58 lines
1.8 KiB
Python
58 lines
1.8 KiB
Python
import numpy as np
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from time import time
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class MatrixBench():
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def __init__(self, iters: int, size: int):
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self.iters = iters
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self.size = size
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self.average = 0
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self.start_time = 0
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self.end_time = 0
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self.elapsed_time = 0
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self.flops:float = 0.0
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def create_matrices(self):
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# Create num x num matrix and an num x 1 vector with random floating point numbers
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self.matrix_A = np.random.rand(self.size, self.size).astype(np.float32)
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self.matrix_B = np.random.rand(self.size).astype(np.float32)
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def do_operation(self):
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# Do Floating Point Operation
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self.start_time = time()
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_ = np.linalg.solve(self.matrix_A, self.matrix_B)
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self.end_time = time()
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def do_calculate(self):
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# Calculate Floating Point Operation per Second (FLOPS)
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self.elapsed_time = self.end_time - self.start_time
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self.num_operations = (2 / 3) * self.size **3
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self.flops = self.num_operations / self.elapsed_time
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self.average += self.flops
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def run(self):
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print("# Linpack-like Matrix-based Floating Point Benchmark #")
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print("# Using NumPy as Core #")
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print(f"# Matrix Size : {self.size} #")
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print(f"# Iterations : {self.iters} #")
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print("# Please Wait #")
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for i in range(1, self.iters+1):
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self.create_matrices()
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self.do_operation()
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self.do_calculate()
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self.avg_flops: float = (self.average/self.iters) / 1000000
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print(f"=> Result : {self.avg_flops:.2f} MFLOPS")
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def main():
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# Adjust Matrix Size
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size = 512
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# Adjust Iteration Time
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iters = 1000
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# Run Benchmark
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bench = MatrixBench(iters=iters, size=size)
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bench.run()
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main()
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