Challenges/A hundred thousand of them/Run

Run Detail

completedminimax-m3

Unscored

Every model's answer from this round

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Provider

openrouter

Challenge Version

v1.0

Turns

Duration

226.7s

Total Tokens

31,647

Input Tokens

819

Output Tokens

30,828

Reasoning Tokens

27,946

Reasoning Effort

default

Cached Input Tokens

159

Est. Cost

$0.0372

Started

8/31/2026, 3:37:38 AM

OR Provider

Minimax

Finish Reason

stop

Generation ID

gen-1788147459-V98rxXDVMYcKp8NbvC8y

Turns (1)

Turn #1

819 in30,828 out27,946 reasoning159 cached31,647 total226.6s

user

You are a graphics engineer who has spent a lot of time finding out why things are slow.

I want a hundred thousand things moving on screen at sixty frames a second, in one HTML file, with no libraries.

What to build

A canvas, 900 × 600.
100,000 particles, each with its own position and velocity, each one individually updated and drawn every frame.
They move through a flow field — a smooth, slowly evolving vector field that pushes them around. Curl, drift, eddies. It should look like weather, or ink in water, not like confetti.
Particles that leave the canvas are recycled back in, so the count never drops.
The field itself must evolve over time. A static field that particles merely settle into is not what I asked for.

On screen, always visible

A live FPS readout, honestly measured — a frame-time average, not a number you decided on.
The particle count, read from the actual data structure holding them, not a constant you typed in the HTML.

House rules

One file. Everything inline — no CDN, no external stylesheet, font, script or image. data: URIs are fine.
No libraries. No WebGL helper you paste in. If you want to use WebGL, write it yourself.
No network at runtime.
No localStorage, sessionStorage, cookies or indexedDB — sandboxed frame, they throw.
Return the file as exactly one fenced html block.

Answer in exactly these sections

The plan

One paragraph, before the file. Your data layout, your draw strategy, and the one thing you expect to be the bottleneck.

The file

The single fenced html block.

The prediction

This is the section I care most about. Be specific and commit to numbers:

FPS I expect on a 2023 laptop, integrated graphics, Chrome — give a single number, not a range wider than about ten.
Where the time goes — roughly what share is update versus draw, and why.
The first thing that breaks if I raise the count to a million.
What you'd need to go ten times faster. Name the technique, not "optimize further".

If you fell short

If you could not do 100,000 at 60fps and shipped fewer, or a cheaper effect than a real flow field, say so here, plainly, and say what you shipped instead. Write Nothing dropped. if you delivered the brief in full.

How this is scored

I am going to open the file and read the FPS counter.

Then I am going to compare it against your prediction. A model that says "about 45fps, fill-rate bound" and measures 43 has understood its own code. A model that says 60 and measures 4 has not, however elegant the code is — and that gap is most of what I am testing here. An honest prediction of a mediocre number beats a confident prediction of a good one.

assistant

The plan

Four Float32Array buffers of length 100,000 (px, py, pvx, pvy) — cache-friendly Structure-of-Arrays, no per-particle object overhead. The flow field is held as a 150 × 100 grid; per frame I resample a 2D value-noise scalar potential onto that grid with a slow time offset, then derive the velocity field by central finite-difference curl of the potential (u = ∂ψ/∂y, v = −∂ψ/∂x). The result is genuinely divergence-free, so particles form eddies instead of piling at sinks. Each particle bilinearly samples the field, blends toward it, integrates, and wraps. Draw strategy: one Path2D of 100,000 moveTo+lineTo pairs (short segments showing instantaneous velocity), a single stroke(), with a rgba(0,0,6,0.16) fillRect over the canvas each frame to give the "ink in water" decay. The bottleneck I expect is the Path2D stroke + alpha blend of 100k short segments on integrated graphics, plus the 200k FFI calls into Canvas2D to build the path.

The file

<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>Flow Field — 100k</title>
<style>
  html, body { margin: 0; padding: 0; background: #000; overflow: hidden; font: 12px/1.5 ui-monospace, Menlo, Consolas, monospace; }
  canvas { display: block; }
  #hud { position: fixed; top: 8px; left: 10px; color: #b8dcff; text-shadow: 0 0 6px #000; pointer-events: none; }
  #hud b { color: #fff; font-weight: 600; }
</style>
</head>
<body>
<canvas id="c" width="900" height="600"></canvas>
<div id="hud">
  <div><b id="fps">--</b> fps &nbsp;·&nbsp; <b id="ft">--</b> ms / frame</div>
  <div>particles: <b id="cnt">--</b></div>
</div>
<script>
(() => {
  'use strict';
  const c = document.getElementById('c');
  const $ = c.getContext('2d', { alpha: false, desynchronized: true });
  const W = c.width, H = c.height;
  const fpsEl = document.getElementById('fps');
  const ftEl  = document.getElementById('ft');
  const cntEl = document.getElementById('cnt');

  // --- particles: Structure-of-Arrays, all 100k individually held & updated ---
  const N = 100000;
  const px  = new Float32Array(N);
  const py  = new Float32Array(N);
  const pvx = new Float32Array(N);
  const pvy = new Float32Array(N);
  for (let i = 0; i < N; i++) {
    px[i]  = Math.random() * W;
    py[i]  = Math.random() * H;
    pvx[i] = (Math.random() - 0.5) * 0.3;
    pvy[i] = (Math.random() - 0.5) * 0.3;
  }
  cntEl.textContent = px.length.toLocaleString();

  // --- flow field: scalar potential -> curl -> (u, v); genuinely divergence-free ---
  const FW = 150, FH = 100;
  const psi = new Float32Array(FW * FH);
  const fU  = new Float32Array(FW * FH);
  const fV  = new Float32Array(FW * FH);
  const FX_INV = (FW - 1) / W;
  const FY_INV = (FH - 1) / H;

  // Fast 2D value noise (hash + smoothstep interp)
  const INV2_32 = 1 / 4294967295;
  function hash2(x, y) {
    let h = (Math.imul(x | 0, 73856093) ^ Math.imul(y | 0, 19349663)) | 0;
    h = Math.imul(h ^ (h >>> 15), 0x5bd1e995);
    return ((h >>> 0) * INV2_32) * 2 - 1;
  }
  function sm(t) { return t * t * (3 - 2 * t); }
  function noise(x, y) {
    const xi = x | 0, yi = y | 0;
    const xf = x - xi, yf = y - yi;
    const u = sm(xf), v = sm(yf);
    const a = hash2(xi,     yi);
    const b = hash2(xi + 1, yi);
    const c = hash2(xi,     yi + 1);
    const d = hash2(xi + 1, yi + 1);
    return (a * (1 - u) + b * u) * (1 - v) + (c * (1 - u) + d * u) * v;
  }

  let T = 0;
  function regen() {
    const sc = 0.075;
    const ts = T * 0.35;
    // 1) sample scalar potential onto the grid; slow time offset = evolving field
    for (let j = 0; j < FH; j++) {
      const yb = j * sc;
      const row = j * FW;
      for (let i = 0; i < FW; i++) {
        psi[row + i] = noise(i * sc + ts, yb - ts * 0.7);
      }
    }
    // 2) curl of the potential -> (u, v), toroidal wrap
    const sc2 = 2.0;
    for (let j = 0; j < FH; j++) {
      const jn = j === 0        ? FH - 1 : j - 1;
      const jp = j === FH - 1   ? 0      : j + 1;
      const rN = jn * FW, rP = jp * FW, rJ = j * FW;
      for (let i = 0; i < FW; i++) {
        const in_ = i === 0      ? FW - 1 : i - 1;
        const ip  = i === FW - 1 ? 0      : i + 1;
        fU[rJ + i] =  (psi[rP + i] - psi[rN + i]) * sc2;
        fV[rJ + i] = -(psi[rJ + ip] - psi[rJ + in_]) * sc2;
      }
    }
  }

  // --- honest rolling-average frame-time meter ---
  const FN = 60;
  const fb = new Float32Array(FN);
  let fi = 0, fc = 0, fs = 0, lt = performance.now();

  // prime the canvas
  $.fillStyle = '#000';
  $.fillRect(0, 0, W, H);

  function frame() {
    const now = performance.now();
    const raw = now - lt;
    lt = now;
    const dt = raw > 100 ? 100 : raw; // ignore tab-switch spikes
    if (fc < FN) { fs += dt; fb[fi] = dt; fc++; }
    else         { fs += dt - fb[fi]; fb[fi] = dt; }
    fi = (fi + 1) % FN;
    const avg = fs / fc;

    T += dt * 0.001;
    regen();

    // --- per-particle update: bilinear sample of field, blend, integrate, wrap ---
    const st = FW;
    const lerp = 0.07;
    for (let i = 0; i < N; i++) {
      const x = px[i], y = py[i];
      const gx = x * FX_INV;
      const gy = y * FY_INV;
      const gi = gx | 0;
      const gj = gy | 0;
      const idx = gj * st + gi;
      const fx = gx - gi;
      const fy = gy - gj;
      const u00 = fU[idx],          u10 = fU[idx + 1];
      const u01 = fU[idx + st],     u11 = fU[idx + st + 1];
      const v00 = fV[idx],          v10 = fV[idx + 1];
      const v01 = fV[idx + st],     v11 = fV[idx + st + 1];
      const u0 = u00 + (u10 - u00) * fx;
      const u1 = u01 + (u11 - u01) * fx;
      const v0 = v00 + (v10 - v00) * fx;
      const v1 = v01 + (v11 - v01) * fx;
      const u  = u0  + (u1  - u0 ) * fy;
      const v  = v0  + (v1  - v0 ) * fy;
      const nvx = pvx[i] + (u - pvx[i]) * lerp;
      const nvy = pvy[i] + (v - pvy[i]) * lerp;
      pvx[i] = nvx;
      pvy[i] = nvy;
      let nx = x + nvx;
      let ny = y + nvy;
      if (nx >= W) nx -= W; else if (nx < 0) nx += W;
      if (ny >= H) ny -= H; else if (ny < 0) ny += H;
      px[i] = nx;
      py[i] = ny;
    }

    // --- draw: fade the previous frame, then a single stroked path of 100k short velocity ticks ---
    $.fillStyle = 'rgba(0, 0, 6, 0.16)';
    $.fillRect(0, 0, W, H);

    $.strokeStyle = 'rgba(170, 215, 255, 0.7)';
    $.lineWidth = 1;
    $.beginPath();
    for (let i = 0; i < N; i++) {
      const x = px[i], y = py[i];
      const vx = pvx[i], vy = pvy[i];
      $.moveTo(x, y);
      $.lineTo(x + vx * 1.4, y + vy * 1.4);
    }
    $.stroke();

    fpsEl.textContent = (1000 / avg).toFixed(1);
    ftEl.textContent  = avg.toFixed(2);

    requestAnimationFrame(frame);
  }
  requestAnimationFrame(frame);
})();
</script>
</body>
</html>

The prediction

FPS I expect on a 2023 laptop, integrated graphics, Chrome: 55. I would be unsurprised at 50, pleasantly surprised at 65.
Where the time goes. Roughly 30% update, 55% draw, 15% everything else (RAF, GC, compositor, browser). The update is the 100k bilinear-sample inner loop (~3–4 ms) plus the field regen (~1.5–2 ms: one value-noise call per cell, then a finite-difference curl — no per-particle noise). The draw is dominated by the Path2D build and the stroke: 200k host-object calls into Canvas2D just to construct the path (~3–5 ms), then a single stroke of 100k short line segments with alpha blending (~3–5 ms), then the 540k-pixel trail-fade fillRect (~0.5–1 ms). That share is because the per-particle work on the JS side is cheap, but the GPU has to scan-convert and blend 100k segments every frame.
First thing that breaks at a million particles: the Path2D draw. The 200k FFI calls become 2M (~30–50 ms just to build the path), and the stroke of 1M short alpha-blended segments burns fill rate on the integrated GPU (~50–80 ms). The JS update loop also scales ~10× to 30–40 ms. Total ≈ 110–170 ms, i.e. ~6–9 fps. The first thing visibly choking, before the update loop, is the stroke's per-segment blend cost.
Ten times faster: WebGL with instanced point sprites, updated in the vertex shader. Feed positions/velocities as a vertex buffer, sample the noise field in the vertex shader, integrate in-shader, draw 100k (or 1M) points in a single drawArraysInstanced call with no JS per-particle work and no FFI. That is the technique — not "optimize more".

If you fell short

Nothing dropped.

Attachments

minimax/minimax-m3 — page