渚  直樹

渚 直樹

1653748200

【知っておきたいIT用語シリーズ】CPU、GPU、TPU の違い

この記事では、CPU、GPU、TPUの違いは何か?ということを説明します。

GPU

GPUは、グラフィック処理や数値計算等で使用される専用メモリを備えた特殊なプロセッサです。GPUは単一処理に特化しており、SIMD(Single Instruction and Multi Data)アーキテクチャ用に設計されています。そのため、GPUは同種の計算を並列に実行(単一の命令で複数のデータを処理)します。

特に深層学習ネットワークでは数百万のパラメータを扱うので、多数の論理コア(演算論理ユニット(ALU)制御ユニットとメモリキャッシュ)を採用しているGPUが重要な役割を果たします。GPUには多数のコアが含まれているため、複数の並列処理を行列計算で高速に計算可能です。

TPU

TPUは、Google社から2016年5月、Google I/O(Google社が毎年開催している開発者向けカンファレンス)で発表されました(すでに同社のデータセンター内で1年以上使用されていたとのことです)。

TPUは、ニューラルネットワークや機械学習のタスクに特化して設計されており、2018年からはサードパーティでも利用可能です。

Google社は、Googleストリートビューのテキスト処理にTPUを使用してストリートビューのデータベース内のすべてのテキストを5日間で発見し、Google Photosでは単一のTPUで1日で1億枚以上の写真を処理できたと発表しています。また、同社の機械学習ベースの検索エンジンアルゴリズム「RankBrain」でも、検索結果を提供するためにTPUを利用しています。

知っておきたいIT用語シリーズ


#CPU #GPU #TPU

What is GEEK

Buddha Community

【知っておきたいIT用語シリーズ】CPU、GPU、TPU の違い
渚  直樹

渚 直樹

1653748200

【知っておきたいIT用語シリーズ】CPU、GPU、TPU の違い

この記事では、CPU、GPU、TPUの違いは何か?ということを説明します。

GPU

GPUは、グラフィック処理や数値計算等で使用される専用メモリを備えた特殊なプロセッサです。GPUは単一処理に特化しており、SIMD(Single Instruction and Multi Data)アーキテクチャ用に設計されています。そのため、GPUは同種の計算を並列に実行(単一の命令で複数のデータを処理)します。

特に深層学習ネットワークでは数百万のパラメータを扱うので、多数の論理コア(演算論理ユニット(ALU)制御ユニットとメモリキャッシュ)を採用しているGPUが重要な役割を果たします。GPUには多数のコアが含まれているため、複数の並列処理を行列計算で高速に計算可能です。

TPU

TPUは、Google社から2016年5月、Google I/O(Google社が毎年開催している開発者向けカンファレンス)で発表されました(すでに同社のデータセンター内で1年以上使用されていたとのことです)。

TPUは、ニューラルネットワークや機械学習のタスクに特化して設計されており、2018年からはサードパーティでも利用可能です。

Google社は、Googleストリートビューのテキスト処理にTPUを使用してストリートビューのデータベース内のすべてのテキストを5日間で発見し、Google Photosでは単一のTPUで1日で1億枚以上の写真を処理できたと発表しています。また、同社の機械学習ベースの検索エンジンアルゴリズム「RankBrain」でも、検索結果を提供するためにTPUを利用しています。

知っておきたいIT用語シリーズ


#CPU #GPU #TPU

Nat  Kutch

Nat Kutch

1596889920

CPU / GPU/ TPU — ML perspective

As a Machine learning Enthusiast who has been trying to improvise performance of the learning models, We all have been at a point where the performance hit a cap and started to experience various degrees of processing lag.

Tasks that used to take minutes with smaller training dataset now started taking hours together to train large dataset . And to solve these issues We have to upgrade our hardware accordingly and for that purpose we need to understand the difference between different Processing Units.

Starting with the Central Processing Unit(CPU) which is essentially the brain of the computing device, carrying out the instructions of a program by performing control, logical, and input/output (I/O) operations.

CPU is used for General Purpose programming problems.

A processor designed to solve every computational problem in general fashion. The Memory and Cache are designed to be optimal for any general programming problem and can handle different programming languages like(C,Java,Python).

The smallest unit of data handled at a time in CPU is a Scalar which is 1x1 dimensional data.

Image for post

Now talking about GPU ,Graphical processing Unit is familiar name to many gamers reading this article. Initially designed mainly as dedicated graphical rendering workhorses of computer games, GPUs were later enhanced to accelerate others like photo/video editing, animation, research and other analytical software, which need to plot graphical results with a huge amount of data.

CPUs are best at handling single, more complex calculations sequentially, while GPUs are better at handling multiple but simpler calculations in parallel.

As a general rule, GPUs are a safer bet for fast machine learning because, at its heart, data science model training is composed of simple matrix math calculations, the speed of which can be greatly enhanced if the computations can be carried out in parallel and for this reason GPU has thousands of ALU in single processor, that means you can perform thousands of multiplications and addition simultaneously.

#cpu #tpu #data-science #machine-learning #gpu #deep learning

Xgboost regression training on CPU and GPU in python

How to unlock the fast training of xgboost models in Python using a GPU

In this article, I want to go along with the steps that are needed to train xgboost models using a GPU and not the default CPU.

Additionally, an analysis of how the training speeds are influenced by the sizes of the matrices and certain hyperparameters is presented as well.

Feel free to clone or fork all the code from here: https://github.com/Eligijus112/xgboost-regression-gpu.

In order to train machine learning models on a GPU you need to have on your machine, well, a Graphical Processing Unit — GPU - a graphics card. By default, machine learning frameworks search for a Central Processing Unit — CPU — inside a computer.

#machine-learning #python #gpu #regression #cpu #xgboost regression training on cpu and gpu in python

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#gpu mining os #gpu mining on mac #gaming on mining gpu #gpu mining september 2019 #testing used mining gpu

Nat  Grady

Nat Grady

1668002820

H2o4gpu: H2Oai GPU Edition

H2O4GPU

H2O4GPU is a collection of GPU solvers by H2Oai with APIs in Python and R. The Python API builds upon the easy-to-use scikit-learn API and its well-tested CPU-based algorithms. It can be used as a drop-in replacement for scikit-learn (i.e. import h2o4gpu as sklearn) with support for GPUs on selected (and ever-growing) algorithms. H2O4GPU inherits all the existing scikit-learn algorithms and falls back to CPU algorithms when the GPU algorithm does not support an important existing scikit-learn class option. The R package is a wrapper around the H2O4GPU Python package, and the interface follows standard R conventions for modeling.

Daal library added for CPU, currently supported only x86_64 architecture.

Requirements

PC running Linux with glibc 2.17+

Install CUDA with bundled display drivers ( CUDA 8 or CUDA 9 or CUDA 9.2) or CUDA 10)

Python shared libraries (e.g. On Ubuntu: sudo apt-get install libpython3.6-dev)

When installing, choose to link the cuda install to /usr/local/cuda . Ensure to reboot after installing the new nvidia drivers.

Nvidia GPU with Compute Capability >= 3.5 (Capability Lookup).

For advanced features, like handling rows/32 > 2^16 (i.e., rows > 2,097,152) in K-means, need Capability >= 5.2

For building the R package, libcurl4-openssl-dev, libssl-dev, and libxml2-dev are needed.

User Installation

Note: Installation steps mentioned below are for users planning to use H2O4GPU. See DEVEL.md for developer installation.

H2O4GPU can be installed using either PIP or Conda

Prerequisites

Add to ~/.bashrc or environment (set appropriate paths for your OS):

export CUDA_HOME=/usr/local/cuda # or choose /usr/local/cuda9 for cuda9 and /usr/local/cuda8 for cuda8
export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:$CUDA_HOME/lib64/:$CUDA_HOME/lib/:$CUDA_HOME/extras/CUPTI/lib64
  • Install OpenBlas dev environment:
sudo apt-get install libopenblas-dev pbzip2

If you are building the h2o4gpu R package, it is necessary to install the following dependencies:

sudo apt-get -y install libcurl4-openssl-dev libssl-dev libxml2-dev

PIP install

Download the Python wheel file (For Python 3.6):

Start a fresh pyenv or virtualenv session.

Install the Python wheel file. NOTE: If you don't use a fresh environment, this will overwrite your py3nvml and xgboost installations to use our validated versions.

pip install h2o4gpu-0.3.0-cp36-cp36m-linux_x86_64.whl

Conda installation

Ensure you meet the Requirements and have installed the Prerequisites.

If not already done you need to install conda package manager. Ensure you test your conda installation

H204GPU packages for CUDA8, CUDA 9 and CUDA 9.2 are available from h2oai channel in anaconda cloud.

Create a new conda environment with H2O4GPU based on CUDA 9.2 and all its dependencies using the following command. For other cuda versions substitute the package name as needed. Note the requirement for h2oai and conda-forge channels.

conda create -n h2o4gpuenv -c h2oai -c conda-forge -c rapidsai h2o4gpu-cuda10

Once the environment is created activate it source activate h2o4gpuenv.

To test, start an interactive python session in the environment and follow the steps in the Test Installation section below.

h2o4gpu R package

At this point, you should have installed the H2O4GPU Python package successfully. You can then go ahead and install the h2o4gpu R package via the following:

if (!require(devtools)) install.packages("devtools")
devtools::install_github("h2oai/h2o4gpu", subdir = "src/interface_r")

Detailed instructions can be found here.

Test Installation

To test your installation of the Python package, the following code:

import h2o4gpu
import numpy as np

X = np.array([[1.,1.], [1.,4.], [1.,0.]])
model = h2o4gpu.KMeans(n_clusters=2,random_state=1234).fit(X)
model.cluster_centers_

should give input/output of:

>>> import h2o4gpu
>>> import numpy as np
>>>
>>> X = np.array([[1.,1.], [1.,4.], [1.,0.]])
>>> model = h2o4gpu.KMeans(n_clusters=2,random_state=1234).fit(X)
>>> model.cluster_centers_
array([[ 1.,  1.  ],
       [ 1.,  4.  ]])

To test your installation of the R package, try the following example that builds a simple XGBoost random forest classifier:

library(h2o4gpu)

# Setup dataset
x <- iris[1:4]
y <- as.integer(iris$Species) - 1

# Initialize and train the classifier
model <- h2o4gpu.random_forest_classifier() %>% fit(x, y)

# Make predictions
predictions <- model %>% predict(x)

Next Steps

For more examples using Python API, please check out our Jupyter notebook demos. To run the demos using a local wheel run, at least download src/interface_py/requirements_runtime_demos.txt from the Github repo and do:

pip install -r src/interface_py/requirements_runtime_demos.txt

and then run the jupyter notebook demos.

For more examples using R API, please visit the vignettes.

Running Jupyter Notebooks

You can run Jupyter Notebooks with H2O4GPU in the below two ways

Creating a Conda Environment

Ensure you have a machine that meets the Requirements and Prerequisites mentioned above.

Next follow Conda installation instructions mentioned above. Once you have activated the environment, you will need to downgrade tornado to version 4.5.3 refer issue #680. Start Jupyter notebook, and navigate to the URL shown in the log output in your browser.

source activate h2o4gpuenv
conda install tornado==4.5.3
jupyter notebook --ip='*' --no-browser

Start a Python 3 kernel, and try the code in example notebooks

Using precompiled docker image

Requirements:

Download the Docker file (for linux_x86_64):

  • Bleeding edge (changes with every successful master branch build):

Load and run docker file (e.g. for bleeding-edge of cuda92):

jupyter notebook --generate-config
echo "c.NotebookApp.allow_remote_access = False >> ~/.jupyter/jupyter_notebook_config.py # Choose True if want to allow remote access
pbzip2 -dc h2o4gpu-0.3.0.10000-cuda92-runtime.tar.bz2 | nvidia-docker load
mkdir -p log ; nvidia-docker run --name localhost --rm -p 8888:8888 -u `id -u`:`id -g` -v `pwd`/log:/log -v /home/$USER/.jupyter:/jupyter --entrypoint=./run.sh opsh2oai/h2o4gpu-0.3.0.10000-cuda92-runtime &
find log -name jupyter* -type f -printf '%T@ %p\n' | sort -k1 -n | awk '{print $2}' | tail -1 | xargs cat | grep token | grep http | grep -v NotebookApp

Copy/paste the http link shown into your browser. If the "find" command doesn't work, look for the latest jupyter.log file and look at contents for the http link and token.

If the link shows no token or shows ... for token, try a token of "h2o" (without quotes). If running on your own host, the weblink will look like http://localhost:8888:token with token replaced by the actual token.

This container has a /demos directory which contains Jupyter notebooks and some data.

Plans

The vision is to develop fast GPU algorithms to complement the CPU algorithms in scikit-learn while keeping full scikit-learn API compatibility and scikit-learn CPU algorithm capability. The h2o4gpu Python module is to be used as a drop-in-replacement for scikit-learn that has the full functionality of scikit-learn's CPU algorithms.

Functions and classes will be gradually overridden by GPU-enabled algorithms (unless n_gpu=0 is set and we have no CPU algorithm except scikit-learn's). The CPU algorithms and code initially will be sklearn, but gradually those may be replaced by faster open-source codes like those in Intel DAAL.

This vision is currently accomplished by using the open-source scikit-learn and xgboost and overriding scikit-learn calls with our own GPU versions. In cases when our GPU class is currently incapable of an important scikit-learn feature, we revert to the scikit-learn class.

As noted above, there is an R API in development, which will be released as a stand-alone R package. All algorithms supported by H2O4GPU will be exposed in both Python and R in the future.

Another primary goal is to support all operations on the GPU via the GOAI initiative. This involves ensuring the GPU algorithms can take and return GPU pointers to data instead of going back to the host. In scikit-learn API language these are called fit_ptr, predict_ptr, transform_ptr, etc., where ptr stands for memory pointer.

RoadMap

2019 Q2:

  • A new processing engine that allows to scale beyond GPU memory limits
  • k-Nearest Neighbors
  • Matrix Factorization
  • Factorization Machines
  • API Support: GOAI API support
  • Data.table support

More precise information can be found in the milestone's list.

Solver Classes

Among others, the solver can be used for the following classes of problems

  • GLM: Lasso, Ridge Regression, Logistic Regression, Elastic Net Regulariation
  • KMeans
  • Gradient Boosting Machine (GBM) via XGBoost
  • Singular Value Decomposition(SVD) + Truncated Singular Value Decomposition
  • Principal Components Analysis(PCA)

Benchmarks

Our benchmarking plan is to clearly highlight when modeling benefits from the GPU (usually complex models) or does not (e.g. one-shot simple models dominated by data transfer).

We have benchmarked h2o4gpu, scikit-learn, and h2o-3 on a variety of solvers. Some benchmarks have been performed for a few selected cases that highlight the GPU capabilities (i.e. compute or on-GPU memory operations dominate data transfer to GPU from host):

Benchmarks for GLM, KMeans, and XGBoost for CPU vs. GPU.

A suite of benchmarks are computed when doing "make testperf" from a build directory. These take all of our tests and benchmarks h2o4gpu against h2o-3. These will soon be presented as a live commit-by-commit streaming plots on a website.

Contributing

Please refer to our CONTRIBUTING.md and DEVEL.md for instructions on how to build and test the project and how to contribute. The h2o4gpu Gitter chatroom can be used for discussion related to open source development.

GitHub issues are used for bugs, feature and enhancement discussion/tracking.

Questions

Please ask all code-related questions on StackOverflow using the "h2o4gpu" tag.

Questions related to the roadmap can be directed to the developers on Gitter.

Troubleshooting

FAQ

References

  1. Parameter Selection and Pre-Conditioning for a Graph Form Solver -- C. Fougner and S. Boyd
  2. Block Splitting for Distributed Optimization -- N. Parikh and S. Boyd
  3. Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers -- S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein
  4. Proximal Algorithms -- N. Parikh and S. Boyd

Download Details:

Author: h2oai
Source Code: https://github.com/h2oai/h2o4gpu 
License: Apache-2.0 license

#r #python #machinelearning #cpu #gpu