Anyone who has written a non-trivial GPU kernel has hit the same wall. You define a struct in C++ and pass it to a kernel. Six months later, somebody adds a member, forgets to update the matching layout on the device side, and a 200-line shader reads garbage. The compiler doesn’t help. The runtime doesn’t help. You find out from a customer.

The traditional answer is more macros. BOOST_PP_SEQ_FOR_EACH, X-macros, FOREACH_FIELD. They work, but they make the source unreadable and they cost nothing to misuse. The other answer is a code generator, which means a build-time dependency, a generated header nobody reads, and a debugging experience that involves three files at once.

C++26 ships P2996, static reflection, which was voted into the working draft at St. Louis in June 2024 and finalized at the Hagenberg meeting in 2025. For most of the C++ world this is an enabler for serialization, ORMs, and CLI parsers. For people writing GPU code, it is also one of the most practical things to happen to the toolchain in years. The reason is simple. GPU programming is about layouts, bindings, kernel variants, and metadata. All four of those are exactly what reflection is good at.

This post is a tour through six concrete things you can do once your compiler speaks reflection:

• validating that host and device struct layouts agree at compile time,
• generating SoA types from AoS definitions,
• packing push constants without hand-computed offsets,
• building kernel-variant dispatch tables from enum definitions,
• inspecting memory layout and detecting padding, and
• attaching binding/memory-space metadata directly to declarations via annotations.

Disclaimer: LLMs were used for proof-reading and grammar check.

C++20 gave us coroutines. The machinery needed was included: co_yield, co_return, co_await but the standard library had no concrete coroutine types. You had to write your own promise type, your own iterator, your own bookkeeping. The complexity of this boilerplate was intimidating for most people.

C++23 fixes the most obvious gap: std::generator<T>. It’s a synchronous, pull-based, lazy sequence. You write a function that co_yields values, and the caller iterates over them with a range-for. No allocator gymnastics, no hand-rolled promise types. See appendix for an example of creating your own generator.

Let’s see this feature in detail, with some examples and illustrate some caveats.

What std::generator actually is

An std::generator<T> is a view. It satisfies std::ranges::input_range. That means it plugs into the entire ranges pipeline. You can use it with useful functions like views::take, views::filter, views::transform and without ever materializing the full sequence in memory.

The function suspends at each co_yield and resumes when the caller asks for the next element. Values are produced one at a time, on demand. If the caller stops iterating, the remaining values are never computed.

This makes it trivial to express infinite sequences, stateful streams, or anything where computing the entire result set upfront is wasteful or impossible.

At Stream HPC, we enjoy opportunities to connect with the HPC and accelerator community, exchange ideas, and learn from engineers and researchers working across the GPU ecosystem. Later this month, several members of our team will be attending GPU Day 2026 in Budapest, Hungary.

Now in its 16th edition, GPU Day has become an established annual conference focused on massively parallel computing in science and industrial applications. Organized by the Wigner Scientific Computation Laboratory, the event brings together researchers, developers, students, and industry experts to discuss technologies spanning GPUs, compilers, machine learning, visualization, and emerging accelerator platforms.

For Stream HPC, events like GPU Day are a natural fit. We work with clients across a wide range of hardware platforms and software ecosystems, helping optimize and accelerate applications where performance matters. Conferences like this provide an opportunity to share practical experiences from real-world projects and contribute to the broader HPC community.

This year two Stream HPC engineers will present their work at the conference:

Parallel primitives are the ubiquitous building blocks of GPU programming with CUDA and HIP, to make your life as a programmer easier. Primitives like scans, reductions, and sorts operate in parallel over large data inputs. The basic use case has input and output residing in device memory as an array of values. However, the libraries provided by NVIDIA and AMD allow the use of iterators, which abstract the concept of input and output. An iterator is a type that behaves as a pointer, but overrides part of the dereferencing and arithmetic logic. With creative use of iterators, using the parallel primitives can become simpler and more performant. Assuming basic familiarity with the primitives, in this article we will show two examples of how iterators can be used to create better GPU programs: finding the arguments of the maximum of a function using a zip iterator in HIP and fast reduction with equally-sized segments in CUDA.

Via our Twitter channel we have various polls. Not always have we shared the full background of these polls, so we’ve taken the polls of the past half year and put them here. The first half of the year there were no polls, in case you wanted to know.

As inclusive polls are not focused (and thus difficult to answer), most polls are incomplete by design. Still insights can be given. Or comments given.

Below’s polls have given us insight and we hope they give you insights too how our industry is developing. It’s sorted on date from oldest first.

It was very interesting that the percentage of votes per choice did not change much after 30 votes. Even when it was retweeted by a large account, opinions had the same distribution.

Is HIP (a clone of CUDA) an option?

This series “Basic concepts” is based on GPGPU-questions we get via email more than once, or when the question is not clearly explained in the books. For one it is obvious, for the other just what they’re missing.

They say that learning a new technique is best done by playing around with working code and then try to combine it. The idea is that when you have Stackoverflowed and Githubed code together, you’ve created so many bugs by design that you’ll learn a lot if you make it work. When applying this to OpenCL, you quickly get to a situation that you want to run one.cl file and then another.cl file. Almost all beginner’s material discuss a single OpenCL-file, so how to do this elegantly?

Continue reading “OpenCL Basics: Running multiple kernels in OpenCL” →

After the last post on Excel (“Accelerating an Excel Sheet with OpenCL“), there have been various request and discussions how we do “the miracle”. Short story: we only apply proper engineering tactics. Below I’ll explain how you can also speed up Excel and when you actually have to call us (last step).

A computer can handle 10s of gigabytes per second. Now look how big your Excel-sheet is and how much time it takes. Now you understand that the problem is probably not your computer.

Excel is a special piece of software from a developer’s perspective. An important rule of software engineering is to keep functionality (code) and data separate. Excel mixes these two as no other, which actually goes pretty well in many cases unless the data gets too big or the computations too heavy. In that case you’ve reached Excel’s limits and need to properly solve it.

Below are the steps to go through, of which most you can do yourself!

The goal of this writing is to explain which applications are suitable to be ported to OpenCL and run on GPU (or multiple GPUs). It is done by showing the main differences between GPU and CPU, and by listing features and characteristics of problems and algorithms, which can make use of highly parallel architecture of GPU and simply run faster on graphic cards. Additionally, there is a list of issues that can decrease potential speed-up.

It does not try to be complete, but tries to focus on the most essential parts of assessing if code is a good candidate for porting to the GPU.

GPU vs CPU

The biggest difference between a GPU and a CPU is how they process tasks, due to different purposes. A CPU has a few (usually 4 or 8, but up to 32) ”fat” cores optimized for sequential serial processing like running an operating system, Microsoft Word, a web browser etc, while a GPU has a thousands of ”thin” cores designed to be very efficient when running hundreds of thousands of alike tasks simultaneously.

A CPU is very good at multi-tasking, whereas a GPU is very good at repetitive tasks. GPUs offer much more raw computational power compared to CPUs, but they would completely fail to run an operating system. Compare this to 4 motor cycles (CPU) of 1 truck (GPU) delivering goods – when the goods have to be delivered to customers throughout the city the motor cycles win, when all goods have to be delivered to a few supermarkets the truck wins.

Most problems need both processors to deliver the best value of system performance, price, and power. The GPU does the heavy lifting (truck brings goods to distribution centers) and the CPU does the flexible part of the job (motor cycles distributing doing deliveries).

Assessing software for GPU-porting fitness

Software that does not meet the performance requirement (time taken / time available), is always a potential candidate for being ported to a GPU. Continue reading “Selecting Applications Suitable for Porting to the GPU” →

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.

The only problem is that CUDA-libraries need to have their HIP-equivalent to be able to port all CUDA-software.

Here is where we come in. We helped AMD make a high-performance Pseudo Random Generator (PRNG) Library, called rocRAND. Random number generation is important in many fields, from finance (Monte Carlo simulations) to Cryptographics, and from procedural generation in games to providing white noise. For some applications it’s enough to have some data, but for large simulations the PRNG is the limiting factor. Continue reading “Learn about AMD’s PRNG library we developed: rocRAND – includes benchmarks” →