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Performance: Optimize argmax loop in decoder #155

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Performance: Optimize argmax loop in decoder #155

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3 changes: 3 additions & 0 deletions .jules/bolt.md
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Original file line number Diff line number Diff line change
Expand Up @@ -13,3 +13,6 @@ Action: Apply loop unrolling for max reductions in high-frequency typed array op
## 2024-11-20 - Softmax math.exp 8x unrolling with local var cache
Learning: Unrolling the `Math.exp` accumulation loop to 8x and caching the multiplication `(tokenLogits[i] - maxLogit) * invTemp` into local variables before passing to `Math.exp` yields a measurable performance improvement (~4%) over the previous 4x unrolled implementation in the V8 engine, by reducing property access and allowing better instruction-level parallelism.
Action: Utilize 8x loop unrolling paired with local variable caching for tight floating-point accumulation loops over TypedArrays.
## 2024-12-04 - Direct TypedArray loop iteration
Learning: In V8, pure branch loops (like an unrolled `argmax`) that perform frequent read-only sequential comparisons over TypedArrays are faster when using direct index access (`arr[i]`) rather than manually caching values into local variables first (`const v0 = arr[i]`). Direct access avoids the overhead of forced assignments on every iteration.
Action: Prefer direct array element access in simple, high-frequency branch loops without complex math operations.
30 changes: 11 additions & 19 deletions src/parakeet.js
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Expand Up @@ -808,26 +808,18 @@ export class ParakeetModel {
for (; i < tLen % 8; i++) {
if (tokenLogits[i] > maxLogit) { maxLogit = tokenLogits[i]; maxId = i; }
}
// Optimization: Reading values into local variables (v0 to v7) within the
// unrolled block before sequential comparisons avoids redundant TypedArray
// index lookups and bounds-checking overhead in V8 when a new max is found.
// Optimization: For pure branch loops like argmax, direct array access is faster
// than caching values to local variables, as it avoids forced assignment overhead
// on every iteration in V8.
for (; i < tLen; i += 8) {
const v0 = tokenLogits[i];
const v1 = tokenLogits[i+1];
const v2 = tokenLogits[i+2];
const v3 = tokenLogits[i+3];
const v4 = tokenLogits[i+4];
const v5 = tokenLogits[i+5];
const v6 = tokenLogits[i+6];
const v7 = tokenLogits[i+7];
if (v0 > maxLogit) { maxLogit = v0; maxId = i; }
if (v1 > maxLogit) { maxLogit = v1; maxId = i + 1; }
if (v2 > maxLogit) { maxLogit = v2; maxId = i + 2; }
if (v3 > maxLogit) { maxLogit = v3; maxId = i + 3; }
if (v4 > maxLogit) { maxLogit = v4; maxId = i + 4; }
if (v5 > maxLogit) { maxLogit = v5; maxId = i + 5; }
if (v6 > maxLogit) { maxLogit = v6; maxId = i + 6; }
if (v7 > maxLogit) { maxLogit = v7; maxId = i + 7; }
if (tokenLogits[i] > maxLogit) { maxLogit = tokenLogits[i]; maxId = i; }
if (tokenLogits[i+1] > maxLogit) { maxLogit = tokenLogits[i+1]; maxId = i + 1; }
if (tokenLogits[i+2] > maxLogit) { maxLogit = tokenLogits[i+2]; maxId = i + 2; }
if (tokenLogits[i+3] > maxLogit) { maxLogit = tokenLogits[i+3]; maxId = i + 3; }
if (tokenLogits[i+4] > maxLogit) { maxLogit = tokenLogits[i+4]; maxId = i + 4; }
if (tokenLogits[i+5] > maxLogit) { maxLogit = tokenLogits[i+5]; maxId = i + 5; }
if (tokenLogits[i+6] > maxLogit) { maxLogit = tokenLogits[i+6]; maxId = i + 6; }
if (tokenLogits[i+7] > maxLogit) { maxLogit = tokenLogits[i+7]; maxId = i + 7; }
}

// Compute maxVal (scaled) only if needed for softmax stability or logProbs
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