DEVELOPER BLOG

Mark Ebersole

As CUDA Educator at NVIDIA, Mark Ebersole teaches developers and programmers about the NVIDIA CUDA parallel computing platform and programming model, and the benefits of GPU computing. With more than ten years of experience as a low-level systems programmer, Mark has spent much of his time at NVIDIA as a GPU systems diagnostics programmer in which he developed a tool to test, debug, validate, and verify GPUs from pre-emulation through bringup and into production. Before joining NVIDIA, he worked for IBM developing Linux drivers for the IBM iSeries server. Mark holds a BS degree in math and computer science from St. Cloud State University.

Posts by Mark Ebersole

AI / Deep Learning

Learn GPU Computing with Hands-On Labs at GTC 2015

There are almost 2400 lab hours of hands-on accelerated computing training available at GTC 2015, including a new Deep Learning track and self-paced labs. 4 MIN READ
HPC

Learn GPU Programming in Your Browser with NVIDIA Hands-On Labs

As CUDA Educator at NVIDIA, I work to give access to massively parallel programming education & training to everyone, whether or not they have access to GPUs in… 3 MIN READ
Artificial Intelligence

CUDACasts Episode 21: Porting a simple OpenCV sample to the Jetson TK1 GPU

In the previous CUDACasts episode, we saw how to flash your Jetson TK1 to the latest release of Linux4Tegra, and install both the CUDA toolkit and OpenCV SDK. < 1
Accelerated Computing

CUDACasts Episode 20: Getting started with Jetson TK1 and OpenCV

The Jetson TK1 development kit has fast become a must-have for mobile and embedded parallel computing due the amazing level of performance packed into such a… < 1
Accelerated Computing

CUDACasts Episode 19: CUDA 6 Guided Performance Analysis with the Visual Profiler

One of the main reasons for accelerating code on an NVIDIA GPU is for an increase in application performance. This is why it's important to use the best tools… < 1
Accelerated Computing

CUDACasts Episode 18: CUDA 6.0 Unified Memory

CUDA 6 introduces Unified Memory, which dramatically simplifies memory management for GPU computing. Now you can focus on writing parallel kernels when porting… < 1