Stream HPC is 10 years old on 1 April 2020. Therefore we offer our one day GPGPU crash course for free that whole month.

Now Corona (and fear for it) spreads, we had to rethink how to celebrate 10 years. So while there were different plans, we simply had to adapt to the market and world dynamics.

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 improve ourselves, it would be good to share a bit more so both new customers and new recruits know what to expect form the team.

Black boxes will never be transparent

Assumption is the mother of all mistakes

Eugene Lewis Fordsworthe

A colleague put “Assumptions is the mother of all fuckups” on the wall, because we should be assuming we assume. Problem is that we want to have full control and make faster decisions, and then assuming fits in all these scary unknowns.

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 finance. It contains codes for Black-Scholes, Monte-Carlo, Bonds, and Repo financial applications which can be run on the CPU and GPU.

The problem is that it has not been maintained for 5 years and there were good improvement opportunities. Even though the paper was already written, we think it can still be of good use within computational finance. As we were seeking a way to make a demo for the financial industry that is not behind an NDA, this looked like the perfect starting point for that. We have emailed all the authors of the library, but unfortunately did not get any reply. As the code is provided under an permissive license, we could luckily go forward.

The first version of the code will be released on Github early next month. Below we discuss some design choices and preliminary results.

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, but we’ll add more dates in this blog-post later on.

If you need to learn solid GPU programming, this is the training you should attend. The concepts can be applied to other GPU-languages too, which makes it a good investment for any probable future where GPUs exist.

This is a public training, which means there are attendees from various companies. If you prefer not to be in a public class, get in contact to learn more about our in-company trainings.

It includes:

• Four days of training online
• Free code-review after the training, to get feedback on what you created with the new knowledge;
• 1 month of limited support, so you can avoid StackOverflow;
• Certificate.

Trainings will be done by employees of Stream HPC, who all have a lot of experience with applying the techniques you are going to learn.

Schedule

Most trainings have around 40% lectures, 50% lab-sessions and 10% discussions.

Soon there will be another Dutch eScience Symposium 2019 in Amsterdam. We thought it might be a good place to meet and listen to e-science talks. Stream HPC in the end is just making scientific software, so we’re here at the right place. The eScience Center is a government institute that aims to advance eScience in the Netherlands.

Interested? Read on!

Last year we accelerated the OpenCL-code in PaSWAS, which is open source software to do DNA/RNA/protein sequence alignment and trimming. It has users world-wide in universities, research groups and industry.

Below you’ll find the benchmark results of our acceleration work. You can also test out yourself, as the code is public. In the readme-file you can learn more about the idea of the software. Lots of background information is described in these two papers:

• Flexible, Fast and Accurate Sequence Alignment Profiling on GPGPU with PaSWAS
• pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment

We chose PaSWAS because we really like bio-informatics and computational chemistry – the science is interesting, the problems are complex and the potential GPU-speedup is real. Other examples of such software we worked on are GROMACS and TeraChem.

In January 2019 I gave a talk about culture in the company, which I wanted to share with you. It was intended to trigger discussions on what environment fits somebody, and examples were given on other companies. The nice part was that it became more clear that the culture of a company like CodePlay was very alike, except they are working on different things (compilers). Same for departments of larger companies we work with or know well.

Important: all answered are based on what my colleagues answered. So most of us are cat-people, but I wouldn’t say that defines a GPU-developer. I hope it still gives you an understanding of our perspective on what defines a GPU-dev in just a few minutes, while it also gives you more than enough matter to think about.

This year we’ll be with 4 people at ISC: Vincent, Adel, Anna and Istvan. You can find us at booth G-812, next to Red Hat.

While we got known in the HPC-world for our expertise on OpenCL, we now have many years of experience in CUDA and OpenMP. To get there, we’ve focused a lot on how to improve code quality of existing software, to reduce bugs and increase speedup-potential. Our main expertise remains full control over algorithms in software – the same data simply processed faster.

Why do we have a booth?

We’ll be mostly talking to (new) customers for development of high performance software for the big machines. Also we’ll have a list of our open job positions with us, and we can do the first introductory interview on the spot.

Our slogan for this year is:

We’ll be sharing our week on Twitter, so you can also see what we find: posters about HPC-programming on CPU and GPU, booths that have nice demos or interesting talks and ofcourse the surprises.

Let’s meet!

If you don’t have an appointment yet, but would like to chat with us, please contact us or drop by at our booth. As we’re with four people, we have high flexibility.

On Monday May 13, 2019 at 09:30 the latest edition of IWOCL starts, not taking into account any pre-events that might be spontaneously organized. This is the biggest OpenCL-focused event that discusses everything that would make any GPGPU-programmer, DSP-programmer and FPGA-programmer enthusiastic.

What’s new since last year, is that it’s actually also more interesting place for CUDA-developers who like to learn and discuss new GPU-programming techniques. This is because Nvidia’s GTC has moved more to AI, where it used to be mostly GPGPU for years.

Since it’s now the last week of the early-bird pricing, it’s a good time to make you think about buying your ticket and book the trip.

We explicitly don't work for the defence/war industry. We therefore recently said no to a training project because of our values. We hope our competitors do the same to slow down wars. pic.twitter.com/xQIuW002QO

— StreamHPC (@StreamHPC) October 24, 2018

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.

Problem is that programming GPUs is not an easy task. Where do you learn to program GPUs? We found these to be the main groups:

• Universities
• Research centers
• GPU vendors (AMD, Nvidia, Intel, Qualcomm, ARM)
• Self-study

This is far from enough. Add to that, that only a very select group learns the craft at a company. We’d like to change that, and we think now is the time for us to be able to deliver on this.

In January we’ll our internal training program will start with 4 to 8 developers. Focus in on fully understanding recent GPU-architectures, CUDA and OpenCL. It will consist of lectures, workshops, discussions, paper reading and ofcourse coding for one month. The months after that will have guidance, paper presentations, code reviews and time for self-study. The exact form will differ per person.

The hard side

The current measurable requirements are:

• EU citizen or already having a working permit
• Great at C/C++
• High interest in algorithmic optimisations
• Any performance improvement focus (i.e. Assembly, clean code) is a plus
• Any GPU experience (i.e. OpenGL, DirectX, self-study) is a plus
• High interest in performance
• Willing to move to Amsterdam
• Willing to work for Stream HPC for at least 2 years

The soft side

We’re looking for people that fit our culture and we think we can train. This means that the selection is based for a large part on “the spark”. Therefore the application starts with a speed date, and we’re sorry for not finding a better wording for this. This is a 20 minute discussion about what we like and what we don’t. This can be done via phone, Skype or in person, during the evening, in the weekends or during your lunch break.

How to apply

Read about our company culture. Look at the jobs we have open. These describe the requirements after the training. Then write us a motivational letter: explain us why this is exactly what you want, why you’re capable and why you’re a cultural fit. If you find it hard to write such letter, then just start with answering the list of requirements. It’s a big bonus to share code (Github, Gitlab, zip-file). Send your email to jobs@streamhpc.com

Other jobs

Feeling more senior? We have other jobs: