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<h1 id="performance-kdtree-benchmarks-and-guidance">Performance: KDTree benchmarks and guidance</h1>
<p>This page summarizes how to measure KDTree performance in this repository and how to compare the two internal backends (Linear vs Gonum) that you can select at build/runtime.</p>
<h2 id="how-benchmarks-are-organized">How benchmarks are organized</h2>
<ul>
<li>Micro-benchmarks live in <code>bench_kdtree_test.go</code>, <code>bench_kdtree_dual_test.go</code>, and <code>bench_kdtree_dual_100k_test.go</code> and cover:</li>
<li><code>Nearest</code> in 2D and 4D with N = 1k, 10k (both backends)</li>
<li><code>Nearest</code> in 2D and 4D with N = 100k (gonum-tag job; linear also measured there)</li>
<li><code>KNearest(k=10)</code> in 2D/4D with N = 1k, 10k</li>
<li><code>Radius</code> (mid radius r≈0.5 after normalization) in 2D/4D with N = 1k, 10k</li>
<li>Datasets: Uniform and 3-cluster synthetic generators in normalized [0,1] spaces.</li>
<li>Backends: Linear (always available) and Gonum (enabled when built with <code>-tags=gonum</code>).</li>
</ul>
<p>Run them locally:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-0-1" name="__codelineno-0-1" href="#__codelineno-0-1"></a><span class="c1"># Linear backend (default)</span>
<a id="__codelineno-0-2" name="__codelineno-0-2" href="#__codelineno-0-2"></a>go<span class="w"> </span><span class="nb">test</span><span class="w"> </span>-bench<span class="w"> </span>.<span class="w"> </span>-benchmem<span class="w"> </span>-run<span class="o">=</span>^$<span class="w"> </span>./...
<a id="__codelineno-0-3" name="__codelineno-0-3" href="#__codelineno-0-3"></a>
<a id="__codelineno-0-4" name="__codelineno-0-4" href="#__codelineno-0-4"></a><span class="c1"># Gonum backend (optimized KD; requires build tag)</span>
<a id="__codelineno-0-5" name="__codelineno-0-5" href="#__codelineno-0-5"></a>go<span class="w"> </span><span class="nb">test</span><span class="w"> </span>-tags<span class="o">=</span>gonum<span class="w"> </span>-bench<span class="w"> </span>.<span class="w"> </span>-benchmem<span class="w"> </span>-run<span class="o">=</span>^$<span class="w"> </span>./...
</code></pre></div>
<p>GitHub Actions publishes benchmark artifacts on every push/PR:
- Linear job: artifact <code>bench-linear.txt</code>
- Gonum job: artifact <code>bench-gonum.txt</code></p>
<h2 id="backend-selection-and-defaults">Backend selection and defaults</h2>
<ul>
<li>Default backend is Linear.</li>
<li>If you build with <code>-tags=gonum</code>, the default switches to the optimized KD backend.</li>
<li>You can override at runtime:</li>
</ul>
<div class="highlight"><pre><span></span><code><a id="__codelineno-1-1" name="__codelineno-1-1" href="#__codelineno-1-1"></a>// Force Linear
<a id="__codelineno-1-2" name="__codelineno-1-2" href="#__codelineno-1-2"></a>kdt, _ := poindexter.NewKDTree(pts, poindexter.WithBackend(poindexter.BackendLinear))
<a id="__codelineno-1-3" name="__codelineno-1-3" href="#__codelineno-1-3"></a>// Force Gonum (requires build tag)
<a id="__codelineno-1-4" name="__codelineno-1-4" href="#__codelineno-1-4"></a>kdt, _ := poindexter.NewKDTree(pts, poindexter.WithBackend(poindexter.BackendGonum))
</code></pre></div>
<p>Supported metrics in the optimized backend: L2 (Euclidean), L1 (Manhattan), L∞ (Chebyshev). Cosine/Weighted-Cosine currently use the Linear backend.</p>
<h2 id="what-to-expect-rule-of-thumb">What to expect (rule of thumb)</h2>
<ul>
<li>Linear backend: O(n) per query; fast for small-to-medium datasets (≤10k), especially in low dims (≤4).</li>
<li>Gonum backend: typically sub-linear for prunable datasets and dims ≤ ~8, with noticeable gains as N grows (≥10k–100k), especially on uniform or moderately clustered data and moderate radii.</li>
<li>For large radii (many points within r) or highly correlated/pathological data, pruning may be less effective and behavior approaches O(n) even with KD-trees.</li>
</ul>
<h2 id="interpreting-results">Interpreting results</h2>
<p>Benchmarks output something like:</p>
<div class="highlight"><pre><span></span><code><a id="__codelineno-2-1" name="__codelineno-2-1" href="#__codelineno-2-1"></a>BenchmarkNearest_10k_4D_Gonum_Uniform-8 50000 12,300 ns/op 0 B/op 0 allocs/op
</code></pre></div>
<ul>
<li><code>ns/op</code>: lower is better (nanoseconds per operation)</li>
<li><code>B/op</code> and <code>allocs/op</code>: memory behavior; fewer is better</li>
<li><code>KNearest</code> incurs extra work due to sorting; <code>Radius</code> cost scales with the number of hits.</li>
</ul>
<h2 id="improving-performance">Improving performance</h2>
<ul>
<li>Normalize and weight features once; reuse across queries (see <code>Build*WithStats</code> helpers).</li>
<li>Choose a metric aligned with your policy: L2 usually a solid default; L1 for per-axis penalties; L∞ for hard-threshold dominated objectives.</li>
<li>Batch queries to benefit from CPU caches.</li>
<li>Prefer the Gonum backend for larger N and dims ≤ ~8; stick to Linear for tiny datasets or when using Cosine metrics.</li>
</ul>
<h2 id="reproducing-and-tracking-performance">Reproducing and tracking performance</h2>
<ul>
<li>Local (Linear): <code>go test -bench . -benchmem -run=^$ ./...</code></li>
<li>Local (Gonum): <code>go test -tags=gonum -bench . -benchmem -run=^$ ./...</code></li>
<li>CI artifacts: download <code>bench-linear.txt</code> and <code>bench-gonum.txt</code> from the latest workflow run.</li>
<li>Optional: add historical trend graphs via Benchstat or Codecov integration.</li>
</ul>
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