Mark Harris

Mark is an NVIDIA Distinguished Engineer working on RAPIDS. Mark has over twenty years of experience developing software for GPUs, ranging from graphics and games, to physically-based simulation, to parallel algorithms and high-performance computing. While a Ph.D. student at The University of North Carolina he recognized a nascent trend and coined a name for it: GPGPU (General-Purpose computing on Graphics Processing Units).

Posts by Mark Harris

AI / Deep Learning

Fast, Flexible Allocation for NVIDIA CUDA with RAPIDS Memory Manager

When I joined the RAPIDS team in 2018, NVIDIA CUDA device memory allocation was a performance problem. RAPIDS cuDF allocates and deallocates memory at high… 24 MIN READ

CUDA Pro Tip: The Fast Way to Query Device Properties

CUDA applications often need to know the maximum available shared memory per block or to query the number of multiprocessors in the active GPU. One way to do… 3 MIN READ
AI / Deep Learning

RAPIDS Accelerates Data Science End-to-End

Today's data science problems demand a dramatic increase in the scale of data as well as the computational power required to process it. Unfortunately… 10 MIN READ
Accelerated Computing

Cooperative Groups: Flexible CUDA Thread Programming

In efficient parallel algorithms, threads cooperate and share data to perform collective computations. To share data, the threads must synchronize. 16 MIN READ

Unified Memory for CUDA Beginners

This post introduces CUDA programming with Unified Memory, a single memory address space that is accessible from any GPU or CPU in a system. 16 MIN READ
Accelerated Computing

CUDA 9 Features Revealed: Volta, Cooperative Groups and More

The CUDA 9 release includes support for Volta GPUs, Cooperative Groups programming model extensions, faster libraries, and improved developer tools. 17 MIN READ