The information you find everywhere: on Linux the current “radeon” and “fglrx” are being replaced by AMDGPU (graphics) and ROCm (compute) for HSA-enabled GPUs. As the whole AMD Linux driver team is seemingly working on getting the new and open source drivers ready, fglrx is now deprecated and will not get updates (or very late).
The CUDA page on Wikipedia includes a table listing Compute Capabilities. While verifying support for AMD Fiji GPUs, such as the Radeon Nano and FirePro S9300 X2, I became curious about how much functionality is still missing in OpenCL. After comparing the feature sets, I found that Fiji GPUs support all the required functionality. Read
CUDA Compute Capability 6.1 Features in OpenCL 2.0 Read More »
We could read here that software is critical for HPC – a market where accelerators/GPUs are used a lot. So all we need to do is to better support all GPU-developers as a whole, not? Unfortunately something else is happening. Each big corporation wants to have their own developers, not to be shared with the
Rant: No surprise there’s a shortage of good GPU-developers Read More »
From 24 to 28 October we give a 4-day training on OpenCL-on-FPGAs using Altera hardware. The learning goals are correctly writing OpenCL code for FPGAs, learning to work with Quartus and understanding the important optimisation techniques. The total costs are €2760 excluding VAT for the whole week ( 2 + 2 days of training, one pause day), including
4-day training on OpenCL-on-FPGAs, 24-28 October, Amsterdam Read More »
When we port software to the GPU or FPGA, testability is very important. A part of making the code testable, is getting its functionality fully under control. And you guessed already that run-time generated random numbers takes good attention. In a selection of past projects random numbers were generated on every run. Statistically the simulations were more correct,
Porting code that uses random numbers Read More »
In the first part of our two-part blog series, we have discussed how parallel computing applications can best use pseudo-random number generators (PRNGs) so as to benefit from parallel computing speedups, without negatively impacting the statistical properties of the random numbers generated. We have argued that index-based PRNGs (e.g., from the Random123 library), which do
Random Numbers in Parallel Computing: Generation and Reproducibility (Part 2) Read More »
In the past year we’ve been working on more internal projects and therefore we’re seeking strong GPU-coders (good OpenCL experience required) worldwide. This way you can combine staying close to your family and working with advanced technologies. You will be on the newly formed international team. Do understand that we have extra requirements for freelancers:
Strengthen our team as a remote worker (freelancer) Read More »
When you ask how fast code is, then we might not be able to answer that question. It depends on the data and the metric. In this article I’ll give an overview of different ways to describe speed and what metrics are used. I focus on two types of data-utilisations: Transfers. Data-movements through cables, interconnects,
Performance can be measured as Throughput, Latency or Processor Utilisation Read More »
Want to help build an important website? OpenCL.org’s components have been designed and partly built, but still a lot of work needs to be done. We’re seeking an intern (or “extern” when not in Amsterdam) who can help us build the site. This internship is not about GPUs! To complete the tasks, the following is
OpenCL.org internship/externship Read More »
For years we haven been complaining on this blog what AMD was lacking and what needed to be improved. And as you might have concluded from the title of this blogpost, there has been a lot of progress. AMD is back! It will all come together in the beginning of 2017, but you’ll see a lot of progress already