Update Matrix-based Benchmark

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Alauddin Maulana Hirzan 2024-06-16 20:13:17 +07:00
parent c8212ebfda
commit 2483e5b741
Signed by: maulanahirzan
GPG key ID: 484DAC952787FA13
4 changed files with 75 additions and 3 deletions

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@ -1,5 +1,4 @@
import numpy as np import numpy as np
from tqdm import tqdm
from time import time from time import time
class MatrixBench(): class MatrixBench():
@ -32,7 +31,9 @@ class MatrixBench():
def run(self): def run(self):
print("# Linpack-like Matrix-based Floating Point Benchmark #") print("# Linpack-like Matrix-based Floating Point Benchmark #")
for i in tqdm(range(self.iters), desc="Running Matrix-based Benchmark..."): print("# Using NumPy as Core #")
for i in range(0, self.iters+1):
print(f"=> Epoch #{str(i).zfill(len(str(self.iters)))}", end="\r")
self.create_matrices() self.create_matrices()
self.do_operation() self.do_operation()
self.do_calculate() self.do_calculate()

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@ -0,0 +1,7 @@
# Matrix-based CPU Benchmark #
## Contains two options: ##
* NumPy script (which requires numpy lib)
* SciPy script (which requires scipy lib)
## Warning: SciPy based scipt kinda buggy and produces many warnings (the warnings already suppresed for visual)##

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@ -0,0 +1,64 @@
import warnings
# Suppress all warnings
warnings.filterwarnings("ignore")
import sys
import numpy as np
import scipy as sp
from scipy.stats import uniform
from time import time
class MatrixBench():
def __init__(self, iters: int, size: int):
self.iters = iters
self.size = size
self.average = 0
self.start_time = 0
self.end_time = 0
self.elapsed_time = 0
self.flops:float = 0.0
def create_matrices(self):
# Create num x num matrix and an num x 1 vector with random floating point numbers
self.matrix_A = uniform.rvs(size=(self.size, self.size)).astype(np.float32)
self.matrix_B = uniform.rvs(size=(self.size, 1)).astype(np.float32)
def do_operation(self):
# Do Floating Point Operation
self.start_time = time()
_ = sp.linalg.solve(self.matrix_A, self.matrix_B)
self.end_time = time()
def do_calculate(self):
# Calculate Floating Point Operation per Second (FLOPS)
self.elapsed_time = self.end_time - self.start_time
self.num_operations = (2 / 3) * self.size **3
self.flops = self.num_operations / self.elapsed_time
self.average += self.flops
def run(self):
print("# Linpack-like Matrix-based Floating Point Benchmark #")
print("# Using SciPy as Core (contains Warn: Ill-conditioned Matrix) #")
for i in range(0, self.iters + 1):
print(f"=> Epoch #{str(i).zfill(len(str(self.iters)))}", end="\r")
self.create_matrices()
self.do_operation()
self.do_calculate()
self.avg_flops: float = (self.average/self.iters) / 1000000
print(f"Result : {self.avg_flops:.2f} MFLOPS")
def main():
# Adjust Matrix Size
size = 192
# Adjust Iteration Time
iters = 10000
# Run Benchmark
bench = MatrixBench(iters=iters, size=size)
bench.run()
main()

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@ -6,5 +6,5 @@ This repository contains useful programs written in **Python 3.9**. Each folder
* **Benchmark Program** : using **psutil** to obtain computer performance * **Benchmark Program** : using **psutil** to obtain computer performance
* **Pickle Compress Program** : compress variable dumps using **gzip**, **bzip2**, **lzma**, or **no compression at all** * **Pickle Compress Program** : compress variable dumps using **gzip**, **bzip2**, **lzma**, or **no compression at all**
* **FastAPI Simple Example** : contains API standard operations (Normal Request, GET Request, HTML Request) * **FastAPI Simple Example** : contains API standard operations (Normal Request, GET Request, HTML Request)
* **Matrix-based Bencmark** : contains a class to recreate Linpack (Matrix-based) Floating-point Per Second (FLOPS) Benchmark. Based on **numpy** lib * **Matrix-based Bencmark** : contains a class to recreate Linpack (Matrix-based) Floating-point Per Second (FLOPS) Benchmark. Based on **numpy** or **scipy** libs
* *adding more soon* * *adding more soon*