issues: 1802960393

What is your issue?

I have a machine with 24 cores and 2 threads per core. I'm trying to optimize the following code for parallel execution. However, I noticed that the code's performance starts to degrade after a certain number of threads.

import argparse
import glob
import h5py
import numpy as np
import pandas as pd
import xarray as xr
from tqdm import tqdm
import time
import datetime
from multiprocessing import Pool, cpu_count, Lock
import multiprocessing
import cProfile, pstats, io


def process_parcel_file(f, bands, mask):
    start_time = time.time()
    test = xr.open_dataset(f)
    print(f"Elapsed in process_parcel_file for reading dataset: {time.time() - start_time}")

    start_time = time.time()
    subset = test[bands + ['SCL']].copy()
    subset = subset.where(subset != 0, np.nan)
    if mask:
        subset = subset.where((subset.SCL >= 3) & (subset.SCL < 7))
    subset = subset[bands]

    # Adding a new dimension week_year and performing grouping
    subset['week_year'] = subset.time.dt.strftime('%Y-%U')
    subset = subset.groupby('week_year').mean().sortby('week_year')
    subset['id'] = test['id'].copy()

    # Store the dates and counting pixels for each parcel
    dates = subset.week_year.values
    n_pixels = test[['id', 'SCL']].groupby('id').count()['SCL'][:, 0].values.reshape(-1, 1)

    # Converting to dataframe
    grouped_sum = subset.groupby('id').sum()
    ids = grouped_sum.id.values
    grouped_sum = grouped_sum.to_array().values
    grouped_sum = np.swapaxes(grouped_sum, 0, 1)
    grouped_sum = grouped_sum.reshape((grouped_sum.shape[0], -1))
    colnames = ["{}_{}".format(b, str(x).split('T')[0]) for b in bands for x in dates] + ['count']
    values = np.hstack((grouped_sum, n_pixels))
    df = pd.DataFrame(values, columns=colnames)
    df.insert(0, 'id', ids)
    print(f"Elapsed in process_parcel_file til end: {time.time() - start_time}")
    return df


def fs_creation(input_dir, out_file, labels_to_keep=None, th=0.1, n=64, days=5, total_days=180, mask=False,
                mode='s2', method='patch', bands=['B02', 'B03', 'B04', 'B05', 'B06', 'B07', 'B08', 'B8A', 'B11', 'B12']):
    files = glob.glob(input_dir)
    times_pool = []  # For storing execution times
    times_seq = []
    cpu_counts = list(range(2, multiprocessing.cpu_count() + 1, 4))  # The different CPU counts to use

    for count in cpu_counts:
        print(f"Executing with {count} threads")
        if method == 'parcel':
            start_pool = time.time()
            with Pool(count) as pool:
                arguments = [(f, bands, mask) for f in files]
                dfs = list(tqdm(pool.starmap(process_parcel_file, arguments), total=len(arguments)))


            end_pool = time.time()
            start_seq = time.time()
            dfs = pd.concat(dfs)
            dfs = dfs.groupby('id').sum()
            counts = dfs['count'].copy()
            dfs = dfs.div(dfs['count'], axis=0)
            dfs['count'] = counts
            dfs.drop(index=-1).to_csv(out_file)
            end_seq = time.time()
            times_pool.append(end_pool - start_pool)  
            times_seq.append(end_seq - start_seq)

    pd.DataFrame({'CPU_count': cpu_counts, 'Time pool': times_pool, 
                  'Time seq' : times_seq}).to_csv('cpu_times.csv', index=False)

    return 0

When executing the code, it scales well up to around 7-8 threads, but after that, the performance starts to deteriorate. I have profiled the code, and it seems that each thread takes more time to execute the same code.

For example, with 2 threads:

Elapsed in process_parcel_file for reading dataset: 0.012271404266357422
Elapsed in process_parcel_file til end: 1.6681673526763916
Elapsed in process_parcel_file for reading dataset: 0.014229536056518555
Elapsed in process_parcel_file til end: 1.5836331844329834

However, with 22 threads:

Elapsed in process_parcel_file for reading dataset: 0.17968058586120605
Elapsed in process_parcel_file til end: 12.049026727676392
Elapsed in process_parcel_file for reading dataset: 0.052398681640625
Elapsed in process_parcel_file til end: 6.014119625091553

I'm struggling to understand why the performance degrades with more threads. I've already verified that the system has the required number of cores and threads.

I would appreciate any guidance or suggestions to help me identify the cause of this issue and optimize the code for better performance.

It's really hard for me to provide a minimal working example so take that into account.

Thank you in advance.

Edit: The files are around 80MB each. I have 451 files. I added the following code to profile the function:

...
    start_time = time.time()
    mem_usage_start = memory_usage(-1, interval=0.1, timeout=None)[0]
    cpu_usage_start = psutil.cpu_percent(interval=None)
    test = xr.open_dataset(f)
    times['read_dataset'] = time.time() - start_time
    memory['read_dataset'] = memory_usage(-1, interval=0.1, timeout=None)[0] - mem_usage_start
    cpu_usage['read_dataset'] = psutil.cpu_percent(interval=None) - cpu_usage_start
...

And more code for each line in a similar fashion. I used the libraries memory_profiler and psutil, and I have the information for each thread. CSV's with the results are available here: https://wetransfer.com/downloads/44df14ea831da7693300a29d8e0d4e7a20230703173536/da04a0 The results identify each line in the function with the number of cpus selected, so each one is a thread. This machine has 125Gb of RAM