My blog site was shutdown for quite a while for certain reasons. However I strongly feel this is a good way for me to capture what I learned along the way and share with anyone who might feel helpful for them. Thanks to all people who provided postivie feedback. After clearup the issue and I finally decide to continue working on my blog site. First thing I do is to update my blog development enviroment which is Jekyll, installed in ubuntu 16.04 VM.
Machine Learning has been a very hot field. Big players like Amazon, Google, they host machine learning infrastructure in the cloud, which is very nice thing, except it can be very expensive to use it. I’m always curious if I can build one by myself: a relative powerful machine learning platform. The following shows what I achieved using UCS220-M5 with Tesla-P4 GPU.
Parallel computing using general purpose GPU is really taking off with advancement of technology from Nvidia, AMD, Intel. Especially Nvidia is dominating the field with variety of GPU offering and also software infrastructure CUDA (initially called as Compute Unified Device Architecture), which is a parallel computing platform and application programming interface (API) model. In this article, I share how GPU programming with CUDA looks like using UCS server with Nvidia GPU GRID K1
Serial Peripheral Interface (SPI) flash memory is often used in an embedded Linux box to hold bootloader. During very initial board bringup, SPI flash memory needs to be programmed properly using device like Dediprog, otherwise the box won’t boot. After the box is up running linux, often there is need to access SPI flash, e.g. upgrade new version of bootloader from linux. I will share what needs to be done for that.
