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Unleashing Mojo: Accelerating K-Nearest Neighbor Learning
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DOI：10.1007/978-3-031-99879-9_3.png)
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Abstract
En 中文
New compiled languages such as Mojo, equipped with native SIMD kernels and explicit thread-level parallelism, promise to raise the performance ceiling that pure-Python machine learning (ML) pipelines often hit. We therefore re-implemented brute-force k-nearest neighbours (k-NN) in Mojo-combining 64-byte-aligned buffers, vectorized Euclidean kernels and lock-free thread pools-and benchmarked it against scikit-learn's canonical Python/Cython implementation. The evaluation spans eleven datasets that vary along three orthogonal axes: sample count (102 -106), dimensionality (4-3 072) and structural regularity (MNIST, CIFAR and synthetic blobs). Each experiment was repeated on a laptop-class six-core CPU and a workstation-class sixteen-core CPU to expose hardware effects. Mojo accelerates structured, cache-friendly workloads by five- to ninety-fold and sustains speed-ups of up to 60% on medium-scale image sets even in single-core mode. The margin shrinks to at most 10% on million-point or ultra-wide tables and can invert on lowend laptops when memory bandwidth dominates. These findings show that Mojo is a convenient accelerator for medium-sized, latency-sensitive applications, e.g. recommendation engines and edge analytics-whereas scikit-learn remains the pragmatic choice once datasets outgrow on-chip resources or when ecosystem maturity outweighs raw speed. All code, timing logs, and hardware counters are available in an open repository to facilitate reproduction and further optimization.
Keywords:
KNN
Mojo
SIMD
Benchmarking
scikit-learn
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