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linalg_solve_np_cp_tf.py
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linalg_solve_np_cp_tf.py
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import cpuinfo
import math
import numba
import os
import pdb
import pkg_resources
import time
import tensorflow as tf
try:
import cupy as cp
except ImportError:
cupy = None
import numpy as np
import matplotlib
import matplotlib.pyplot as plt
from numba import guvectorize, int32, float32, cuda, float64, void, jit, njit, prange, threading_layer
from timeit import default_timer as timer
cpuID = cpuinfo.get_cpu_info()['brand']
print(cpuID)
cpuID = cpuID[:30]
cudaID = ''
''' MIGHT NEED SMTH EXTRA DEPENDING ON CUDATOOLKIT'''
if numba.cuda.is_available() == True:
booCUDA = True
print (numba.cuda.detect())
cudaID = str(numba.cuda.get_current_device().name)
cudaID = cudaID[2:-1]
RunTime = {'cp_cuda':[], 'np_cpuMT':[], '/cpu:0':[], '/gpu:0':[]}
GFLOPS = {'cp_cuda':[], 'np_cpuMT':[], '/cpu:0':[], '/gpu:0':[]}
targetOpt = 'cuda'
# targetOpt = 'parallel'
# targetOpt = 'cpu'
else:
booCUDA = False
print ('No CUDA detected, parallel will be used')
RunTime = {'np_cpuMT':[], '/cpu:0':[]}
GFLOPS = {'np_cpuMT':[], '/cpu:0':[]}
targetOpt = 'parallel'
# targetOpt = 'cpu'
step = 500
count = 14
npdtype = np.float32
tfdtype = tf.float32
config = tf.ConfigProto()
config.log_device_placement = True
config.gpu_options.allow_growth = True
config.gpu_options.per_process_gpu_memory_fraction = 0.4
print('A X = B for variable size N using float32')
matrix_sizes = range(step, step * count + 1, step)
for N in matrix_sizes:
print('N = ' + str(N))
N_ops = N**3
''' numpy cpuMT'''
if True:
tR = timer()
A_np = np.random.randn(N**2).reshape(N,N).astype(npdtype)
B_np = np.random.randn(N).astype(npdtype)
tG = timer()
X_np = np.linalg.tensorsolve(A_np, B_np)
tG = timer() - tG
tG = round(N_ops/tG/10**9, 1)
tR = round(timer() - tR, 3)
GFLOPS['np_cpuMT'].append(tG)
RunTime['np_cpuMT'].append(tR)
# if np.allclose(np.matmul(A_np,X_np), B_np, rtol=0, atol=1e-03, equal_nan=False) == False:
# print(' numpy cpuMT is False for N = ' + str(N) )
''' tensorflow cpu'''
if True:
tR = timer()
with tf.device('/cpu:0'):
A_tf_cpu = tf.random_uniform(shape=(N,N), minval=0, maxval=1, dtype=tfdtype)
B_tf_cpu = tf.random_uniform(shape=(N,1), minval=0, maxval=1, dtype=tfdtype)
tf_op_cpu = tf.linalg.solve(A_tf_cpu, B_tf_cpu, adjoint=False, name=None)
with tf.Session(config=config) as session:
tG = timer()
X_tf_cpu = session.run(tf_op_cpu)
tG = timer() - tG
tG = round(N_ops/tG/10**9, 1)
session.close()
tR = round(timer() - tR, 3)
GFLOPS['/cpu:0'].append(tG)
RunTime['/cpu:0'].append(tR)
''' tensorflow gpu'''
if targetOpt == 'cuda':
tR = timer()
with tf.device('/device:GPU:0'):
A_tf_gpu = tf.random_uniform(shape=(N,N), minval=0, maxval=1, dtype=tfdtype)
B_tf_gpu = tf.random_uniform(shape=(N,1), minval=0, maxval=1, dtype=tfdtype)
tf_op_gpu = tf.linalg.solve(A_tf_gpu, B_tf_gpu, adjoint=False, name=None)
with tf.Session(config=config) as session:
tG = timer()
X_tf_gpu = session.run(tf_op_gpu)
tG = timer() - tG
tG = round(N_ops/tG/10**9, 1)
session.close()
tR = round(timer() - tR, 3)
GFLOPS['/gpu:0'].append(tG)
RunTime['/gpu:0'].append(tR)
''' cupy cuda'''
if targetOpt == 'cuda':
tR = timer()
mempool = cp.get_default_memory_pool()
mempool.free_all_blocks()
A_cp = cp.random.randn(N**2).reshape(N,N).astype(npdtype)
B_cp = cp.random.randn(N).astype(npdtype)
tG = timer()
X_cp = cp.linalg.tensorsolve(A_cp, B_cp, axes=None)
tG = timer() - tG
tG = round(N_ops/tG/10**9, 1)
A_cp = None
B_cp = None
X_cp = None
mempool.free_all_blocks()
tR = round(timer() - tR, 3)
GFLOPS['cp_cuda'].append(tG)
RunTime['cp_cuda'].append(tR)
''' Times '''
np_RunTime = RunTime['np_cpuMT']
plt.plot(matrix_sizes[:len(np_RunTime)], np_RunTime, 'bo-')
tf_cpu_RunTime = RunTime['/cpu:0']
plt.plot(matrix_sizes[:len(tf_cpu_RunTime)], tf_cpu_RunTime, 'bo--')
if numba.cuda.is_available():
cp_RunTime = RunTime['cp_cuda']
plt.plot(matrix_sizes[:len(cp_RunTime)], cp_RunTime, 'r^-')
tf_gpu_RunTime = RunTime['/gpu:0']
plt.plot(matrix_sizes[:len(tf_gpu_RunTime)], tf_gpu_RunTime, 'r^--')
plt.title('RunTime vs Matrix size using float32')
plt.legend(('np ' + cpuID, 'tf ' + cpuID, 'cp ' + cudaID, 'tf ' + cudaID), loc='upper left')
plt.ylabel('Time')
plt.xlabel('Matrix size')
plt.show()
''' GFLOPS '''
np_GFLOPS = GFLOPS['np_cpuMT']
plt.plot(matrix_sizes[:len(np_GFLOPS)], np_GFLOPS, 'bo-')
tf_cpu_GFLOPS = GFLOPS['/cpu:0']
plt.plot(matrix_sizes[:len(tf_cpu_GFLOPS)], tf_cpu_GFLOPS, 'bo--')
if numba.cuda.is_available():
cp_GFLOPS = GFLOPS['cp_cuda']
plt.plot(matrix_sizes[:len(cp_GFLOPS)], cp_GFLOPS, 'r^-')
tf_gpu_GFLOPS = GFLOPS['/gpu:0']
plt.plot(matrix_sizes[:len(tf_gpu_GFLOPS)], tf_gpu_GFLOPS, 'r^--')
plt.title('GFLOPS vs Matrix size using float32')
plt.legend(('np ' + cpuID, 'tf ' + cpuID, 'cp ' + cudaID, 'tf ' + cudaID), loc='upper left')
plt.ylabel('GFLOPS')
plt.xlabel('Matrix size')
plt.show()