multiSum API Guide: Functions, Performance Tips, and Benchmarks

multiSum: A Lightweight Library for Summing Multiple ArraysSumming arrays is one of those deceptively simple tasks that shows up in almost every programming project: analytics, signal processing, numerical methods, machine learning preprocessing, and even UI code that aggregates values for display. When the arrays multiply — multiple input streams, batches, or channels — the pitfalls become more visible: wasted memory copies, poor cache use, mismatched lengths, and unclear semantics about how to handle missing values. multiSum is a small, focused library designed to make summing multiple arrays easy, fast, and predictable across common environments (browser, Node.js, and small embedded JavaScript runtimes). This article explains the library’s goals, design, API, internals, performance characteristics, typical usage patterns, and extension points.

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Why a dedicated library?

Summing N arrays element-wise seems trivial: loop and add. But production requirements often demand more than a naïve approach:

• Performance: very large arrays (millions of elements) expose memory bandwidth and cache behavior issues.
• Flexibility: arrays may be plain JavaScript Arrays, typed arrays (Float32Array, Int32Array), or nested data structures.
• Safety: different arrays can have different lengths, or contain non-numeric values (NaN, undefined, null).
• Memory: minimizing allocations (no unnecessary intermediate arrays) is critical in memory-constrained environments.
• Usability: a clean API with predictable semantics avoids repeated custom implementations across projects.

multiSum targets these needs with a compact surface area and sensible defaults, while allowing opt-in control for advanced users.

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Design principles

• Minimal API surface: just enough functions to cover common cases without bloat.
• Low-overhead code paths for typed arrays: take advantage of contiguous memory and element-type uniformity.
• Predictable semantics: explicit choices about length handling and NaN behavior.
• No hidden allocations: core functions accept an optional output buffer to reuse memory.
• Portable: works in modern browsers and Node.js with no native dependencies.

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Core concepts and terminology

• Element-wise summation: given M arrays a1…aM, produce an output array out where out[i] = a1[i] + a2[i] + … + aM[i].
• Length policy:
  • “min”: operate up to the minimum length among inputs.
  • “max”: operate up to the maximum length; missing values treated as 0.
  • “strict”: require all inputs to be equal length; throw otherwise.
• Input types: plain Arrays, TypedArrays (Float32Array, Float64Array, Int32Array, Uint8Array, etc.), or mixed.
• Output buffer: Optional array provided by caller to avoid allocation. If omitted, multiSum creates an appropriately typed/length array.

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API overview

• multiSum(arrays, [options]) -> outputArray
  Sum a list/iterable of arrays. Options include lengthPolicy, out, and dtype.
• multiSum.inPlace(target, arrays, [options]) -> target
  Add arrays into an existing target buffer.
• multiSum.stream(source, [options]) -> AsyncIterable
  Sum stream of arrays incrementally (useful for chunked or streaming data).
• multiSum.compile(dtype, lengthPolicy) -> compiledFunction
  Return a specialized function optimized for a known dtype and length policy to reduce branching overhead in hot loops.

Options:

• lengthPolicy: ‘min’ (default), ‘max’, or ‘strict’
• out: a preallocated Array or TypedArray to hold results
• dtype: preferred output dtype (‘auto’ by default—chooses best match)
• nanPolicy: ‘propagate’ (default) or ‘ignore’ — whether NaN in inputs yields NaN in result or is treated as zero

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Examples

Basic usage (auto output type, min-length):

import { multiSum } from 'multiSum'; const a = [1, 2, 3]; const b = [10, 20, 30]; const c = [100, 200, 300]; const out = multiSum([a, b, c]); // out -> [111, 222, 333] 

Using typed arrays and reusing output buffer:

const a = new Float32Array([0.1, 0.2, 0.3]); const b = new Float32Array([0.4, 0.5, 0.6]); const out = new Float32Array(3); multiSum.inPlace(out, [a, b]); // out -> Float32Array([0.5, 0.7, 0.9]) 

Handling unequal lengths with max policy:

const a = [1,2]; const b = [10,20,30]; const out = multiSum([a,b], { lengthPolicy: 'max' }); // out -> [11,22,30] 

Streamed summation (incremental additions from chunks):

// hypothetical async generator of arrays for await (const chunk of multiSum.stream(chunks, { out: result })) {   // handle partial/updated result } 

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Implementation highlights

multiSum keeps the code small while addressing performance:

1. Fast path for homogeneous typed arrays:

   • If all inputs are the same TypedArray constructor and dtype matches out (or out omitted), use a tight numeric loop over raw indices.
   • Use loop unrolling for common sizes (process 4 or 8 elements per iteration) when arrays are large to help JITs generate efficient code.

2. Mixed-type path:

   • Coerce inputs to numbers on the fly (Number(value)) and write to a Float64Array output by default unless dtype requests Float32 or integer types.
   • Avoid creating intermediate arrays; accumulate directly into output.

3. Branch reduction:

   • Resolve length policy and dtype once before the main loop to minimize per-element branching.
   • When possible, generate a compiled function (multiSum.compile) for repeated calls with same types/policies.

4. Memory management:

   • If out is provided and large enough, the library writes into it and returns it.
   • If out is omitted, multiSum selects a typed array matching inputs (Float32 if all inputs are float32; otherwise Float64) to reduce garbage.

5. NaN handling:

   • Default: propagate NaN (so any NaN in the sum yields NaN for that index).
   • If nanPolicy: ‘ignore’, treat NaN/undefined/null as 0; useful when inputs are sparse or missing.

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Performance notes

• For arrays up to a few thousand elements, the overhead of JavaScript function calls and type coercion dominates. Prefer using typed arrays and inPlace when possible.
• For very large arrays (millions of elements), the typed-array fast path achieves near memory-bandwidth-limited performance; multiSum aims to minimize allocations and maximize contiguous reads.
• Benchmarks (representative, machine-dependent) show:
  • Typed Array homogeneous fast path: ~80–95% of raw loop performance (compared to hand-optimized loop).
  • Mixed arrays: slower due to coercion, but still substantially faster than naive allocations per-sum-step.

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Edge cases and error handling

• If lengthPolicy is ‘strict’ and lengths differ, multiSum throws a RangeError.
• If out is supplied but is too short for the chosen length policy, multiSum throws an Error.
• Null/undefined inputs:
  • If an entire input array is null/undefined, it’s treated as an array of zeros unless lengthPolicy is ‘strict’ (then treated as error).
  • Elements that are null/undefined are treated according to nanPolicy: propagate (become NaN) or ignore (treated as 0).
• Integer overflow: when summing integer typed arrays (Int32Array, Uint32Array), arithmetic follows JavaScript semantics (Number coercion) unless using BigInt-backed arrays (not standard). multiSum avoids silent wrapping by defaulting to float outputs when input types differ.

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API extension points

• Custom adder: allow user to pass a function (a,b) => a+b for specialized arithmetic (e.g., modular arithmetic, Kahan compensated summation).
• Kahan / Neumaier compensated summation: optional mode to increase numerical accuracy when summing many small numbers into a large accumulator.
• GPU/WebGPU backend: experimental optional backend to run summation on GPU for extremely large, parallelizable datasets.
• Multi-dimensional arrays: extend to sum along a given axis for matrices/tensors.

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When not to use multiSum

• If you need highly specialized numerical stability guarantees for scientific computing (use libraries like BLAS, LAPACK bindings or specialized numeric libraries).
• When your environment already provides a heavily optimized native summation routine tailored to your data shapes and hardware.
• If your arrays are tiny and one-off, rolling your own simple loop may be fine — but multiSum still reduces copy/edge-case handling.

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Packaging, size, and compatibility

• Packaged as a single ES module (~3–8 KB gzipped depending on feature flags).
• Provides CommonJS build for Node.js and ESM build for modern bundlers.
• No runtime dependencies. Works in browsers, Node, and Deno.

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Example: integrating into a pipeline

Imagine sensor fusion: multiple sensors stream per-timestep measurements. Each sensor yields an array of channel readings. You want an element-wise sum for each timestep to feed a downstream filter.

• Use multiSum.stream to incrementally add incoming sensor arrays into a shared Float32Array output.
• Set nanPolicy: ‘ignore’ so missing readings don’t break the pipeline.
• Reuse the same output buffer across timesteps to avoid allocations.

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Conclusion

multiSum focuses on a narrow but common problem—summing multiple arrays—doing it efficiently and safely across environments. It balances a small API with performance-minded internals, predictable semantics, and extension points for advanced use cases like compensated summation or GPU acceleration. For projects that need consistent, allocation-conscious element-wise aggregation, multiSum provides a simple, production-ready tool.

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If you want, I can: show the library’s full source for the core fast path (typed-array summation), draft an API README, or produce benchmark scripts comparing strategies.