Posts in GPU Computing

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.

A Good Introduction to C++ AMP

“C++ AMP: Accelerated Massive Parallelism with Microsoft Visual C++” is a good introduction to programming with C++ AMP. Unfortunately, at the time of this review, it is the only book...

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.

The Best Book on OpenCL 1.x

What makes “OpenCL in Action: How to Accelerate Graphics and Computation” special is that the author, Matthew Scarpino, truly tries to explain the programs. You learn the reasons behind his...

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.

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.

Technical proofer of 'OpenCL in Action'

The OpenCL standard is not always easy and the technical proof was a challenge, because the code had to run on different os'es and platforms.

The best introduction to CUDA

“Cuda by Example” is the perfect start to CUDA for beginners. However, even some advanced users and professionals might enjoy the clear explanations and examples.

CUDA Real-Time Ray Tracer

During the christmas holidays i rewrote my ray tracer for the NVIDIA CUDA architecture.

Compiling OpenCL programs on Mac OS X Snow Leopard

I installed Snow Leopard on my laptop yesterday. I was very curious about OpenCL and installed the drivers and the GPU Computing SDK from NVIDIA.