这听起来非常适合多处理，因为不同的元素需要单独处理，并且可以单独保存到磁盘。
Python有一个非常有用的包，叫做multiprocessing，里面有各种池、处理和其他选项。
下面是一个简单的（并有注解记录的）用法示例：

from multiprocessing import Process
import numpy as np    

# This should be your existing function
def write_file(element):
    # write file
    pass

# You'll still be looping of course, but in parallel over batches. This is a helper function for looping over a "batch"
def write_list_of_files(elements_list):
    for element in elements_list:
        write_file(element)

# You're data goes here...
all_elements = np.ones((1000, 256, 256, 3))

num_procs = 10  # Depends on system limitations, number of cpu-cores, etc.
procs = [Process(target=write_list_of_files, args=[all_elements[k::num_procs, ...]]) for k in range(num_procs)]  # Each of these processes in the list is going to run the "write_list_of_files" function, but have separate inputs, due to the indexing trick of using "k::num_procs"...

for p in procs:
    p.start()  # Each process starts running independantly

for p in procs:
    p.join()  # assures the code won't continue until all are "joined" and done. Optional obviously...
    
print('All done!')  # This only runs onces all procs are done, due to "p.join"