`datasets.map` multi processing much slower than single processing 
Hi @hwijeen, you might want to look at issues #1796 and #1949. I think it could be something related to the I/O operations being performed. I see that many people are experiencing the same issue. Is this problem considered an "official" bug that is worth a closer look? @lhoestq Yes this is an official bug. On my side I haven't managed to reproduce it but @theo-m has. We'll investigate this ! Thank you for the reply! I would be happy to follow the discussions related to the issue.
If you do not mind, could you also give a little more explanation on my p.s.2? I am having a hard time figuring out why the single processing `map` uses all of my cores.
@lhoestq @theo-m  Regarding your ps2: It depends what function you pass to `map`.
For example, fast tokenizers from `transformers` in Rust tokenize texts and parallelize the tokenization over all the cores. I am still experiencing this issue with datasets 1.9.0..
Has there been a further investigation? 
<img width="442" alt="image" src="https://user-images.githubusercontent.com/29157715/126143387-8b5ddca2-a896-4e18-abf7-4fbf62a48b41.png">
 Hi. Is there any update on this issue? I am desperately trying to decrease my times, and multiprocessing "should" be the solution, but it literally takes 5 times longer. Which version of `datasets` are you using ? Hi,

I’m running into the same issue and trying to come up with a simple benchmark. 

# environment info
I have a total of 80 CPUs.

- `datasets` version: 2.4.0
- Platform: Linux-4.18.0-305.65.1.el8_4.x86_64-x86_64-with-glibc2.28
- Python version: 3.10.4
- PyArrow version: 8.0.0
- Pandas version: 1.4.3

# How to reproduce

```py
In [1]: from datasets import Dataset, set_caching_enabled                                                                                                       
In [2]: import numpy as np              
In [3]: set_caching_enabled(False)                                                                                                    
In [4]: d = Dataset.from_dict({'foo': np.random.randn(1000,256)})                                                                           
In [9]: d.set_format('np')
In [14]: def sort(array):                                                                                                                   
    ...:     np.sort(array)        
# multiprocessing disabled
In [19]: %%timeit                                                                                                                           
    ...: d.map(sort, input_columns='foo')                                                                                                   
78.8 ms ± 1.22 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)    
# multiprocessing enabled   
In [27]: %%timeit                                                                                                                           
    ...: d.map(sort, input_columns='foo',num_proc=10)               
858 ms ± 45.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)                                                                         
``` Spawning multiple processes has an overhead. For small datasets the processing is likely to be faster than spawning the processes and passing the data to them.

Especially since your dataset is in memory: the data has to be copied to the subprocesses.
On the other hand, datasets loaded from disk are much faster to reload from a subprocess thanks to memory mapping. Thanks for the clarifications! 

Indeed, when saving then loading the above dataset to disk, and increasing the number of rows to 10K or 100K, the performance gap narrows.

```py
# with 10000 rows
In [3]: %%timeit
   ...: d.map(sort, input_columns='foo')
578 ms ± 5.89 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [4]: %%timeit                                                                                                                                                                                                  
   ...: d.map(sort, input_columns='foo',num_proc=10)                                                                                                                                                              
1.06 s ± 47.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)

# with 100000 rows
In [6]: %%timeit
   ...: d.map(sort, input_columns='foo')
5.8 s ± 25.5 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)
In [7]: %%timeit
   ...: d.map(sort, input_columns='foo',num_proc=10)
7.23 s ± 154 ms per loop (mean ± std. dev. of 7 runs, 1 loop each
```
Hi, thank you for the great library.

I've been using datasets to pretrain language models, and it often involves datasets as large as ~70G.
My data preparation step is roughly two steps: `load_dataset` which splits corpora into a table of sentences, and `map` converts a sentence into a list of integers, using a tokenizer.

I noticed that `map` function with `num_proc=mp.cpu_count() //2` takes more than 20 hours to finish the job where as `num_proc=1` gets the job done in about 5 hours. The machine I used has 40 cores, with 126G of RAM. There were no other jobs when `map` function was running.

What could be the reason? I would be happy to provide information necessary to spot the reason.

p.s. I was experiencing the imbalance issue mentioned in [here](https://github.com/huggingface/datasets/issues/610#issuecomment-705177036) when I was using multi processing.
p.s.2 When I run `map` with `num_proc=1`, I see one tqdm bar but all the cores are working. When `num_proc=20`, only 20 cores work. 
![Screen Shot 2021-03-05 at 11 04 59](https://user-images.githubusercontent.com/29157715/110056895-ef6cf000-7da2-11eb-8307-6698e9fb1ad4.png)
