All technical blog posts written by people of the Stream HPC team.
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
Lazy Ranges in C++23 with Std::generator Read More »
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
Our Two Presentations at GPU Day 2026 Read More »
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
Thinking with iterators in CUDA and HIP Read More »
Why you should just delve into porting difficult puzzles using the GPU, to learn GPGPU-languages like CUDA, HIP, SYCL, Metal or OpenCL. And if you did not pick one, why not N-Queens? N-Queens is a truly fun puzzle to work on, and I am looking forward to learning about better approaches via the comments. We
N-Queens project from over 10 years ago Read More »
Written by Máté Ferenc Nagy-Egri and Gergely Mészáros Disclaimer: if you’ve stumbled across this page in search of a fix for the ROCm SDK’s CMake HIP language support on Windows and care only about the fix, please skip to the end of this post to download the patches. If you wish to learn some things
How to get full CMake support for AMD HIP SDK on Windows – including patches Read More »
Speeding up finance algorithms for GPUs
Improving FinanceBench for GPUs Part II – low hanging fruit Read More »
How fast is your software? The simpler the software setup, the easier to answer this question. The more complex the software, the more the answer will “it depends”. But just peek at F1-racing – the answer will depend on the driver and the track. This article focuses on the foundations of solid benchmarking, so it
The Art of Benchmarking Read More »
We are a problem solving company first, specialised in HPC – building software close to the processor. The more projects we finish, the more it’s clear that without our problem solving skills, we could not tackle the complexity of a GPU and CPU-clusters. While I normally shield off how we do and how we continuously
Problem solving tactic: making black boxes smaller Read More »
If you’re into computational finance, you might have heard of FinanceBench. It’s a benchmark developed at the University of Deleware and is aimed at those who work with financial code to see how certain code paths can be targeted for accelerators. It utilizes the original QuantLib software framework and samples to port four existing applications for quantitative
Improving FinanceBench Read More »
Update: due to Corona, the Amsterdam training has been cancelled. We’ll offer the training online on dates that better suit the participants. As it has been very busy here, we have not done public trainings for a long time. This year we’re going to train future GPU-developers again – online. For now it’s one date,
Updated: OpenCL and CUDA programming training – now online Read More »