High Performance Computing Applied to Logistic Regression: A CPU and GPU
Implementation Comparison
- URL: http://arxiv.org/abs/2308.10037v1
- Date: Sat, 19 Aug 2023 14:49:37 GMT
- Title: High Performance Computing Applied to Logistic Regression: A CPU and GPU
Implementation Comparison
- Authors: Nechba Mohammed, Mouhajir Mohamed, Sedjari Yassine
- Abstract summary: We present a versatile GPU-based parallel version of Logistic Regression (LR)
Our implementation is a direct translation of the parallel Gradient Descent Logistic Regression algorithm proposed by X. Zou et al.
Our method is particularly advantageous for real-time prediction applications like image recognition, spam detection, and fraud detection.
- Score: 0.0
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a versatile GPU-based parallel version of Logistic Regression
(LR), aiming to address the increasing demand for faster algorithms in binary
classification due to large data sets. Our implementation is a direct
translation of the parallel Gradient Descent Logistic Regression algorithm
proposed by X. Zou et al. [12]. Our experiments demonstrate that our GPU-based
LR outperforms existing CPU-based implementations in terms of execution time
while maintaining comparable f1 score. The significant acceleration of
processing large datasets makes our method particularly advantageous for
real-time prediction applications like image recognition, spam detection, and
fraud detection. Our algorithm is implemented in a ready-to-use Python library
available at : https://github.com/NechbaMohammed/SwiftLogisticReg
Related papers
- Implementation and Analysis of GPU Algorithms for Vecchia Approximation [0.8057006406834466]
Vecchia Approximation is widely used to reduce the computational complexity and can be calculated with embarrassingly parallel algorithms.
While multi-core software has been developed for Vecchia Approximation, software designed to run on graphics processing units ( GPU) is lacking.
We show that our new method outperforms the other two and then present it in the GpGpU R package.
arXiv Detail & Related papers (2024-07-03T01:24:44Z) - INR-Arch: A Dataflow Architecture and Compiler for Arbitrary-Order
Gradient Computations in Implicit Neural Representation Processing [66.00729477511219]
Given a function represented as a computation graph, traditional architectures face challenges in efficiently computing its nth-order gradient.
We introduce INR-Arch, a framework that transforms the computation graph of an nth-order gradient into a hardware-optimized dataflow architecture.
We present results that demonstrate 1.8-4.8x and 1.5-3.6x speedup compared to CPU and GPU baselines respectively.
arXiv Detail & Related papers (2023-08-11T04:24:39Z) - Going faster to see further: GPU-accelerated value iteration and
simulation for perishable inventory control using JAX [5.856836693166898]
We use the Python library JAX to implement value iteration and simulators of the underlying Markov decision processes in a high-level API.
Our method can extend use of value iteration to settings that were previously considered infeasible or impractical.
We compare the performance of the optimal replenishment policies to policies, fitted using simulation optimization in JAX which allowed the parallel evaluation of multiple candidate policy parameters.
arXiv Detail & Related papers (2023-03-19T14:20:44Z) - PARTIME: Scalable and Parallel Processing Over Time with Deep Neural
Networks [68.96484488899901]
We present PARTIME, a library designed to speed up neural networks whenever data is continuously streamed over time.
PARTIME starts processing each data sample at the time in which it becomes available from the stream.
Experiments are performed in order to empirically compare PARTIME with classic non-parallel neural computations in online learning.
arXiv Detail & Related papers (2022-10-17T14:49:14Z) - Adaptive Elastic Training for Sparse Deep Learning on Heterogeneous
Multi-GPU Servers [65.60007071024629]
We show that Adaptive SGD outperforms four state-of-the-art solutions in time-to-accuracy.
We show experimentally that Adaptive SGD outperforms four state-of-the-art solutions in time-to-accuracy.
arXiv Detail & Related papers (2021-10-13T20:58:15Z) - Providing Meaningful Data Summarizations Using Examplar-based Clustering
in Industry 4.0 [67.80123919697971]
We show, that our GPU implementation provides speedups of up to 72x using single-precision and up to 452x using half-precision compared to conventional CPU algorithms.
We apply our algorithm to real-world data from injection molding manufacturing processes and discuss how found summaries help with steering this specific process to cut costs and reduce the manufacturing of bad parts.
arXiv Detail & Related papers (2021-05-25T15:55:14Z) - GPU-Accelerated Primal Learning for Extremely Fast Large-Scale
Classification [10.66048003460524]
One of the most efficient methods to solve L2-regularized primal problems, such as logistic regression and linear support vector machine (SVM) classification, is the widely used trust region Newton algorithm, TRON.
We show that using judicious GPU-optimization principles, TRON training time for different losses and feature representations may be drastically reduced.
arXiv Detail & Related papers (2020-08-08T03:40:27Z) - Kernel methods through the roof: handling billions of points efficiently [94.31450736250918]
Kernel methods provide an elegant and principled approach to nonparametric learning, but so far could hardly be used in large scale problems.
Recent advances have shown the benefits of a number of algorithmic ideas, for example combining optimization, numerical linear algebra and random projections.
Here, we push these efforts further to develop and test a solver that takes full advantage of GPU hardware.
arXiv Detail & Related papers (2020-06-18T08:16:25Z) - Heterogeneous CPU+GPU Stochastic Gradient Descent Algorithms [1.3249453757295084]
We study training algorithms for deep learning on heterogeneous CPU+GPU architectures.
Our two-fold objective -- maximize convergence rate and resource utilization simultaneously -- makes the problem challenging.
We show that the implementation of these algorithms achieves both faster convergence and higher resource utilization than on several real datasets.
arXiv Detail & Related papers (2020-04-19T05:21:20Z) - MPLP++: Fast, Parallel Dual Block-Coordinate Ascent for Dense Graphical
Models [96.1052289276254]
This work introduces a new MAP-solver, based on the popular Dual Block-Coordinate Ascent principle.
Surprisingly, by making a small change to the low-performing solver, we derive the new solver MPLP++ that significantly outperforms all existing solvers by a large margin.
arXiv Detail & Related papers (2020-04-16T16:20:53Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.