couple more benchmark formatting fixes

Author: finrunsfar

benchmarks/elementwise-math/README.md

@@ -24,14 +24,17 @@ The scripts scale the number of samples via `ELM_POINTS` (default
 ### Elementwise Math Perf Sweep (points)
 | points | RunMat (ms) | PyTorch (ms) | NumPy (ms) | NumPy ÷ RunMat | PyTorch ÷ RunMat |
 |---|---:|---:|---:|---:|---:|
-| 1,000,000   | 145.15 | 856.41  |   72.39 | 0.50× | 5.90× |
-| 2,000,000   | 149.75 | 901.05  |   79.49 | 0.53× | 6.02× |
-| 5,000,000   | 145.14 | 1111.16 |  119.45 | 0.82× | 7.66× |
-| 10,000,000  | 143.39 | 1377.43 |  154.38 | 1.08× | 9.61× |
-| 100,000,000 | 144.81 | 16,404.22 | 1,073.09 | 7.41× | 113.28× |
-| 200,000,000 | 156.94 | 16,558.98 | 2,114.66 | 13.47× | 105.51× |
-| 500,000,000 | 137.58 | 17,882.11 | 5,026.94 | 36.54× | 129.97× |
-| 1,000,000,000 | 144.40 | 20,841.42 | 11,931.93 | 82.63× | 144.34× |
+| 1M   | 145.15 | 856.41  |   72.39 | 0.50× | 5.90× |
+| 2M   | 149.75 | 901.05  |   79.49 | 0.53× | 6.02× |
+| 5M   | 145.14 | 1111.16 |  119.45 | 0.82× | 7.66× |
+| 10M  | 143.39 | 1377.43 |  154.38 | 1.08× | 9.61× |
+| 100M | 144.81 | 16,404.22 | 1,073.09 | 7.41× | 113.28× |
+| 200M | 156.94 | 16,558.98 | 2,114.66 | 13.47× | 105.51× |
+| 500M | 137.58 | 17,882.11 | 5,026.94 | 36.54× | 129.97× |
+| 1B | 144.40 | 20,841.42 | 11,931.93 | 82.63× | 144.34× |
+
+*M = 10⁶ elements, B = 10⁹ elements.*
+
 
 ---
 

benchmarks/monte-carlo-analysis/README.md

@@ -16,14 +16,16 @@ price  = mean(payoff) · exp(−μ T Δt)
 
 ![RunMat is up to 131x faster than NumPy](https://web.runmatstatic.com/monte-carlo-analysis_speedup-b.svg)
 
-### Monte Carlo Perf Sweep (M = paths)
-| M | RunMat (ms) | PyTorch (ms) | NumPy (ms) | NumPy ÷ RunMat | PyTorch ÷ RunMat |
-|---|---:|---:|---:|---:|---:|
-| 250,000 | 108.58 | 824.42 | 4065.87 | 37.44× | 7.59× |
-| 500,000 | 136.10 | 900.11 | 8206.56 | 60.30× | 6.61× |
-| 1,000,000 | 188.00 | 894.32 | 16,092.49 | 85.60× | 4.76× |
-| 2,000,000 | 297.65 | 1108.80 | 32,304.64 | 108.53× | 3.73× |
-| 5,000,000 | 607.36 | 1697.59 | 79,894.98 | 131.55× | 2.80× |
+### Monte Carlo Perf Sweep 
+| Paths (simulations) | RunMat (ms) | PyTorch (ms) | NumPy (ms) | NumPy ÷ RunMat | PyTorch ÷ RunMat |
+|--------------------:|-----------:|-------------:|-----------:|---------------:|-----------------:|
+| 250k   | 108.58 |   824.42 |  4,065.87 | 37.44× | 7.59× |
+| 500k   | 136.10 |   900.11 |  8,206.56 | 60.30× | 6.61× |
+| 1M     | 188.00 |   894.32 | 16,092.49 | 85.60× | 4.76× |
+| 2M     | 297.65 | 1,108.80 | 32,304.64 |108.53× | 3.73× |
+| 5M     | 607.36 | 1,697.59 | 79,894.98 |131.55× | 2.80× |
+
+*250k = 250,000 paths, 1M = 1,000,000 paths, etc.*
 
 ---
 

website/components/benchmarks/BenchmarkSweepCarousel.tsx

@@ -7,9 +7,9 @@ import MonteCarloSweep from "./MonteCarloSweep";
 import ElementwiseMathSweep from "./ElementwiseMathSweep";
 
 const slides = [
-  { id: "4k", component: FourKImagePipelineSweep },
-  { id: "monte-carlo", component: MonteCarloSweep },
-  { id: "elementwise", component: ElementwiseMathSweep },
+  { id: "4k", component: FourKImagePipelineSweep, href: "/benchmarks/4k-image-processing" },
+  { id: "monte-carlo", component: MonteCarloSweep, href: "/benchmarks/monte-carlo-analysis" },
+  { id: "elementwise", component: ElementwiseMathSweep, href: "/benchmarks/elementwise-math" },
 ];
 
 export default function BenchmarkSweepCarousel() {
@@ -31,9 +31,9 @@ export default function BenchmarkSweepCarousel() {
         >
           ←
         </button>
-        <div className="w-full max-w-[42rem]">
+        <a className="w-full max-w-[42rem]" href={activeSlide.href}>
           <Component />
-        </div>
+        </a>
         <button
           type="button"
           onClick={goNext}