Technical

Want to get an overview of what Heterogeneous Systems Architecture (HSA) does, or want to know what terminology has changed since version 1.0? Read further. Back in 2012 the goals for HSA were high. The group tried to design a system where CPU and GPU would work together in an efficient way. In the 2013/2014 time-frame you’ll find lots of articles around the

10 years ago we had CPUs from Intel and AMD and GPUs from ATI and NVidia. There was even another CPU-makers VIA, and GPU-makers S3 and Matrox. Things are different now. Below I want to shortly discuss the most noticeable processors from each of the big three. The reason for this blog-post is that many processors are relatively unknown, and several

During the panel discussion some very interesting questions were asked, I’d like to share with you. Should the Khronos group poll the community more often about the future of OpenCL? I asked it on twitter, and this is the current result: Khronos needs more feedback from OpenCL developers, to better serve the user base. Tell

Both CUDA and OpenCL are well-known GPGPU-languages. Unfortunately there are some slight differences between the languages, which are shown below. You might have heard of HiP, the language that AMD made to support both modern AMD Fiji GPUs and CUDA-devices. CUDA can be (mostly automatically) translated to HiP and from that moment your code also

The coming month we’re travelling every week. This generates are a lot of opportunities where you can meet the StreamHPC team! For appointments, send an email to contact@streamhpc.com. Meet us at ParallelCon (6 April 2016, Heidelberg, Germany). Besides the crash course (see below), we also have a talk on Vulkan. Crash Course OpenCL @ ParallelCon (8 April 2016, Heidelberg, Germany). This is part

If one company is bad at bragging, then it’s AMD. It’s two main competitors are a lot better in that – NVIDIA even bragged about their upcoming GPUs having HBM. So I was surprised that recently I encountered a nice infographic, where AMD was actually bragging. And they deserved to do it! I wanted to have comparisons with Intel/Micron’s

We ask all our customers if we could use their story on our webpage. For competition reasons, this is often not possible. The people of CEWE Stiftung & Co. KGaA were so kind to share his experience since he did a OpenCL training with us and we reviewed his code. Enjoy his story on his experience from the

Random numbers are important elements in stochastic simulations, but they also show up in machine learning and applications of Monte Carlo methods such as within computational finances, fluid dynamics and molecular dynamics. These are classical fields in high-performance computing, which StreamHPC has experience in. A common problem when porting traditional software in these fields to

Looking around in our code and on our intranet, I found lots of great and unique solutions to get the most performance out. Ofcourse we cannot share all of those, but there are some code-snippets that just have to get out. Below is one of them. In OpenCL there is only atomic_add or atomic_mul for ints, and

If there would be one rule to get the best performance, then it’s avoiding data-transfers. Therefore it’s important to have lots of bandwidth and GFLOPS per processor, and not simply add up those numbers. Everybody who has worked with MPI, knows why: transferring data between processors can totally kill the performance. So the more is packed in one