Why the GPU performance changed so much for different data types
Use a piece of code to test the different of performance with CPU and GPU, I found the performance diff is very interesting.
float16 has the worst performance, but the improvement of GPU is most obvious.
float32 has the best performance, but the improvement of GPU is limited.
float64 is better than float16, but gpu is not as good as CPU.
Can any one here can share some insights on why?
My hardware is Xeon E5-1650 + Quadro K620.
This is my testing code.
from __future__ import print_function
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf
import time
def get_times(maximum_time):
device_times = {
"/gpu:0":,
"/cpu:0":
}
matrix_sizes = range(500,50000,50)
for size in matrix_sizes:
for device_name in device_times.keys():
print("####### Calculating on the " + device_name + " #######")
shape = (size,size)
data_type = tf.float16
with tf.device(device_name):
r1 = tf.random_uniform(shape=shape, minval=0, maxval=1, dtype=data_type)
r2 = tf.random_uniform(shape=shape, minval=0, maxval=1, dtype=data_type)
dot_operation = tf.matmul(r2, r1)
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as session:
start_time = time.time()
result = session.run(dot_operation)
time_taken = time.time() - start_time
print(result)
device_times[device_name].append(time_taken)
print(device_times)
if time_taken > maximum_time:
return device_times, matrix_sizes
device_times, matrix_sizes = get_times(1.5)
gpu_times = device_times["/gpu:0"]
cpu_times = device_times["/cpu:0"]
plt.plot(matrix_sizes[:len(gpu_times)], gpu_times, 'o-')
plt.plot(matrix_sizes[:len(cpu_times)], cpu_times, 'o-')
plt.ylabel('Time')
plt.xlabel('Matrix size')
plt.show()
tensorflow gpu
asked Nov 21 at 10:11
Capemer
365
add a comment |
Use a piece of code to test the different of performance with CPU and GPU, I found the performance diff is very interesting.
float16 has the worst performance, but the improvement of GPU is most obvious.
float32 has the best performance, but the improvement of GPU is limited.
float64 is better than float16, but gpu is not as good as CPU.
Can any one here can share some insights on why?
My hardware is Xeon E5-1650 + Quadro K620.
This is my testing code.
from __future__ import print_function
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf
import time
def get_times(maximum_time):
device_times = {
"/gpu:0":,
"/cpu:0":
}
matrix_sizes = range(500,50000,50)
for size in matrix_sizes:
for device_name in device_times.keys():
print("####### Calculating on the " + device_name + " #######")
shape = (size,size)
data_type = tf.float16
with tf.device(device_name):
r1 = tf.random_uniform(shape=shape, minval=0, maxval=1, dtype=data_type)
r2 = tf.random_uniform(shape=shape, minval=0, maxval=1, dtype=data_type)
dot_operation = tf.matmul(r2, r1)
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as session:
start_time = time.time()
result = session.run(dot_operation)
time_taken = time.time() - start_time
print(result)
device_times[device_name].append(time_taken)
print(device_times)
if time_taken > maximum_time:
return device_times, matrix_sizes
device_times, matrix_sizes = get_times(1.5)
gpu_times = device_times["/gpu:0"]
cpu_times = device_times["/cpu:0"]
plt.plot(matrix_sizes[:len(gpu_times)], gpu_times, 'o-')
plt.plot(matrix_sizes[:len(cpu_times)], cpu_times, 'o-')
plt.ylabel('Time')
plt.xlabel('Matrix size')
plt.show()
tensorflow gpu
asked Nov 21 at 10:11
Capemer
365
add a comment |
Use a piece of code to test the different of performance with CPU and GPU, I found the performance diff is very interesting.
float16 has the worst performance, but the improvement of GPU is most obvious.
float32 has the best performance, but the improvement of GPU is limited.
float64 is better than float16, but gpu is not as good as CPU.
Can any one here can share some insights on why?
My hardware is Xeon E5-1650 + Quadro K620.
This is my testing code.
from __future__ import print_function
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf
import time
def get_times(maximum_time):
device_times = {
"/gpu:0":,
"/cpu:0":
}
matrix_sizes = range(500,50000,50)
for size in matrix_sizes:
for device_name in device_times.keys():
print("####### Calculating on the " + device_name + " #######")
shape = (size,size)
data_type = tf.float16
with tf.device(device_name):
r1 = tf.random_uniform(shape=shape, minval=0, maxval=1, dtype=data_type)
r2 = tf.random_uniform(shape=shape, minval=0, maxval=1, dtype=data_type)
dot_operation = tf.matmul(r2, r1)
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as session:
start_time = time.time()
result = session.run(dot_operation)
time_taken = time.time() - start_time
print(result)
device_times[device_name].append(time_taken)
print(device_times)
if time_taken > maximum_time:
return device_times, matrix_sizes
device_times, matrix_sizes = get_times(1.5)
gpu_times = device_times["/gpu:0"]
cpu_times = device_times["/cpu:0"]
plt.plot(matrix_sizes[:len(gpu_times)], gpu_times, 'o-')
plt.plot(matrix_sizes[:len(cpu_times)], cpu_times, 'o-')
plt.ylabel('Time')
plt.xlabel('Matrix size')
plt.show()
tensorflow gpu
asked Nov 21 at 10:11
Capemer
365
Use a piece of code to test the different of performance with CPU and GPU, I found the performance diff is very interesting.
float16 has the worst performance, but the improvement of GPU is most obvious.
float32 has the best performance, but the improvement of GPU is limited.
float64 is better than float16, but gpu is not as good as CPU.
Can any one here can share some insights on why?
My hardware is Xeon E5-1650 + Quadro K620.
This is my testing code.
from __future__ import print_function
import matplotlib
import matplotlib.pyplot as plt
import tensorflow as tf
import time
def get_times(maximum_time):
device_times = {
"/gpu:0":,
"/cpu:0":
}
matrix_sizes = range(500,50000,50)
for size in matrix_sizes:
for device_name in device_times.keys():
print("####### Calculating on the " + device_name + " #######")
shape = (size,size)
data_type = tf.float16
with tf.device(device_name):
r1 = tf.random_uniform(shape=shape, minval=0, maxval=1, dtype=data_type)
r2 = tf.random_uniform(shape=shape, minval=0, maxval=1, dtype=data_type)
dot_operation = tf.matmul(r2, r1)
with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as session:
start_time = time.time()
result = session.run(dot_operation)
time_taken = time.time() - start_time
print(result)
device_times[device_name].append(time_taken)
print(device_times)
if time_taken > maximum_time:
return device_times, matrix_sizes
device_times, matrix_sizes = get_times(1.5)
gpu_times = device_times["/gpu:0"]
cpu_times = device_times["/cpu:0"]
plt.plot(matrix_sizes[:len(gpu_times)], gpu_times, 'o-')
plt.plot(matrix_sizes[:len(cpu_times)], cpu_times, 'o-')
plt.ylabel('Time')
plt.xlabel('Matrix size')
plt.show()
tensorflow gpu
tensorflow gpu
asked Nov 21 at 10:11
Capemer
365
asked Nov 21 at 10:11
Capemer
365
asked Nov 21 at 10:11
Capemer
365
asked Nov 21 at 10:11
Capemer
365
asked Nov 21 at 10:11
Capemer
365
365
add a comment |
add a comment |
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