Posts tagged with #nvidia-cuda

Machine learning library 'cuDNN' explained in iX Developer

It’s very difficult to implement algorithms optimally for GPUs. Therefore NVIDIA provides the library cuDNN for deep learning with neural networks. This article published in the german magazine iX Developer...

Talk about GPU computing with CUDA and OpenCL

In March 2017 I gave a talk about GPU computing with CUDA and OpenCL at the para//el 2017 conference in Heidelberg, Germany.

CUDA 8.0 on Mac OS/X

CUDA 8.0 on Mac OS X 10.12 gave me the following error:

Using a __device__ lambda in CUDA 8.0

In CUDA 8.0 C++ 11 lambdas can be used on the device. See the following code.

Heat diffusion on multiple GPUs

The implementation of the heat diffusion equation on multiple GPUs is explained in the talk (in german).

Talk at the parallel 2015 about Multi-GPU programming with Cuda, OpenCL and C++ AMP

At the parallel 2015 conference I gave an introduction into Multi-GPU programming with Cuda, OpenCL and C++ AMP.

Examples for parallelism: ray tracer on the GPU

I tested the parallel ray tracer on all NVIDIA cards i bought. This page contains the results.

Solid Optimization Tips for CUDA

While browsing through “CUDA Fortran for Scientists and Engineers”, I noticed that the authors provide valuable optimization tips. That piqued my curiosity.

Talk at parallel 2014 about GPU-Computing

At the parallel 2014 I gave an introduction into the optimization of GPU kernels.

Inspiration for CUDA Experts in the Natural Sciences

The book “GPU Computing Gems (Emerald Edition)” is a collection of 50 scientific articles about experiences using GPU computing in various subject areas.

Only recommended for die-hard CUDA fans

I had already read many articles from the series “CUDA, Supercomputing for the Masses” by Rob Farber on the Dr. Dobb’s website and was quite impressed. Back then in 2009,...

Best CUDA Book for Advanced Users

With CUDA, it’s easy to speed up a calculation by a factor of 2-5. How to achieve higher speedups is explained in “CUDA Programming” by Shane Cook.

Good Tips for Performance Optimization

In “The CUDA Handbook”, Nicholas Wilt proves himself to be very competent and keen to experiment with kernel optimization. Advanced readers will find this book very beneficial.

Currently the Best Presentation of Patterns for Shared Mutability

If you want to bring a bit of systematism to your parallel programming skills, “Structured Parallel Programming: Patterns for Efficient Computation” is the book to start with.

Two articles in iX Developer: GPU Computing mit CUDA

"Massiv parallel - Grundlagen der GPU Programmierung"

"GPU-Programmierung in der Praxis mit CUDA"

Talk at parallel 2012 about GPU-Computing

At the parallel 2012 I presented my experiences and learnings I gathered in GPU computing projects.

Essential for Performance Optimization

What distinguishes “Programming Massively Parallel Processors: A Hands-On Approach” from other books is the precise description of how the hardware works.