[QST]Why is cuml GPU dozens of times slower than scikit-learn CPU in Lasso，Ridge and ElasticNet models? #6080

JiayuChen02 · 2024-09-24T07:10:58Z

I tried to compare the speed difference of the same model on CPU and GPU. Here is my code：

import numpy as np
import pandas as pd
import time
import cudf
import cupy as cp
from cuml.linear_model import Lasso as cuml_model
from sklearn.linear_model import Lasso as sk_model

n_samples = 300 
n_features = 120000  

X = np.random.rand(n_samples, n_features)
y = np.random.rand(n_samples)


param = {
    "alpha": 0.001,
    "max_iter": 1000,
    "tol": 1e-3,
}

sklearn_model = sk_model()
sklearn_model.set_params(**param)

start_time = time.time()
sklearn_model.fit(X, y)
end_time = time.time()

print(sklearn_model.get_params())

print(f"scikit-learn training time: {end_time - start_time:.4f} seconds")


X_cudf = cp.array(X)
y_cudf = cp.array(y)


rapids_model = cuml_model()
rapids_model.set_params(**param)

start_time = time.time()

rapids_model.fit(X_cudf, y_cudf)

end_time = time.time()
print(rapids_model.get_params())
print(f"cuML GPU training time: {end_time - start_time:.4f} seconds")

And the result is

{'alpha': 0.001, 'copy_X': True, 'fit_intercept': True, 'max_iter': 1000, 'positive': False, 'precompute': False, 'random_state': None, 'selection': 'cyclic', 'tol': 0.001, 'warm_start': False}
scikit-learn training time: 14.0172 seconds
{'selection': 'cyclic', 'solver': 'cd', 'tol': 0.001, 'output_type': 'input', 'normalize': False, 'handle': <pylibraft.common.handle.Handle object at 0x7fa5b99e02d0>, 'alpha': 0.001, 'fit_intercept': True, 'verbose': 4, 'max_iter': 1000}
cuML GPU training time: 486.6329 seconds

Why?

The text was updated successfully, but these errors were encountered:

dantegd · 2024-09-24T14:42:33Z

Thanks for the issue @JiayuChen02, I just tried your code on a machine with a T4 and this is the results I got:

{'alpha': 0.001, 'copy_X': True, 'fit_intercept': True, 'max_iter': 1000, 'positive': False, 'precompute': False, 'random_state': None, 'selection': 'cyclic', 'tol': 0.001, 'warm_start': False}
scikit-learn training time: 7.7504 seconds
{'output_type': 'input', 'verbose': 4, 'handle': <pylibraft.common.handle.Handle object at 0x7fd8addf73f0>, 'fit_intercept': True, 'normalize': False, 'tol': 0.001, 'solver': 'cd', 'max_iter': 1000, 'alpha': 0.001, 'selection': 'cyclic'}
cuML GPU training time: 0.8425 seconds

In general, 300 rows is not a dataset where I would expect massive GPU speedups, but things seem to be working fine. What could be happening is you could be running maybe into some JIT compilation issues? What GPU are you using? It would be very helpful if you could run this script and put the output here: https://github.com/rapidsai/cuml/blob/branch-24.10/print_env.sh

JiayuChen02 · 2024-09-25T01:20:39Z

Thanks for the issue @JiayuChen02, I just tried your code on a machine with a T4 and this is the results I got:
{'alpha': 0.001, 'copy_X': True, 'fit_intercept': True, 'max_iter': 1000, 'positive': False, 'precompute': False, 'random_state': None, 'selection': 'cyclic', 'tol': 0.001, 'warm_start': False}
scikit-learn training time: 7.7504 seconds
{'output_type': 'input', 'verbose': 4, 'handle': <pylibraft.common.handle.Handle object at 0x7fd8addf73f0>, 'fit_intercept': True, 'normalize': False, 'tol': 0.001, 'solver': 'cd', 'max_iter': 1000, 'alpha': 0.001, 'selection': 'cyclic'}
cuML GPU training time: 0.8425 seconds
In general, 300 rows is not a dataset where I would expect massive GPU speedups, but things seem to be working fine. What could be happening is you could be running maybe into some JIT compilation issues? What GPU are you using? It would be very helpful if you could run this script and put the output here: https://github.com/rapidsai/cuml/blob/branch-24.10/print_env.sh

@dantegd Thanks for your reply. This is the output of print_env.sh

Click here to see environment details

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 DISTRIB_RELEASE=22.04
 DISTRIB_CODENAME=jammy
 DISTRIB_DESCRIPTION="Ubuntu 22.04.4 LTS"
 PRETTY_NAME="Ubuntu 22.04.4 LTS"
 NAME="Ubuntu"
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 VERSION="22.04.4 LTS (Jammy Jellyfish)"
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 +-----------------------------------------------------------------------------------------+
 | Processes:                                                                              |
 |  GPU   GI   CI        PID   Type   Process name                              GPU Memory |
 |        ID   ID                                                               Usage      |
 |=========================================================================================|
 +-----------------------------------------------------------------------------------------+

CPU
Architecture: x86_64
CPU op-mode(s): 32-bit, 64-bit
Address sizes: 46 bits physical, 57 bits virtual
Byte Order: Little Endian
CPU(s): 104
On-line CPU(s) list: 0-103
Vendor ID: GenuineIntel
Model name: Intel(R) Xeon(R) Gold 5320 CPU @ 2.20GHz
CPU family: 6
Model: 106
Thread(s) per core: 2
Core(s) per socket: 26
Socket(s): 2
Stepping: 6
BogoMIPS: 4400.00
Flags: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush dts acpi mmx fxsr sse sse2 ss ht tm pbe syscall nx pdpe1gb rdtscp lm constant_tsc art arch_perfmon pebs bts rep_good nopl xtopology nonstop_tsc cpuid aperfmperf pni pclmulqdq dtes64 monitor ds_cpl vmx smx est tm2 ssse3 sdbg fma cx16 xtpr pdcm pcid dca sse4_1 sse4_2 x2apic movbe popcnt tsc_deadline_timer aes xsave avx f16c rdrand lahf_lm abm 3dnowprefetch cpuid_fault epb cat_l3 invpcid_single ssbd mba ibrs ibpb stibp ibrs_enhanced tpr_shadow vnmi flexpriority ept vpid ept_ad fsgsbase tsc_adjust bmi1 avx2 smep bmi2 erms invpcid cqm rdt_a avx512f avx512dq rdseed adx smap avx512ifma clflushopt clwb intel_pt avx512cd sha_ni avx512bw avx512vl xsaveopt xsavec xgetbv1 xsaves cqm_llc cqm_occup_llc cqm_mbm_total cqm_mbm_local wbnoinvd dtherm ida arat pln pts avx512vbmi umip pku ospke avx512_vbmi2 gfni vaes vpclmulqdq avx512_vnni avx512_bitalg tme avx512_vpopcntdq rdpid md_clear pconfig flush_l1d arch_capabilities

Virtualization: VT-x
L1d cache: 2.4 MiB (52 instances)
L1i cache: 1.6 MiB (52 instances)
L2 cache: 65 MiB (52 instances)
L3 cache: 78 MiB (2 instances)
NUMA node(s): 2
NUMA node0 CPU(s): 0,2,4,6,8,10,12,14,16,18,20,22,24,26,28,30,32,34,36,38,40,42,44,46,48,50,52,54,56,58,60,62,64,66,68,70,72,74,76,78,80,82,84,86,88,90,92,94,96,98,100,102
NUMA node1 CPU(s): 1,3,5,7,9,11,13,15,17,19,21,23,25,27,29,31,33,35,37,39,41,43,45,47,49,51,53,55,57,59,61,63,65,67,69,71,73,75,77,79,81,83,85,87,89,91,93,95,97,99,101,103
Vulnerability Itlb multihit: Not affected
Vulnerability L1tf: Not affected
Vulnerability Mds: Not affected
Vulnerability Meltdown: Not affected
Vulnerability Spec store bypass: Mitigation; Speculative Store Bypass disabled via prctl and seccomp
Vulnerability Spectre v1: Mitigation; usercopy/swapgs barriers and __user pointer sanitization
Vulnerability Spectre v2: Mitigation; Enhanced IBRS, IBPB conditional, RSB filling
Vulnerability Srbds: Not affected
Vulnerability Tsx async abort: Not affected

 ***CMake***

g++

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 /opt/conda/bin/nvcc
 nvcc: NVIDIA (R) Cuda compiler driver
 Copyright (c) 2005-2024 NVIDIA Corporation
 Built on Thu_Jun__6_02:18:23_PDT_2024
 Cuda compilation tools, release 12.5, V12.5.82
 Build cuda_12.5.r12.5/compiler.34385749_0

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libnvjitlink 12.5.82 he02047a_0 conda-forge
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pip 24.2 pyhd8ed1ab_0 conda-forge
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proj 9.3.0 h1d62c97_2 conda-forge
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pyaml 24.7.0 pypi_0 pypi
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pygments 2.18.0 pyhd8ed1ab_0 conda-forge
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pynvjitlink 0.3.0 py39hd269673_0 rapidsai
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pyogrio 0.7.2 py39hcfcd403_1 conda-forge
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python 3.9.19 h0755675_0_cpython conda-forge
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rapids-dask-dependency 24.08.00 py_0 rapidsai
rapids-xgboost 24.08.00 cuda12_py39_240808_g86654f0_0 rapidsai
rdma-core 53.0 he02047a_0 conda-forge
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referencing 0.35.1 pyhd8ed1ab_0 conda-forge
reproc 14.2.4.post0 hd590300_1 conda-forge
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ruamel.yaml 0.18.6 py39hd1e30aa_0 conda-forge
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scikit-optimize 0.10.2 pypi_0 pypi
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typing_extensions 4.12.2 pyha770c72_0 conda-forge
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tzcode 2024a h3f72095_0 conda-forge
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uc-micro-py 1.0.3 pyhd8ed1ab_0 conda-forge
ucx 1.15.0 hda83522_8 conda-forge
ucx-proc 1.0.0 gpu rapidsai
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ucxx 0.39.01 cuda12_py3.9_240807_g19a66b4_0 rapidsai
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xgboost 2.1.1 rapidsai_pyh26c0410_4 rapidsai
xorg-kbproto 1.0.7 h7f98852_1002 conda-forge
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xorg-libsm 1.2.4 h7391055_0 conda-forge
xorg-libx11 1.8.9 h8ee46fc_0 conda-forge
xorg-libxau 1.0.11 hd590300_0 conda-forge
xorg-libxdmcp 1.1.3 h7f98852_0 conda-forge
xorg-libxext 1.3.4 h0b41bf4_2 conda-forge
xorg-libxrender 0.9.11 hd590300_0 conda-forge
xorg-renderproto 0.11.1 h7f98852_1002 conda-forge
xorg-xextproto 7.3.0 h0b41bf4_1003 conda-forge
xorg-xproto 7.0.31 h7f98852_1007 conda-forge
xyzservices 2024.6.0 pyhd8ed1ab_0 conda-forge
xz 5.2.6 h166bdaf_0 conda-forge
yaml 0.2.5 h7f98852_2 conda-forge
yaml-cpp 0.8.0 h59595ed_0 conda-forge
yarl 1.9.4 py39hd1e30aa_0 conda-forge
zeromq 4.3.5 h75354e8_4 conda-forge
zict 3.0.0 pyhd8ed1ab_0 conda-forge
zipp 3.20.0 pyhd8ed1ab_0 conda-forge
zlib 1.3.1 h4ab18f5_1 conda-forge
zstandard 0.23.0 py39h623c9ba_0 conda-forge
zstd 1.5.6 ha6fb4c9_0 conda-forge

Could you please help me see what the possible errors are and how I can fix them next?

JiayuChen02 added ? - Needs Triage Need team to review and classify question Further information is requested labels Sep 24, 2024

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[QST]Why is cuml GPU dozens of times slower than scikit-learn CPU in Lasso，Ridge and ElasticNet models? #6080

[QST]Why is cuml GPU dozens of times slower than scikit-learn CPU in Lasso，Ridge and ElasticNet models? #6080

JiayuChen02 commented Sep 24, 2024

dantegd commented Sep 24, 2024

JiayuChen02 commented Sep 25, 2024 •

edited

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[QST]Why is cuml GPU dozens of times slower than scikit-learn CPU in Lasso，Ridge and ElasticNet models? #6080

[QST]Why is cuml GPU dozens of times slower than scikit-learn CPU in Lasso，Ridge and ElasticNet models? #6080

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JiayuChen02 commented Sep 24, 2024

dantegd commented Sep 24, 2024

JiayuChen02 commented Sep 25, 2024 • edited Loading

JiayuChen02 commented Sep 25, 2024 •

edited

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