Run Detail
completeddeepseek-v4.1-flashRecorded for these videos
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Provider
openrouter
Challenge Version
v1.0
Turns
1
Duration
211.6s
Total Tokens
52,149
Input Tokens
688
Output Tokens
51,461
Reasoning Tokens
47,911
Reasoning Effort
default
Est. Cost
$0.0310
Started
9/10/2026, 11:02:42 AM
OR Provider
DeepSeek
Finish Reason
stop
Generation ID
gen-1789038162-XMLJdgdifgEkaLbkBFzH
Turns (1)
Turn #1
688 in51,461 out47,911 reasoning52,149 total211.6s
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 orindexedDB— sandboxed frame, they throw. - Return the file as exactly one fenced
htmlblock.
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 parallel Float32Arrays hold x, y, vx, vy for 100 000 particles (structure-of-arrays, walked in lockstep so the hot loop is a streaming read with no object derefs). The field is 4 plane waves — each one the curl of a stream function, so the sum is genuinely divergence-free: v = Σ Cᵢ·cos(kᵢ·x + ωᵢt), with Cᵢ ⊥ kᵢ. In the loop each term costs one multiply-add pair plus one lookup into a 2048-entry Float32Array of cosine values — there is not a single Math.sin/Math.cos call per particle. Velocity is blended toward the field (τ ≈ 70 ms) so particles carry inertia, then integrated, wrapped toroidally to recycle escapees, converted to a speed index, and written as one 32-bit store into a reused ImageData buffer. Each frame that buffer is first decayed ×0.75 globally using a packed-byte trick (v - ((v & 0x00FCFCFC) >>> 2) — no per-channel unpacking), which is what makes the trails, then blitted with a single putImageData. The one thing I expect to be the bottleneck is the per-particle field evaluation + scatter write, i.e. the CPU advection loop, not the blit.
The file
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="utf-8">
<title>100,000 particles · evolving curl flow field</title>
<style>
html, body { margin:0; height:100%; background:#04060a; }
body {
display:flex; align-items:center; justify-content:center;
color:#7f93a8;
font:12px/1.5 ui-monospace, SFMono-Regular, Menlo, Consolas, monospace;
}
#app { display:flex; flex-direction:column; gap:9px; }
canvas { display:block; background:#000; border:1px solid #131c26; }
#hud { display:flex; gap:26px; font-size:11px; letter-spacing:.14em; text-transform:uppercase; }
#hud b { color:#dff0ff; font-weight:600; font-variant-numeric:tabular-nums; }
</style>
</head>
<body>
<div id="app">
<canvas id="cv" width="900" height="600"></canvas>
<div id="hud">
<span>fps <b id="fps">--</b></span>
<span>particles <b id="cnt">--</b></span>
</div>
</div>
<script>
(function () {
"use strict";
var W = 900, H = 600, AREA = W * H;
var N = 100000;
var cv = document.getElementById("cv");
var ctx = cv.getContext("2d", { alpha: false });
var img = ctx.createImageData(W, H);
/* one 32-bit view over the same bytes: LE => 0xAABBGGRR */
var buf = new Uint32Array(img.data.buffer, img.data.byteOffset, AREA);
/* ---------------- particle state: 4 flat arrays ---------------- */
var px = new Float32Array(N), py = new Float32Array(N);
var vx = new Float32Array(N), vy = new Float32Array(N);
for (var i = 0; i < N; i++) {
px[i] = Math.random() * W;
py[i] = Math.random() * H;
vx[i] = (Math.random() - 0.5) * 40;
vy[i] = (Math.random() - 0.5) * 40;
}
/* ---------------- cosine table: kills all trig in the hot loop ---------------- */
var TAU = Math.PI * 2;
var TB = 2048, TM = TB - 1, IDX = TB / TAU;
var CTAB = new Float32Array(TB);
for (var k = 0; k < TB; k++) CTAB[k] = Math.cos(k * TAU / TB);
/* ---------------- the flow field ----------------
v(x,t) = sum_i C_i * cos( k_i . x + omega_i t )
with C_i perpendicular to k_i => curl of a stream function => div-free.
Directions rotate, phases drift, amplitudes breathe. */
var NT = 4;
var LAM = [920, 560, 310, 170]; // wavelengths, px
var A0 = [0.30, 1.95, -0.80, 2.70]; // initial wave-vector angles
var SPIN = [0.045, -0.070, 0.110, -0.150]; // rad/s, angle drift
var SPD = [62, 42, 26, 15]; // px/s, velocity amplitude
var OMG = [0.21, -0.33, 0.52, -0.71]; // rad/s, phase drift
var kxI = new Float64Array(NT), kyI = new Float64Array(NT);
var cxv = new Float64Array(NT), cyv = new Float64Array(NT);
var off = new Float64Array(NT);
function field(t) {
for (var i = 0; i < NT; i++) {
var a = A0[i] + SPIN[i] * t;
var ca = Math.cos(a), sa = Math.sin(a);
var kk = (TAU / LAM[i]) * IDX; // table steps per pixel
kxI[i] = kk * ca;
kyI[i] = kk * sa;
var s = SPD[i] * (1 + 0.30 * Math.sin((0.09 + 0.06 * i) * t + 1.3 * i));
cxv[i] = -sa * s; // perpendicular to k
cyv[i] = ca * s;
var o = (OMG[i] * t * IDX) % 204800; // 204800 = 100 * TB, so phase-exact
if (o < 0) o += 204800;
off[i] = o + 409600; // keep the index positive => |0 == floor
}
}
/* ---------------- speed -> colour ---------------- */
var PAL = new Uint32Array(256);
for (var p = 0; p < 256; p++) {
var sN = p / 255, r, g, b;
if (sN < 0.55) {
var u1 = sN / 0.55;
r = 2 + 22 * u1; g = 8 + 50 * u1; b = 20 + 92 * u1;
} else {
var u2 = (sN - 0.55) / 0.45;
r = 24 + 86 * u2; g = 58 + 112 * u2; b = 112 + 113 * u2;
}
PAL[p] = (255 << 24) | ((b & 255) << 16) | ((g & 255) << 8) | (r & 255);
}
/* ---------------- main loop ---------------- */
var fpsEl = document.getElementById("fps");
var cntEl = document.getElementById("cnt");
var hist = new Float64Array(60);
var hi = 0, hn = 0;
var lastT = performance.now(), startT = lastT, hudAt = 0;
function tick(now) {
var raw = now - lastT;
lastT = now;
hist[hi] = raw < 100 ? raw : 100; // ms; clamp tab-switch spikes
hi = (hi + 1) % 60;
if (hn < 60) hn++;
var dt = raw * 0.001;
if (dt < 0.001) dt = 0.001; else if (dt > 0.05) dt = 0.05;
field((now - startT) * 0.001);
var b = buf, i, v;
/* 1. ink decays: every channel x0.75 in one packed pass */
for (i = 0; i < AREA; i++) {
v = b[i];
b[i] = 0xFF000000 | (v - ((v & 0x00FCFCFC) >>> 2));
}
/* 2. advect + deposit one pixel per particle */
var k0x = kxI[0], k0y = kyI[0], C0x = cxv[0], C0y = cyv[0], O0 = off[0];
var k1x = kxI[1], k1y = kyI[1], C1x = cxv[1], C1y = cyv[1], O1 = off[1];
var k2x = kxI[2], k2y = kyI[2], C2x = cxv[2], C2y = cyv[2], O2 = off[2];
var k3x = kxI[3], k3y = kyI[3], C3x = cxv[3], C3y = cyv[3], O3 = off[3];
var CT = CTAB, M = TM, P = PAL;
var al = 1 - Math.exp(-dt / 0.07); // inertia blend (frame-rate independent)
var be = 1 - al;
for (i = 0; i < N; i++) {
var x = px[i], y = py[i];
var a0 = CT[((x * k0x + y * k0y + O0) | 0) & M];
var a1 = CT[((x * k1x + y * k1y + O1) | 0) & M];
var a2 = CT[((x * k2x + y * k2y + O2) | 0) & M];
var a3 = CT[((x * k3x + y * k3y + O3) | 0) & M];
var fx = C0x * a0 + C1x * a1 + C2x * a2 + C3x * a3;
var fy = C0y * a0 + C1y * a1 + C2y * a2 + C3y * a3;
var u = vx[i] * be + fx * al;
var w = vy[i] * be + fy * al;
vx[i] = u; vy[i] = w;
var nx = x + u * dt, ny = y + w * dt;
if (nx < 0) nx += W; else if (nx >= W) nx -= W; // recycle at the walls
if (ny < 0) ny += H; else if (ny >= H) ny -= H;
px[i] = nx; py[i] = ny;
var sp = (u * u + w * w) * 0.0155;
if (sp > 255) sp = 255;
b[((ny | 0) * W) + (nx | 0)] = P[sp | 0];
}
ctx.putImageData(img, 0, 0);
if (now - hudAt > 200) {
hudAt = now;
var sum = 0;
for (i = 0; i < hn; i++) sum += hist[i];
fpsEl.textContent = (1000 / (sum / hn)).toFixed(1);
cntEl.textContent = String(px.length);
}
requestAnimationFrame(tick);
}
cntEl.textContent = String(px.length);
requestAnimationFrame(tick);
})();
</script>
</body>
</html>
The prediction
FPS I expect: 60, vsync-capped, on a 2023 laptop with integrated graphics in Chrome. Raw frame cost is about 7–9 ms, so this is not a marginal 60 — there's roughly 2× headroom before it would fall to 30.
Where the time goes. Roughly 55–60 % update, 40 % draw, and the split is deliberate because I made the draw side of the particle loop cheap (one 32-bit store) and paid for it in the update:
- Advection + field, ~3.5–4 ms (~50 %). 100 000 × (4 index computations, 4 table reads, 8 multiply-adds, blending, wrapping, one scatter store) ≈ 7 M ops and ~1.2 M array reads on a single core. This is the piece that scales linearly with the particle count and dominates everything else. Because the whole loop is branch-predictable and sequentially streams four Float32Arrays, TurboFan compiles it well — but 100 k iterations of this shape is genuinely milliseconds, not microseconds.
- Trail decay, ~1–1.5 ms (~18 %). 540 000 pixels, load +
and/shift/sub/or+ store. Pure memory bandwidth (~4.3 MB read+write per frame), which is why the bit-packed trick matters: unpacking to fourUint8ClampedArraywrites would roughly triple this. putImageData, ~0.7–1 ms (~12 %). 2.16 MB CPU→canvas per frame.- Everything else (~0.5 ms):
field()(16 trig calls per frame), the rAF callback, the HUD write four times a second.
The one number I'd watch: if the machine is on battery, or Chrome falls back to software canvas rasterization, the decay pass and the blit both get slower and the frame time roughly doubles — which is exactly the 30 fps cliff.
The first thing that breaks at a million. The per-particle advection loop, not the drawing. 10× the particles is 10× that ~4 ms cost, so it alone becomes ~40–50 ms/frame (≈20 fps), and no amount of render-side cleverness touches it. The second failure is right behind it: 1 M single-pixel deposits into a 540 000-pixel buffer is 2× oversubscription, so the plot pass becomes meaningless smearing as well as slow (~10 ms of near-random writes). The canvas simply cannot resolve a million distinct things at 900 × 600.
What I'd need to go 10× faster. Move the simulation off the CPU entirely: GPGPU particle integration with ping-pong float render targets (WebGL2 float textures, or transform feedback), one texel per particle, position and velocity read and rewritten inside a fragment shader, then drawn with gl.POINTS. The flow field evaluates per-texel on the GPU for free, and the CPU issues three draw calls and uploads nothing. That's the technique — the JS loop cannot be optimized into this regime, it has to be relocated.
If you fell short
Nothing dropped. 100,000 real particles with per-particle position and velocity state, all updated and drawn every frame; the field is a genuine divergence-free curl field that rotates, drifts and breathes over time; escapees are wrapped back in so the count is constant; and both readouts are live — the FPS is the mean of the last 60 measured frame deltas, and the count is px.length, read from the array that actually holds them.
Attachments
deepseek/deepseek-v4.1-flash — page