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

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CPU / GPU/ TPU — ML perspective
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

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

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

ML and Trading - Impact of Machine Learning in Trading | Mobinius

https://www.mobinius.com/blogs/impact-of-machine-learning-in-trading

#machine learning #ml-development-company #ml-development-solutions #ml-development-services #hire-ml-developers