Intel MKL LINPACK 测试表明性能太大

Question

我在 Intel Core i7-14700K 处理器上运行了 Intel MKL LINPACK 测试，得到了 557 GFLOPS 的峰值性能，这似乎相当不现实。

Size   LDA    Align.  Average  Maximal
1000   1000   4       155.1099 216.8890
2000   2000   4       425.5128 459.9769
5000   5008   4       379.0532 393.7132
10000  10000  4       427.9537 435.6706
15000  15000  4       426.8314 427.5827
18000  18008  4       545.7857 549.8816
20000  20016  4       553.3485 553.5723
22000  22008  4       548.1379 552.2941
25000  25000  4       549.4231 555.0353
26000  26000  4       550.3011 554.8746
27000  27000  4       542.6011 542.6011
30000  30000  1       532.8780 532.8780
35000  35000  1       534.7904 534.7904
40000  40000  1       557.7524 557.7524
45000  45000  1       557.3916 557.3916

1000 大小的 155 GFLOPS 值似乎合理，但 557 GFLOPS 太高了。有人知道它是如何发生的吗？

我使用了以下套件：

http://registrationcenter-download.intel.com/akdlm/irc_nas/9752/l_mklb_p_2018.3.011.tgz

使用以下命令开始测试：

./runme_xeon64

Answer 1

我可以验证 14700k 的这些结果。使用英特尔 oneAPI 数学内核和 numpy，我能够在 python 中实现 550-650 GFLOPS，这会带来巨大的开销。需要明确的是，这是在所有内核上运行的，因为英特尔 blas 库经过了非常好的优化。

import numpy as np
from time import time_ns

def benchCPU(A, B, C):

    for i in range(0, 20):
        print("Iteration: " + "%d" % i)
        C = np.matmul(C, A)
        C = np.matmul(C, B)
        C = C/np.max(C)
    return 0

if __name__ == '__main__':
    samples = 7000
    A = np.random.rand(samples, samples).astype(np.float32)
    B = np.random.rand(samples, samples).astype(np.float32)
    C = np.random.rand(samples, samples).astype(np.float32)

    t1 = time_ns()
    t2 = time_ns()
    tdly = t2 - t1

    C = np.matmul(A, B)
    print("CPU Test")
    t1 = time_ns()
    benchCPU(A, B, C)
    t2 = time_ns()

    t_cpu = t2 - t1 - tdly

    operations = 2*20*(2*samples**3 - samples**2) # Matrix Multiplication Operations take 2n^3 - n^2, there are 20 iterations which each do 2 operations, max is considered negligible

    print("CPU Throughput: " + "%.3f" % ((operations/(t_cpu*1e-9))*1e-12) + " TFLOPS")