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[update] poster
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@@ -862,8 +862,8 @@ <h1>Breaking the Speed Limit</h1>
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<section class="columns">
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<div class="col">
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<section>
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<div class="section-title blue"><b class="dot">1</b><h2>Simulation Defines "Correct"</h2></div>
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<p class="lead">Real data rarely provides ground truth. Simulation creates a controlled setting to test power, calibration, and agreement.</p>
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<div class="section-title blue"><b class="dot">1</b><h2>Simulation Makes Correctness Testable</h2></div>
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<p class="lead">Real data often lacks known ground truth for method behavior. Simulation creates a controlled setting to test power, calibration, and agreement.</p>
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<div class="workflow">
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<article class="workflow-card card blue"><span class="num">01</span><strong>Define Target</strong><p>what to estimate, recover, preserve</p></article>
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<article class="workflow-card card teal"><span class="num">02</span><strong>Build Scenarios</strong><p>null, alternative, hard or extremecases</p></article>
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<article class="workflow-card card gold"><span class="num">05</span><strong>Analyze Workload</strong><p>data flow, loops, algebra, repetition, memory</p></article>
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<article class="workflow-card card berry"><span class="num">06</span><strong>Select Tool</strong><p>suitable for your task</p></article>
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</div>
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<div class="gate">Rule: Do not optimize until ALL validations pass.</div>
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<div class="gate">Rule: Speedups are trusted only after validation passes.</div>
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</section>
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<section>
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<tr><td>Statistical target</td><td>estimator, test statistic, or stopping criterion</td></tr>
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<tr><td>Simulated truth</td><td>labels, effect size, or nominal alpha</td></tr>
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<tr><td>Acceptance criteria</td><td>exact match, numerical tolerance, or calibration</td></tr>
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<tr><td>Failure mode</td><td>code bug, method breakdown, or out-of-memory (OOM)</td></tr>
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<tr><td>Failure mode</td><td>code bug, method breakdown, or out-of-memory</td></tr>
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<tr><td>Timing scope</td><td>compile, transfer, allocation</td></tr>
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<tr><td>Cost drivers</td><td>samples (n), features (p), or repeats (R)</td></tr>
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</tbody>
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</table>
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<article class="validation-gate card">
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<h3>Speedup results verified using these criteria:</h3>
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<div class="gate-list">
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<div class="gate-row"><b>✓</b><span><strong>K-means agreement:</strong> max relative inertia difference well below the 1e-8 tolerance.</span></div>
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<div class="gate-row"><b>✓</b><span><strong>K-means agreement:</strong> max relative inertia difference well below the tolerance.</span></div>
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<div class="gate-row"><b>✓</b><span><strong>Permutation equivalence:</strong> same test statistic, same p-value definition, same resampling stream; max |p diff| < tolerance.</span></div>
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<div class="gate-row"><b>✓</b><span><strong>Statistic difference:</strong> max |stat diff| < tolerance across the validation grid.</span></div>
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<div class="gate-row"><b>✓</b><span><strong>Null calibration:</strong> estimated type-I error aligns with nominal alpha 0.05.</span></div>
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<div class="gate-row"><b>✓</b><span><strong>Null calibration:</strong> estimated type-I error within MC uncertainty of nominal α = 0.05.</span></div>
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</div>
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</article>
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</section>
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<div class="contract" style="font-size: 1.2cqb;">
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<strong>Validation criteria:</strong>
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<b>K-means</b> Match cluster assignments by fixing seeds and convergence thresholds.
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<b>Permutation</b> Guarantee null distribution consistency via deterministic resampling seeds.
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<b>Permutation</b> Same W, same statistic, same p-value formula.
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</div>
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</section>
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<li>• <b>Dense algebra:</b> NumPy dominates via optimized BLAS kernels.</li>
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<li>• <b>Massive data batches:</b> JAX/GPU wins on SIMT throughput.</li>
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</ul>
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<img src="figures/kmeans_evidence_rows_cropped.png" alt="Server k-means evidence showing implementation choice depends on workload structure" style="margin-top: -2.2cqb;" />
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<img src="figures/kmeans_evidence_rows_cropped.png" alt="Server k-means evidence showing implementation choice depends on workload structure" style="margin-top: -0.2cqb;" />
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</article>
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<article class="plot-card card">
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<h3>Permutation: GPU Scaling via Vectorized Batching</h3>
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<ul style="font-size: 1cqb; list-style-type: none; padding-left: 0; margin-bottom: -1cqb;">
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<ul style="font-size: 1cqb; list-style-type: none; padding-left: 0; margin-bottom: -1cqb; margin-bottom: -1cqb;">
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<li><strong>Infrastructure Requirement:</strong> High-concurrency batching and fused reductions</li>
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<li><strong>Validated Speedup:</strong> Peaks at 8.54× vs. optimized multi-core CPU</li>
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<li><strong>Observed Speedup:</strong> up to 8.54× for n=5k, p=500k, R=5k, using a matched CPU matrix baseline vs. A100 streamed end-to-end path.</li>
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<li><strong>Operational Boundaries:</strong> Explicit OOM profiling for high-dimensional tensors</li>
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</ul>
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<img src="figures/gpu_permutation_decision_map.png" alt="GPU permutation decision map" style="margin-top: -0.1cqb;"/>
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<img src="figures/gpu_permutation_decision_map.png" alt="GPU permutation decision map" style="margin-top: 0.8cqb; margin-bottom: -0.4cqb;"/>
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</article>
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</div>
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</section>

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