Table of Contents

• When to Use
• Required TodoWrite Items
• Step 1: Establish Current Baseline
• Step 2: Narrow the Scope
• Step 3: Instrument Before You Optimize
• Step 4: Throttle and Sequence Work
• Step 5: Log Decisions and Next Steps
• Output Expectations

CPU/GPU Performance Discipline

When To Use

• At the beginning of every session (auto-load alongside token-conservation).
• Whenever you plan to build, train, or test anything that could pin CPU cores

or GPUs for more than a minute.

• Before retrying a failing command that previously consumed significant resources.

When NOT To Use

• Simple operations with no resource impact
• Quick single-file operations

Required TodoWrite Items

1. cpu-gpu-performance:baseline
2. cpu-gpu-performance:scope
3. cpu-gpu-performance:instrument
4. cpu-gpu-performance:throttle
5. cpu-gpu-performance:log

Step 1: Establish Current Baseline

• Capture current utilization:
• uptime
• ps -eo pcpu,cmd | head
• nvidia-smi --query-gpu=utilization.gpu,memory.used --format=csv

Note which hosts/GPUs are already busy.

• Record any CI/cluster budgets (time quotas, GPU hours) before launching work.
• Set a per-task CPU minute / GPU minute budget that respects those limits.

Step 2: Narrow the Scope

• Avoid running "whole world" jobs after a small fix. Prefer diff-based

or tag-based selective testing:

• pytest -k
• Bazel target patterns
• cargo test <module>
• Batch low-level fixes so you can validate multiple changes with a single targeted command.
• For GPU jobs, favor unit-scale smoke inputs or lower epoch counts before

scheduling the full training/eval sweep.

Step 3: Instrument Before You Optimize

• Pick the right profiler/monitor:
• CPU work:
• perf
• intel vtune
• cargo flamegraph
• language-specific profilers
• GPU work:
• nvidia-smi dmon
• nsys
• nvprof
• DLProf
• framework timeline tracers
• Capture kernel/ops timelines, memory footprints, and data pipeline latency

so you have evidence when throttling or parallelizing.

• Record hot paths and I/O bottlenecks in notes so future reruns can jump straight to the culprit.

Step 4: Throttle and Sequence Work

• Use nice, ionice, or Kubernetes/Slurm quotas to prevent starvation of shared nodes.
• Chain heavy tasks with guardrails:
• Rerun only the failed test/module
• Then (optionally) escalate to the next-wider shard
• Reserve the full suite for the final gate
• Stagger GPU kernels (smaller batch sizes or gradient accumulation) when memory

pressure risks eviction; prefer checkpoint/restore over restarts.

Step 5: Log Decisions and Next Steps

Conclude by documenting the commands that were run and their resource cost (duration, CPU%, GPU%), confirming whether they remained within the per-task budget. If a full suite or long training run was necessary, justify why selective or staged approaches were not feasible. Capture any follow-up tasks, such as adding a new test marker or profiling documentation, to simplify future sessions.

Output Expectations

• Brief summary covering:
• baseline metrics
• scope chosen
• instrumentation captured
• throttling tactics
• follow-up items
• Concrete example(s) of what ran (e.g.):
• "reran pytest tests/test_orders.py -k test_refund instead of pytest -m slow"
• "profiled nvidia-smi dmon output to prove GPU idle time before scaling"

Exit Criteria

• ] uptime and ps baseline captured and recorded before any

build, training run, or test suite starts

• ] Scope narrowed to diff-based or tag-based targets (e.g.,

pytest -k, cargo test <module>); full-suite justification documented if selective approach was not feasible

• ] Output summary includes: duration, CPU% or GPU% consumed, and

whether the run stayed within the per-task budget

• ] Any follow-up tasks (new test markers, profiling docs) written

to a todo or issue so they survive the session