added tests to cover my changes.
• All new and existing tests passed.