From spam filters to movie recommendation and face detection, nowadays machine learning algorithms are used everywhere to make the machine think for us. But, running these algorithms require high computation power and in most cases supercomputers. This is where the 500 core GPUs step in: GPUs have evolved to the point where real-world applications can be developed quickly and run orders of magnitude faster on hardware you can afford at home.
On this blog post I’m diving deeper into Thrust usage scenarios with a simple implementation of Monte Carlo simulation.
On this post I will give you some simple examples how to use the massively parallel GPU on your computer to generate uniformly distibuted psuedo-random numbers.
On this post I would like to give an entry level example how you can use NVIDIA CUDA technology to achieve better performance within C# with minimum possible amount of code.
On this blog I will be showing you the simplest way to take advantage of your hardware without introducing any code complexity.
On this post I will explain you a limitation about optional arguments and how you can prevent a headache on production environments.
On this post, I would like to talk about a technology which will simplify user access for developers by allowing building claims-aware application: Windows Identity Foundation (WIF).
I decided to write this post to explain what are some basic steps you have to follow which will help you to become a better software architect or better said deliver projects successfully
On this post I'll help you to make better decisions on using C# anonymous methods.