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<!DOCTYPE html>
<html>
<head>
<title>Can There Be Too Much Parallelism?</title>
<meta http-equiv="Content-Type" content="text/html; charset=UTF-8" />
<link rel="stylesheet" href="./assets/style.css">
</head>
<body>
<textarea id="source">title: Can There Be Too Much Parallelism?
use_katex: False
class: title-slide
# Can There Be Too Much Parallelism?
.larger[Thomas J. Fan]<br>
<a href="https://www.github.com/thomasjpfan" target="_blank" class="title-link"><span class="icon icon-github right-margin"></span>@thomasjpfan</a>
<a class="this-talk-link", href="https://github.com/thomasjpfan/scipy-2023-too-parallel" target="_blank">
This talk on Github: thomasjpfan/scipy-2023-too-parallel</a>
---
class: chapter-slide
# Yes 👀
---
class: center
# User?
---
class: center
# Developer?
---
# My Perspective
.g[
.g-6[
## Parallelism in Scikit-learn
- BLAS through SciPy
- OpenMP + Cython
- Python Multi-Threading
- Python Multi-Processing
]
.g-6[
]
]
---
# Scope
.g[
.g-4[
]
.g-4[
]
.g-4[
]
]
---
.center[
# State of Python Parallelism
]
.g.g-middle[
.g-6.larger[
## APIs 💻
## Interactions 🥂
## Defaults 🌈
]
.g-6[
]
]
---
class: center
# APIs 💻
---
.g.g-middle[
.g-8[
# Environment Variables 🌲
.larger[
- **OpenMP**: `OMP_NUM_THREADS`
- **MKL**: `MKL_NUM_THREADS`
- **OpenBLAS**: `OPENBLAS_NUM_THREADS`
]
]
.g-4[
]
]
---
.g.g-middle[
.g-8[
# Environment Variables 🌲
.larger[
- **OpenMP**: `OMP_NUM_THREADS`
- **MKL**: `MKL_NUM_THREADS`
- **OpenBLAS**: `OPENBLAS_NUM_THREADS`
- **Polars**: `POLARS_MAX_THREADS`
- **Numba**: `NUMBA_NUM_THREADS`
- **macOS accelerate**: `VECLIB_MAXIMUM_THREADS`
- **numexpr**: `NUMEXPR_NUM_THREADS`
]
]
.g-4[
]
]
---
# Global Configuration 🌎
.g.g-middle[
.g-8[
.larger[
- `torch.set_num_threads`
- `numba.set_num_threads`
- `threadpoolctl.threadpool_limits`
- `cv.setNumThreads`
]
]
.g-4[
]
]
---
# Block Configuration 🧱
## `threadpoolctl`
```python
from threadpoolctl import threadpool_limits
import numpy as np
*with threadpool_limits(limits=2):
a = np.random.randn(1000, 1000)
a_squared = a @ a
```
---
# Call-site ☎️
.g.g-middle[
.g-8[
.larger[
- **scikit-learn**: `n_jobs`
- **SciPy**: `workers`
- **PyTorch DataLoader**: `num_workers`
- **Python**: `max_workers`
]
]
.g-4[
]
]
---
.g.g-middle[
.g-6[
# APIs 💻
.larger[
- Environment Variables 🌲
- Global Configuration 🌎
- Block Configuration 🧱
- Call-site ☎️
]
]
.g-6.g-center[
]
]
---
class: top
<br><br><br><br>
# Proposal: Consistent APIs 🔮
.g[
.g-6[
## Now
```
export OMP_NUM_THREADS=1
export MKL_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
export POLARS_MAX_THREADS=1
export NUMEXPR_NUM_THREADS=1
```
]
.g-6[
]
]
---
class: top
<br><br><br><br>
# Proposal: Consistent APIs 🔮
.g[
.g-6[
## Now
```
export OMP_NUM_THREADS=1
export MKL_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
export POLARS_MAX_THREADS=1
export NUMEXPR_NUM_THREADS=1
```
]
.g-6[
## Future 🚀
### Pragmatic
```
export OMP_NUM_THREADS=1
```
]
]
---
class: top
<br><br><br><br>
# Proposal: Consistent APIs 🔮
.g[
.g-6[
## Now
```
export OMP_NUM_THREADS=1
export MKL_NUM_THREADS=1
export OPENBLAS_NUM_THREADS=1
export POLARS_MAX_THREADS=1
export NUMEXPR_NUM_THREADS=1
```
]
.g-6[
## Future 🚀
### Pragmatic
```
export OMP_NUM_THREADS=1
```
### Better ☀️
```
export GOTO_NUM_THREADS=1
```
]
]
---
# Proposal 🔮
## Recognize more threadpools in `threadpoolctl`
.center[
]
---
# Proposal 🔮
.g[
.g-6[
## Now
- **scikit-learn**: `n_jobs`
- **SciPy**: `workers`
- **PyTorch DataLoader**: `num_workers`
- **Python**: `max_workers`
]
.g-6[
]
]
---
# Proposal 🔮
.g[
.g-6[
## Now
- **scikit-learn**: `n_jobs`
- **SciPy**: `workers`
- **PyTorch DataLoader**: `num_workers`
- **Python**: `max_workers`
]
.g-6[
## Future 🚀
- Everyone uses `workers`
]
]
---
class: center
## Interactions 🥂
---
# Oversubscription 💥
## Python + native threading 🐍 + 🧵
```python
from scipy import optimize
optimize.brute(
* computation_that_uses_8_cores, ...
* workers=8
)
```
---
# Current workarounds 🩹
## Dask 
[Source](https://docs.dask.org/en/stable/array-best-practices.html#avoid-oversubscribing-threads)
---
[Source](https://docs.ray.io/en/latest/serve/scaling-and-resource-allocation.html#configuring-parallelism-with-omp-num-threads)
---
# PyTorch's DataLoader
.g.g-middle[
.g-8[
```python
from torch.utils.data import DataLoader
dl = DataLoader(..., num_workers=8)
# torch/utils/data/_utils/worker.py
def _worker_loop(...):
...
* torch.set_num_threads(1)
```
]
.g-4.center[
]
]
[Source]()
---
# scikit-learn
.g.g-middle[
.g-8[
```python
from sklearn.experimental import enable_halving_search_cv
from sklearn.model_selection import HalvingGridSearchCV
from sklearn.ensemble import HalvingRandomSearchCV
*clf = HistGradientBoostingClassifier()
search = HalvingGridSearchCV(
clf,
param_distributions,
* n_jos=8
)
search.fit(X, y)
```
]
.g-4.center[
]
]
---
class: top
<br><br><br><br>
# Multiple Parallel Abstractions 🧵 + 🧶
- Python multiprocessing using `fork` + GCC OpenMP: **stalls**
--
- Intel OpenMP + LLVM OpenMP on Linux: **stalls**
--
- Multiple OpenBLAS libraries: **sometimes slower**
--
- Read more at: [thomasjpfan.github.io/parallelism-python-libraries-design/](https://thomasjpfan.github.io/parallelism-python-libraries-design/)
---
# Multiple Parallel Abstractions 🧵 + 🧶
## Using more than one parallel backends 🤯
Sources: [polars](https://pola-rs.github.io/polars-book/user-guide/howcani/multiprocessing.html),
[numba](https://numba.pydata.org/numba-doc/latest/user/threading-layer.html),
[scikit-learn](https://scikit-learn.org/stable/faq.html#why-do-i-sometime-get-a-crash-freeze-with-n-jobs-1-under-osx-or-linux),
[pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/enhancingperf.html#caveats)
---
class: top
<br><br>
# Proposal: Catch issues early 🔮
.g.g-middle[
.g-10[
]
.g-2.center[
]
]
[Source](https://github.com/numba/numba/blob/249c8ff3206928b486346443ec148508f8c25f8e/numba/np/ufunc/omppool.cpp#L119-L121)
--
<br>
## Not a full solution 🩹
---
# Multiple Native threading libraries 🧵 + 🧶
.center[
]
[Source](https://www.slideshare.net/RalfGommers/parallelism-in-a-numpybased-program)
---
# Multiple Native threading libraries 🧵 + 🧶
## CPU Waiting ⏳
```python
for n_iter in range(100):
UV = U @ V.T # Use OpenBLAS with pthreads
compute_with_openmp(UV) # Use OpenMP
```
[xianyi/OpenBLAS#3187](https://github.com/xianyi/OpenBLAS/issues/3187)
---
# Current Workaround 🩹
.g.g-middle[
.g-6[
## Conda-forge + OpenMP
]
.g-6.center[
]
]
---
# Current Workaround 🩹
.center[
]
[Source](https://www.slideshare.net/RalfGommers/parallelism-in-a-numpybased-program)
---
class: top
# Proposal 🔮
## Ship PyPI wheels for OpenMP
--
## Not a full solution 🩹
.g.g-center.g-middle[
.g-6[
]
.g-6[
]
]
---
class: center
# Defaults 🌈
---
class: top
<br><br>
# NumPy
.g.g-middle[
.g-8[
```python
import numpy as np
out = np.sum(A_array, axis=1)
```
]
.g-4.center[
]
]
--
.alert.bold.center[🐌 One Core 🐌]
---
class: top
<br><br>
# NumPy matmul
.g.g-middle[
.g-8[
```python
import numpy as np
out = A_array @ B_array
```
]
.g-4.center[
]
]
--
.success.bold.center[🏎️ All Cores 🏎️]
---
class: top
<br><br><br>
# NumPy matmul (Configuration)
.g[
.g-8[
## Environment variable: `OMP_NUM_THREADS`
```python
out = A_array @ B_array
```
]
.g-4.center[
]
]
---
class: top
<br><br><br>
# NumPy matmul (Configuration)
.g[
.g-8[
## Environment variable: `OMP_NUM_THREADS`
```python
out = A_array @ B_array
```
## `threadpoolctl`
```python
from threadpoolctl import threadpool_limits
with threadpool_limits(limits=1):
out = A_array @ B_array
```
]
.g-4.center[
]
]
---
class: top
<br>
<br>
<br>
<br>
# PyTorch
.g[
.g-8[
```python
import torch
*out = torch.sum(A_tensor, axis=1)
```
]
.g-4.center[
]
]
--
.success.bold.center[🏎️ All Cores 🏎️]
---
class: top
<br><br>
# PyTorch (Configuration)
.g[
.g-8[
- Environment variable: `OMP_NUM_THREADS`
- `threadpoolctl`
```python
with threadpool_limits(limits=2):
out = torch.sum(A_tensor, axis=1)
```
]
.g-4.center[
]
]
---
class: top
<br><br>
# PyTorch (Configuration)
.g[
.g-8[
- Environment variable: `OMP_NUM_THREADS`
- `threadpoolctl`
```python
with threadpool_limits(limits=2):
out = torch.sum(A_tensor, axis=1)
```
- PyTorch function
```python
import torch
*torch.set_num_threads(2)
out = torch.sum(A_tensor, axis=1)
```
]
.g-4.center[
]
]
---
class: top
<br><br><br><br>
# pandas apply
.g[
.g-8[
```python
import pandas as pd
df = pd.DataFrame(np.random.randn(10_000, 100))
roll = df.rolling(100)
*out = roll.mean()
```
]
.g-4.center[
]
]
--
.alert.bold.center[🐌 One Core 🐌]
---
class: top
<br><br>
# pandas apply + numba
.g.g-middle[
.g-8[
```python
import pandas as pd
df = pd.DataFrame(np.random.randn(10_000, 100))
roll = df.rolling(100)
out = roll.mean(
* engine="numba",
* engine_kwargs={"parallel": True},
)
```
[Read more](https://pandas.pydata.org/pandas-docs/stable/user_guide/enhancingperf.html#pandas-numba-engine)
]
.g-4.center[
]
]
--
.success.bold.center[🏎️ All Cores 🏎️]
---
class: top
<br><br><br><br>
# pandas apply + numba (Configuration)
.g[
.g-8[
- Environment variable: `NUMBA_NUM_THREADS`
]
.g-4.center[
]
]
---
class: top
<br><br><br><br>
# pandas apply + numba (Configuration)
.g[
.g-8[
- Environment variable: `NUMBA_NUM_THREADS`
- Numba function
```python
import numba
*numba.set_num_threads(2)
out = roll.mean(engine="numba", engine_kwargs={"parallel": True})
```
]
.g-4.center[
]
]
---
class: top
<br><br><br>
# LogisticRegression
.g.g-middle[
.g-8[
```python
from sklearn.linear_model import LogisticRegression
log_reg = LogisticRegression().fit(...)
*log_reg.predict(X)
```
]
.g-4.center[
]
]
--
.success.bold.center[🏎️ All Cores 🏎️]
---
# LogisticRegression (Configuration)
.g[
.g-8[
- Environment variable: `OMP_NUM_THREADS`
]
.g-4.center[
]
]
---
# LogisticRegression (Configuration)
.g[
.g-8[
- Environment variable: `OMP_NUM_THREADS`
- `threadpoolctl`
```python
*with threadpool_limits(limits=2):
log_reg.predict(X)
```
]
.g-4.center[
]
]
---
class: top
<br><br><br>
# HistGradientBoostingClassifier
.g[
.g-8[
```python
from sklearn.ensemble import HistGradientBoostingClassifier
hist = HistGradientBoostingClassifier()
hist.fit(X, y)
```
]
.g-4.center[
]
]
--
.success.bold.center[🏎️ All Cores 🏎️]
---
# HistGradientBoostingClassifier (Configuration)
.g[
.g-8[
- Environment variable: `OMP_NUM_THREADS`
]
.g-4.center[
]
]
---
# HistGradientBoostingClassifier (Configuration)
.g[
.g-8[
- Environment variable: `OMP_NUM_THREADS`
- `threadpoolctl`
```python
*with threadpool_limits(limits=2):
hist.predict(X)
```
]
.g-4.center[
]
]
---
class: top
<br><br><br>
# polars
.g.g-middle[
.g-8[
```python
out = (
pl.scan_csv(...)
.filter(pl.col("sepal_length") > 5)
.groupby("species")
.agg(pl.col("sepal_width").mean())
.collect()
)
```
]
.g-4.center[
]
]