AMD, ARM-vendors and Intel have been busy unifying CPU and GPU memories for years. It is not easy to design a model where 2 (or more) processors can access memory without dead-locking each other. NVIDIA just announced CUDA 6 and to my surprise includes “Unified Memory”. Am missing something completely, or did they just pass […]
CUDA 6 Unified Memory explained Read More »
Altera has just released their optimisation guide for OpenCL-on-FPGAs. It does not go into the howto’s of OpenCL, but assumes you have knowledge of the technology. Niether does it provide any information on the basics of Altera’s Stratix V or other FPGA. It is the first public optimisation document, so it is appreciated to send
Altera published their OpenCL-on-FPGA optimization guide Read More »
The past year you might not have heard much from OpenCL-on-ARM, besides the Arndale developer-board. You have heard just a small portion of what has been going on. Yesterday the (Linux) OpenCL-drivers for the Chromebook (which contains an ARM MALI T604) the have been released and several companies will launch products using OpenCL. Below are
Products using OpenCL on ARM MALI are coming Read More »
Altera has been very busy adding resources and has kicked off the beginning of June with opening up their OpenCL-program for the general public. Only Stratix V devices are supported, but that could change later. Below are all pages and PDFs concerning OpenCL I’ve found while searching Altera’s website. Evaluation of CPUs, GPUs, and FPGAs
Starting with Altera OpenCL-on-FPGAs Read More »
Which algorithms map is best to which accelerator? In other words: What kind of algorithms are faster when using accelerators and OpenCL/CUDA? Professor Wu Feng and his group from VirginiaTech took a close look at which types of algorithms were a good fit for vector-processors. This resulted in a document: “The 13 (computational) dwarves of
The 13 application areas where OpenCL and CUDA can be used Read More »
Bored at work? Go start working for one of the anti-boring GPU-expert companies: StreamHPC (Netherlands, EU), Appilo (Israel) or AccelerEyes (Georgia, US). We all look for people who know how to code GPUs. Experience is key, so you need to have at least one year of experience in GPU-programming. Submit your CV now and don’t
GPU-developers, work for StreamHPC and friends Read More »
GPUVerify is a tool for formal analysis of GPU kernels written in OpenCL and CUDA. The tool can prove that kernels are free from certain types of defect, such as data races and bugs. This is quite useful feedback for any GPU-programmer. Below you find a online version of the tool (please don’t break it!).
Verify your OpenCL and CUDA kernels online for race conditions Read More »
On the 20th of April 2013 there was an interesting discussion between Jan Gray and David Kanter. Jan is a specialist in C++ and FPGAs (twitter, homepage). David is a specialist in CPU and GPU architectures (twitter, homepage). Both know their ways well in the field of semiconductors. It is always a joy to follow their short
Scaling mobile GPUs to 1000 GFLOPS Read More »
In OpenCL large memory objects, residing in the main memory of the host or the global memory at the accelerator/GPU, need special treatment. First reason is that these memories are relatively slow. Second reason is that the most times serial copy of objects between these two memories take time. In this post I’d like to
OpenCL Basics: Flags for the creating memory objects Read More »
Important: this article was written before Intel “Haswell” and AMD “Richland” architectures came out. So you want to start developing for OpenCL? When you focus on developing OpenCL for X86, you have these three options: CPUs, GPUs and CPUs with and embedded GPU. This article is for you and represents the current state of hardware – if you
X86-workstation buying guide for OpenCL developers, Q1 2013 Read More »