OpenBLAS vs. MKL NumPy

Preface

When working with scientific computing or machine learning in Python, NumPy is one of the core dependencies for numerical operations. Behind the scenes, NumPy relies on optimized linear algebra libraries—primarily OpenBLAS or Intel MKL (Math Kernel Library)—to accelerate matrix multiplications, convolutions, decompositions, and other dense numerical routines. This article provides a clear comparison between the OpenBLAS-based and MKL-based NumPy distributions, helping you make an informed choice for your development environment.

BLAS and LAPACK

NumPy delegates its heavy numerical computation to low-level libraries such as BLAS (Basic Linear Algebra Subprograms) and LAPACK (Linear Algebra PACKage). These libraries provide optimized implementations of common mathematical operations.

• BLAS: Vector and matrix operations
• LAPACK: Matrix factorizations and solvers built on top of BLAS

The performance of NumPy often depends more on the BLAS/LAPACK backend than on NumPy itself.

OpenBLAS: Open-Source and Widely Compatible

OpenBLAS is an open-source, actively maintained implementation of BLAS and LAPACK. It is the default backend for many community-driven builds (e.g., Linux distributions, Python from source).

Advantages

• Open-source & community-supported No vendor lock-in and easy to distribute.
• Good performance across architectures Particularly optimized for x86, ARM, and PowerPC.
• Lightweight and easy to build Ideal for environments requiring portability.

Limitations

• Not always the fastest for Intel CPUs Its optimizations may trail behind machine-specific tuning.
• Threading behavior can be inconsistent Some workloads exhibit oversubscription or suboptimal thread usage.

MKL: Intel’s High-Performance Option

MKL (Math Kernel Library) is Intel’s proprietary, highly optimized numerical library. Anaconda’s NumPy distribution, for example, is compiled against MKL.

Advantages

• Excellent performance on Intel hardware MKL includes CPU-specific optimizations and vectorization.
• Consistent multi-threading Thread scheduling is generally more stable and predictable.
• Broad functionality Includes FFTs, sparse solvers, and advanced math routines beyond BLAS/LAPACK.

Limitations

• Not fully open-source Redistribution and compilation flexibility are limited.
• Performance may vary on non-Intel CPUs MKL can fall back to less optimized code paths (e.g., “generic” mode).

Performance Comparison

Although performance depends heavily on workload and hardware, the general trends are:

If your workflow includes heavy numerical operations (e.g., ML preprocessing, scientific simulations), MKL usually delivers better performance on Intel systems.

Deployment

urob/numpy-mkl provides binary wheels for NumPy and SciPy, linked to Intel’s high-performance oneAPI Math Kernel Library for Intel CPUs.

Experiments

CPU Information

CPU: Intel(R) Xeon(R) Platinum 8336C CPU @ 2.30GHz

Physical cores: 64

Logical processors: 128

Benchmark

import numpy as np, time, platform

N        = 8000  # 16000 24000 32000
DTYPE    = np.float64
REPEAT   = 7
WARMUP   = 2

def show_blas():
    np.__config__.show()

def benchmark():
    A = np.random.randn(N, N).astype(DTYPE)
    B = np.random.randn(N, N).astype(DTYPE)

    # warm up
    for _ in range(WARMUP):
        _ = np.dot(A, B)

    times = []
    for _ in range(REPEAT):
        t0 = time.perf_counter()
        C = np.dot(A, B)
        t1 = time.perf_counter()
        times.append(t1 - t0)
    best = np.median(times)
    gflops = 2 * N**3 / best * 1e-9
    return best, gflops, times


if __name__ == '__main__':
    print('Platform :', platform.processor())
    show_blas()
    dt, gflops, all_t = benchmark()
    print(f'N: {N}')
    print(f'Raw times (s): {[f"{t:.3f}" for t in all_t]}')
    print(f'Median  : {dt:.3f} s   ≈ {gflops:.1f} GFlops')

Performance Highlights

OpenBLAS vs. MKL

Overall: MKL clearly outperforms OpenBLAS across all matrix sizes in both time and GFLOPS.

Speed

MKL is ~20–35% faster at N=8000 and maintains a strong advantage as N increases. Example:

• N=8000 → MKL: 0.582 s, OpenBLAS: 0.725 s
• N=32000 → MKL: 29.133 s, OpenBLAS: 37.198 s

Throughput (GFLOPS)

MKL delivers consistently higher throughput, with about 300–600 GFLOPS advantage across sizes.

• N=8000 → MKL: 1758 GFLOPS, OpenBLAS: 1413 GFLOPS
• N=24000 → MKL: 2362 GFLOPS, OpenBLAS: 1698 GFLOPS

MKL vs. urob MKL

Overall: urob MKL provides slight improvements at small matrix sizes and performance very close to standard MKL overall.

Speed

urob MKL is faster at smaller N and comparable at larger N:

• N=8000 → MKL: 0.582 s, urob MKL: 0.500 s
• N=16000 → MKL: 3.969 s, urob MKL: 3.995 s (nearly identical)
• N=32000 → MKL: 29.133 s, urob MKL: 29.992 s (small difference)

Throughput (GFLOPS)

urob MKL shows a small advantage in GFLOPS at smaller sizes but converges toward MKL at larger sizes:

• N=8000 → urob MKL: 2048 GFLOPS, MKL: 1758 GFLOPS
• N=24000 → urob MKL: 2315 GFLOPS, MKL: 2362 GFLOPS

Choice

Choose OpenBLAS if:

• You need open-source and broad platform compatibility
• You work on ARM, non-Intel CPUs, or minimal Linux environments
• You prefer lighter deployments and simpler distribution

Choose MKL if:

• You use an Intel CPU and prioritize the best performance
• You work in data science, AI, or heavy numerical computation
• You use Conda, where MKL integration is seamless

Conclusion

Both OpenBLAS and MKL deliver powerful numerical acceleration for NumPy, but they shine in different scenarios. OpenBLAS provides portability, open-source accessibility, and solid performance across architectures. MKL, on the other hand, offers top-tier, hardware-tuned performance that excels in multi-threaded and computationally intensive workloads.

Understanding these differences allows you to choose the right NumPy distribution for your environment—whether you’re optimizing for speed, compatibility, or reproducibility.

Resources

Intel/MKL

urob/numpy-mkl