Jeff Larkin

Jeff is a Principal HPC Application Architect in NVIDIA's HPC Software team. He is passionate about the advancement and adoption of parallel programming models for High Performance Computing. He was previously a member of NVIDIA's Developer Technology group, specializing in performance analysis and optimization of high performance computing applications. Jeff is also the chair of the OpenACC technical committee and has worked in both the OpenACC and OpenMP standards bodies. Before joining NVIDIA, Jeff worked in the Cray Supercomputing Center of Excellence, located at Oak Ridge National Laboratory.

Posts by Jeff Larkin

Data Center / Cloud Mar 06, 2024

How to Accelerate Quantitative Finance with ISO C++ Standard Parallelism

Quantitative finance libraries are software packages that consist of mathematical, statistical, and, more recently, machine learning models designed for use in... 10 MIN READ
Simulation / Modeling / Design Nov 13, 2023

Simplifying GPU Programming for HPC with NVIDIA Grace Hopper Superchip

The new hardware developments in NVIDIA Grace Hopper Superchip systems enable some dramatic changes to the way developers approach GPU programming. Most... 17 MIN READ
Simulation / Modeling / Design Jun 12, 2022

Using Fortran Standard Parallel Programming for GPU Acceleration

Standard languages have begun adding features that compilers can use for accelerated GPU and CPU parallel programming, for instance, do concurrent loops and... 12 MIN READ
Four panels vertically laid out each showing a simulation with a black background
Simulation / Modeling / Design Apr 18, 2022

Multi-GPU Programming with Standard Parallel C++, Part 2

It may seem natural to expect that the performance of your CPU-to-GPU port will range below that of a dedicated HPC code. After all, you are limited by the... 13 MIN READ
Four panels vertically laid out each showing a simulation with a black background
Simulation / Modeling / Design Apr 18, 2022

Multi-GPU Programming with Standard Parallel C++, Part 1

The difficulty of porting an application to GPUs varies from one case to another. In the best-case scenario, you can accelerate critical code sections by... 17 MIN READ
Four panels vertically laid out each showing a simulation with a black background
Data Center / Cloud Jan 12, 2022

Developing Accelerated Code with Standard Language Parallelism

The NVIDIA platform is the most mature and complete platform for accelerated computing. In this post, I address the simplest, most productive, and most portable... 12 MIN READ