Claude Skill

cpu-pipelines-and-hazards

Use when explaining pipeline stages, data or control hazards, forwarding, stalls, or superscalar basics behind a counter reading. Not for mispredict cost: use branch-prediction-and-speculation.

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Part of outlinedriven/outline-driven-development — 145 skills

Install

skills CLI npx skills add https://github.com/OutlineDriven/outline-driven-development/tree/main/.devin/skills/cpu-pipelines-and-hazards
Claude Code claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install outlinedriven-outline-driven-development@llmmart
Git git clone https://github.com/OutlineDriven/outline-driven-development.git

The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole outlinedriven/outline-driven-development collection as a plugin from our marketplace. Git is the plain clone.

Skill manifest

CPU pipelines and hazards

Contract

Field Bound contract
Trigger A stall counter needs interpreting, instruction order changes throughput in a hot loop, or assembly scheduling needs relating to hardware behavior.
Authority Read-only. The skill runs perf stat on a user-named binary and answers in chat. Nothing on disk changes, so there is nothing to roll back. No remote mutation.
Side effect Chat output only.
Done The hazard class in the loop is named, the counter that shows it is quoted where a binary exists, and one restructuring is proposed with its condition.

Inputs

  • Hot loop (required): source or assembly.
  • Binary and workload (optional): needed for counter evidence.
  • Target microarchitecture (optional): in-order embedded core or out-of-order desktop or server core. The answer differs.

Procedure

  1. Set the model. The five-stage in-order pipeline (fetch, decode, execute, memory, writeback) overlaps instruction N in execute with N+1 in decode. Out-of-order cores rename registers, issue to several ports, and retire in order; the five-stage picture still explains where a dependency costs. Done when: the user knows which model applies to the target.
  2. Classify the hazard. Done when: each dependency in the loop has a class.
Hazard Example Hardware answer
Read after write (true dependency) add r1, r2, r3 then sub r4, r1, r5 Forwarding from the execute or memory stage; a stall when the producer is a load
Write after read or write after write Rare in an in-order core; matters under out-of-order rename Register renaming
Control A branch whose target is unknown until execute Prediction, then a flush on mispredict; cost scales with pipeline depth
Structural One memory port shared by two loads Stall with no dependency at all
  1. Break the dependency chain where the loop is latency-bound. A single accumulator serializes every iteration on the add latency. Two or more accumulators expose independent chains and let the core issue them in parallel. Done when: the loop is restructured or the user confirms the loop is memory-bound and the change would not help.
/* Serial: each iteration waits on acc. */
acc = 0;
for (int i = 0; i < n; i++)
    acc = acc + data[i];

/* Two chains: the core overlaps them. */
acc0 = acc1 = 0;
for (int i = 0; i + 1 < n; i += 2) {
    acc0 += data[i];
    acc1 += data[i + 1];
}
if (n & 1) /* odd tail: one element left */
    acc0 += data[n - 1];
acc = acc0 + acc1;
  1. Measure the stall split. Done when: front-end and back-end stall counts for the real workload are recorded, or the answer is marked unmeasured.
perf stat -e instructions,cycles,stalled-cycles-frontend,stalled-cycles-backend ./app

Read instructions per cycle against the core's issue width, not against a fixed number: a four-wide core running a dependent chain sits near one, and that is the chain's floor, not a defect. A high front-end stall share points at instruction cache misses or mispredicts; a high back-end share points at load latency or a saturated port. Memory latency dominates most loops, so pair this with cpu-cache-opt before scheduling instructions by hand.

Failure and recovery

Failure class Behavior
No binary or workload Deliver the hazard classification and the restructuring as a hypothesis.
perf stat denied Report the perf_event_paranoid value the tool prints. Do not change the sysctl.
High front-end stalls Look at instruction cache footprint and mispredicts; use branch-prediction-and-speculation.
Unrolling gives no gain The loop is memory-bound. Profile loads and consider prefetch; use cpu-cache-opt.
Cycle model does not match The target is out-of-order and the in-order count was applied. Use the counters, not a hand count.
A nop fixes a device timing bug That is memory-mapped I/O ordering, not a pipeline hazard. Use the proper barrier or delay primitive; never tune device delays with nop.

Output

A chat answer naming the hazard class for each dependency, the front-end versus back-end stall split when measured, and one restructuring with the condition under which it helps.

Files (outline-driven-development)
  • agents
    • openai.yaml 208 B
      interface:
        display_name: "Cpu Pipelines And Hazards"
        short_description: "Use when explaining pipeline stages, data or control hazards, forwarding, stalls, or superscalar basics behind a counter reading."
      
  • SKILL.md 4.4 KB
    ---
    name: cpu-pipelines-and-hazards
    description: 'Use when explaining pipeline stages, data or control hazards, forwarding, stalls, or superscalar basics behind a counter reading. Not for mispredict cost: use branch-prediction-and-speculation.'
    ---
    
    # CPU pipelines and hazards
    
    ## Contract
    
    | Field | Bound contract |
    |---|---|
    | Trigger | A stall counter needs interpreting, instruction order changes throughput in a hot loop, or assembly scheduling needs relating to hardware behavior. |
    | Authority | Read-only. The skill runs `perf stat` on a user-named binary and answers in chat. Nothing on disk changes, so there is nothing to roll back. No remote mutation. |
    | Side effect | Chat output only. |
    | Done | The hazard class in the loop is named, the counter that shows it is quoted where a binary exists, and one restructuring is proposed with its condition. |
    
    ## Inputs
    
    - Hot loop (required): source or assembly.
    - Binary and workload (optional): needed for counter evidence.
    - Target microarchitecture (optional): in-order embedded core or out-of-order desktop or server core. The answer differs.
    
    ## Procedure
    
    1. Set the model. The five-stage in-order pipeline (fetch, decode, execute, memory, writeback) overlaps instruction N in execute with N+1 in decode. Out-of-order cores rename registers, issue to several ports, and retire in order; the five-stage picture still explains where a dependency costs. Done when: the user knows which model applies to the target.
    2. Classify the hazard. Done when: each dependency in the loop has a class.
    
    | Hazard | Example | Hardware answer |
    |---|---|---|
    | Read after write (true dependency) | `add r1, r2, r3` then `sub r4, r1, r5` | Forwarding from the execute or memory stage; a stall when the producer is a load |
    | Write after read or write after write | Rare in an in-order core; matters under out-of-order rename | Register renaming |
    | Control | A branch whose target is unknown until execute | Prediction, then a flush on mispredict; cost scales with pipeline depth |
    | Structural | One memory port shared by two loads | Stall with no dependency at all |
    
    3. Break the dependency chain where the loop is latency-bound. A single accumulator serializes every iteration on the add latency. Two or more accumulators expose independent chains and let the core issue them in parallel. Done when: the loop is restructured or the user confirms the loop is memory-bound and the change would not help.
    
    ```c
    /* Serial: each iteration waits on acc. */
    acc = 0;
    for (int i = 0; i < n; i++)
        acc = acc + data[i];
    
    /* Two chains: the core overlaps them. */
    acc0 = acc1 = 0;
    for (int i = 0; i + 1 < n; i += 2) {
        acc0 += data[i];
        acc1 += data[i + 1];
    }
    if (n & 1) /* odd tail: one element left */
        acc0 += data[n - 1];
    acc = acc0 + acc1;
    ```
    
    4. Measure the stall split. Done when: front-end and back-end stall counts for the real workload are recorded, or the answer is marked unmeasured.
    
    ```bash
    perf stat -e instructions,cycles,stalled-cycles-frontend,stalled-cycles-backend ./app
    ```
    
    Read instructions per cycle against the core's issue width, not against a fixed number: a four-wide core running a dependent chain sits near one, and that is the chain's floor, not a defect. A high front-end stall share points at instruction cache misses or mispredicts; a high back-end share points at load latency or a saturated port. Memory latency dominates most loops, so pair this with `cpu-cache-opt` before scheduling instructions by hand.
    
    ## Failure and recovery
    
    | Failure class | Behavior |
    |---|---|
    | No binary or workload | Deliver the hazard classification and the restructuring as a hypothesis. |
    | `perf stat` denied | Report the `perf_event_paranoid` value the tool prints. Do not change the sysctl. |
    | High front-end stalls | Look at instruction cache footprint and mispredicts; use `branch-prediction-and-speculation`. |
    | Unrolling gives no gain | The loop is memory-bound. Profile loads and consider prefetch; use `cpu-cache-opt`. |
    | Cycle model does not match | The target is out-of-order and the in-order count was applied. Use the counters, not a hand count. |
    | A `nop` fixes a device timing bug | That is memory-mapped I/O ordering, not a pipeline hazard. Use the proper barrier or delay primitive; never tune device delays with `nop`. |
    
    ## Output
    
    A chat answer naming the hazard class for each dependency, the front-end versus back-end stall split when measured, and one restructuring with the condition under which it helps.
    

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