Answer: Yes, actually a lot!
The company was built on OpenCL and we are still work with the language a lot – from embedded GPUs and FPGAs to high-end GPUs. Like OpenCL unjustly isn’t associated with clusters full of professional GPUs, we were not associated with CUDA. I can tell many of our customers have found us to build high performance software in CUDA.
Breaking with the past is not easy due to associations that seem to stick. With the name change from StreamComputing to Stream HPC some years ago, we wanted to enforce that break with being “the OpenCL company”. For some time we were much more pragmatic in solving the problems of our customers, which resulted in making software in MPI and CUDA – sometimes an unexpected direction as the customer initially chose OpenCL.
We also started hiring people who only knew CUDA (but expect them to learn OpenCL), as the right algorithm and the right processor is more important. Internships with CUDA, large CUDA-projects, seeking better relations with Nvidia and such – all have been going on for years. And we like it as much as we like OpenCL – both have unique advantages.
So if you have questions about CUDA, don’t be afraid that you hurt us – we’re happy to help you get fast software.
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GPUs have been our mysterious friends and known enemies for years, as they let us run code in expected and unexpected ways. GPUs have solved problems for many of our customers. GPUs have such a high rate of evolvement, that they’ll remain important for the years to come.
Our managing director, 
As of this month Stream exists 8 years. 8 full years of helping our customers with fast software.In Chinese numerology 8 is a very lucky number, and we notice that.
Ever saw a claim on a paper you disagreed with or got triggered by, and then wanted to reproduce the experiment? Good luck finding the code and the data used in the experiments.
When CUDA kept having a dominance over OpenCL, AMD introduced HIP – a programming language that closely resembles CUDA. Now it doesn’t take months to port code to AMD hardware, but more and more CUDA-software converts to HIP without problems. The real large and complex code-bases only take a few weeks max, where we found that solved problems also made the CUDA-code run faster.
It takes quite some effort to program FPGAs using VHDL or Verilog. Since several years Intel/Altera has OpenCL-drivers, with the goal to reduce this effort. OpenCL-on-FPGAs reduced the required effort to a quarter of the time, while also making it easier to alter the specifications during the project. Exactly the latter was very beneficiary when creating the demo, as the to-be-solved problem was vaguely defined. The goal was to make a video look like a cartoon using image filters. We soon found out that “cartoonized” is a vague description, and it took several iterations to get the right balance between blur, color-reduction and edge-detection. 
A month ago IWOCL (OpenCL workshop) and DHPCC++ (C++ for GPUs) took place. Meanwhile many slides and posters have been 
Most of our projects are around performance optimisation, but we’re cleaning up bugs too. This is because you can only speed up software when certain types of bugs are cleared out. A few months ago, we got a different type of request. If we could solve bugs in MESA 3D that appear in games.