When designing performance tests, focus on efficiency and meaningful insights rather than exhaustive combinations. Consider these principles:

1. Limit parameter combinations to avoid excessive test duration. For example, instead of:

   @pytest.mark.parametrize(
    "pipelining_config,ps_io_concurrency,l0_stack_height,queue_depth,name",
    [
        (config, ps_io_concurrency, l0_stack_height, queue_depth, f"{dataclasses.asdict(config)}")
        for config in LATENCY_CONFIGS
        for ps_io_concurrency in PS_IO_CONCURRENCY
        for queue_depth in [1, 2, 3, 4, 16, 32]
        for l0_stack_height in [0, 3, 10]
    ],
   )
   

   Consider testing only critical parameter combinations such as production values and a few boundary cases.

2. Use robust performance measurement techniques when comparing optimizations. For significant features, measure performance both with and without the optimization and average multiple runs to account for system variability: ```python

   Run each test multiple times and average the results

   run_times_with_feature = [] run_times_without_feature = [] for _ in range(SAMPLE_SIZE): run_times_with_feature.append(measure_execution(feature_enabled=True)) run_times_without_feature.append(measure_execution(feature_enabled=False))

avg_with_feature = sum(run_times_with_feature) / len(run_times_with_feature) avg_without_feature = sum(run_times_without_feature) / len(run_times_without_feature) ```

1. Balance thoroughness with execution time. Consider randomized parameter selection for exploratory testing when exhaustive testing is impractical, but ensure values are logged for reproducibility.