Finally – GPUs are Coming to Azure!

This entry was posted in category Blog on October 1, 2015 by dani

GPU cloud computing is gaining more and more momentum because a growing number of applications and use cases rely on fast enterprise GPU hardware, such as deep learning, applied to image and speech recognition or natural language processing, data mining, photo-realistic real-time rendering, etc. Some of these applications also benefit from scaling to multi-GPU servers […]

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.NET GPU Cloud Computing with Alea GPU

This entry was posted in category Blog on August 24, 2015 by dani

Cloud computing is all about making resources available on demand, and its availability, flexibility, and lower cost has helped it take commercial computing by storm. At the Microsoft Build 2015 conference in San Francisco Microsoft revealed that its Azure cloud computing platform is averaging over 90 thousand new customers per month; contains more than 1.4 […]

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Don’t Get Lost in the Forest IV – Performance Test

This entry was posted in category Blog on July 20, 2015 by dani

Having discussed and implemented a random forest algorithm in the last three posts, we will finalize this series by comparing the performance of our two implementations with the random forest implementation of the Python package sklearn. For the test we will use a randomly created data set consisting of 20000 samples with 20 features and […]

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Don’t Get Lost in the Forest III – The GPU Implementation

This entry was posted in category Blog on July 7, 2015 by dani

In the previous post we looked at the CPU implementation of a random forest training algorithm. We also discussed two parallelization strategies at different levels: Build the independent trees in parallel. Search for the optimal split for all features in parallel. The first strategy is straightforward. Here we focus on the second strategy and discuss […]

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Don’t Get Lost in the Forest II – The CPU Implementation

This entry was posted in category Blog on July 2, 2015 by dani

This is the second post in a series about GPU accelerated random forests. In our last post we introduced random forests and described how to train them with an algorithm based on the split entropy. In this post we will focus on a CPU implementation and leave the GPU implementation for the next blog post. […]

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