Hey there! Let’s break this down for you based on your NLP task and iMac 5K setup.

First, let’s clarify when each option makes sense:

• GPU Acceleration: If your performance bottleneck is deep learning model training/inference (like fine-tuning BERT, training LSTMs, etc.), this is almost always the better choice. Deep learning relies heavily on matrix operations, which GPUs are optimized for thanks to their thousands of parallel cores. iMac 5K models (especially those with M-series chips or mid-to-high-end AMD GPUs) have solid GPU power—you can leverage frameworks like TensorFlow/PyTorch with Metal (for Apple Silicon) or AMD GPU support to cut down runtime drastically, even for 32K rows of text.
• Multi-CPU Parallelism: This shines for data preprocessing steps (text cleaning, tokenization, feature engineering) where tasks are independent and CPU-bound. It’s also a fallback if you don’t have access to a capable GPU (e.g., older Intel iMac with integrated graphics). That said, multi-CPU is far less efficient for actual model training compared to GPUs—CPUs have fewer cores and weaker floating-point performance for the parallel math deep learning needs.

In short: Prioritize GPU acceleration if your slowdown comes from model work. Use multi-CPU parallelism for preprocessing or as a last resort for model training without a GPU.

Let’s cover the two main use cases:

1. Parallelizing Data Preprocessing #

These tools make it easy to split preprocessing tasks across CPU cores:

Using multiprocessing (Native Library) #

Great for simple, batch-based tasks like tokenization:

from multiprocessing import Pool
from nltk.tokenize import word_tokenize

def process_single_text(text):
    # Your preprocessing logic: lowercasing, tokenization, cleaning, etc.
    return word_tokenize(text.strip().lower())

if __name__ == "__main__":
    # Load your 32K rows of text into a list
    raw_texts = [...]
    
    # Use 4 cores (adjust based on your iMac's CPU count)
    with Pool(processes=4) as pool:
        processed_texts = pool.map(process_single_text, raw_texts)

Using concurrent.futures (Simpler API) #

A more modern alternative to multiprocessing:

from concurrent.futures import ProcessPoolExecutor

def process_single_text(text):
    # Same preprocessing logic as above
    return processed_text

# Process texts across 4 cores
with ProcessPoolExecutor(max_workers=4) as executor:
    processed_texts = list(executor.map(process_single_text, raw_texts))

Parallel Pandas apply #

If your data is in a Pandas DataFrame, use swifter to auto-optimize parallel processing:

import pandas as pd
import swifter

def process_single_text(text):
    # Preprocessing logic
    return processed_text

df = pd.read_csv("your_text_data.csv")
# swifter automatically uses parallel processing if it's beneficial
df["processed_text"] = df["text_column"].swifter.apply(process_single_text)

2. Multi-CPU Parallelism for Model Training (Not Recommended, But Possible) #

If you have to train your model on CPU, frameworks like TensorFlow and PyTorch support distributing work across cores:

TensorFlow #

import tensorflow as tf

# Use all available CPU cores
strategy = tf.distribute.MirroredStrategy(cross_device_ops=tf.distribute.HierarchicalCopyAllReduce())

with strategy.scope():
    # Define your model inside the strategy scope
    model = tf.keras.Sequential([
        tf.keras.layers.Embedding(input_dim=10000, output_dim=128),
        tf.keras.layers.LSTM(64),
        tf.keras.layers.Dense(1, activation="sigmoid")
    ])
    model.compile(optimizer="adam", loss="binary_crossentropy", metrics=["accuracy"])

# Train the model across CPU cores
model.fit(x_train, y_train, epochs=10, batch_size=32)

PyTorch #

import torch
from torch.utils.data import DataLoader, Dataset

# Define your custom dataset
class TextDataset(Dataset):
    def __init__(self, texts, labels):
        self.texts = texts
        self.labels = labels
    
    def __len__(self):
        return len(self.texts)
    
    def __getitem__(self, idx):
        return self.texts[idx], self.labels[idx]

# Load data and create a multi-process dataloader
dataset = TextDataset(processed_texts, labels)
# num_workers = number of cores to use for data loading
dataloader = DataLoader(dataset, batch_size=32, num_workers=4)

# Wrap model with DataParallel to use multiple CPU cores
model = YourNLPModel()
model = torch.nn.DataParallel(model)

# Training loop
optimizer = torch.optim.Adam(model.parameters(), lr=1e-3)
for epoch in range(10):
    for batch_texts, batch_labels in dataloader:
        optimizer.zero_grad()
        outputs = model(batch_texts)
        loss = torch.nn.functional.binary_cross_entropy(outputs, batch_labels)
        loss.backward()
        optimizer.step()

Note: Multi-CPU model training will still be much slower than using a GPU—only use this if you have no other option.

内容的提问来源于stack exchange，提问作者Ali Yousef