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Execution / Warp Behavior

GPU Execution Model

This page explains how warps, divergence, and scheduling behavior affect real GPU utilization. The goal is to connect abstract CUDA concepts to practical outcomes like underutilized lanes, hidden latency, and inconsistent kernel efficiency.

How to use this page

  1. 1. Start by stepping through divergence behavior instead of memorizing definitions.
  2. 2. Watch how branch splits reduce active-lane utilization inside a warp.
  3. 3. Connect that behavior back to your kernel design, control flow, and data layout.
  4. 4. Use this as an intuition tool before deeper occupancy or roofline analysis.

What this helps decide

Use this page when you want to understand whether kernel inefficiency is coming from control flow, low active-lane utilization, or poor latency hiding.

After this, continue to GPU performance for bottleneck classification or go to the occupancy estimator for a more direct tuning workflow.

Back to GPU

Precision Tool

Warp Divergence Visualizer

Enter a CUDA branch condition and visualize how a warp serializes active and waiting lanes across execution passes.

KERNEL CONDITIONAL

QUICK EXAMPLES

Valid expression

EXECUTION METRICS

Efficiency Ratio

33.3%

Active Threads: 11/32

Waiting Threads: 21/32

Passes: 3

Overhead: 66.7% serialization

Warp View

Warp Divergence

Pass 1 of 2
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Pass 1 of 2
Speed:

PASS 1

Executing the if-branch. Non-matching threads are waiting.

11 threads active, 21 threads waiting

PASS 2

Executing the else-branch. Previously active threads become idle.

21 threads active, 11 threads waiting

DIVERGENCE EXPLAINER

WHAT HAPPENED

Only 11 of 32 threads take the if-branch. 21 threads sit idle during pass 1, then 11 sit idle during pass 2. Efficiency: 33.3%.

HOW TO FIX IT

Consider restructuring data so threads in the same warp process elements of the same type - eliminating the modulo branch. Warp divergence is unavoidable sometimes, but minimizing it is key to high GPU occupancy and throughput.

DETAILED METRICS

Efficiency Ratio: 33.3%

Active Threads: 11 / 32

Wasted Slots: 11

Execution Passes: 3

Serialization Overhead: 66.7%

Threads Taking If-Branch: 11

Threads Taking Else-Branch: 21

Branch Imbalance: 31.3%

Recommended Fix: Group similar data per warp to avoid modulo-driven divergence.

High divergence

Condition: threadIdx.x % 3 == 0

Key concept

A warp does best when many lanes stay active together. The more lanes peel off into separate execution paths, the lower your effective utilization becomes.

Common mistake

Developers often focus only on core count or clock speed while ignoring control-flow structure. Divergence can quietly dominate performance even when hardware looks strong on paper.