Written by Máté Ferenc Nagy-Egri and Gergely Mészáros

Disclaimer: if you’ve stumbled across this page in search of fixing up 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 about ROCm and CMake, join us for a ride.

Finally, ROCm on Windows

The recent release of the AMD’s ROCm SDK on Windows brings a long awaited rejuvenation of developer tooling for offload APIs. Undoubtedly it’s most anticipated feature is a HIP-capable compiler. The runtime component amdhip64.dll has been shipping with AMD Software: Adrenalin Edition for multiple years now, and with some trickery one could consume the HIP host-side API by taking the API headers from GitHub (or a Linux ROCm install) and creating an export lib from the driver DLL. Feeding device code compiled offline and given to HIP’s Module API  was attainable, yet cumbersome. Anticipation is driven by the single-source compilation model of HIP borrowed from CUDA. That is finally available* now!

[*]: That is, if you are using Visual Studio and MSBuild, or legacy HIP compilation atop CMake CXX language support.

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 love it when junior applicants have a personal project to show, even if it’s unfinished. As it can be scary to share such unfinished project, I’ll go first.

Introduction in 2023

Everybody who starts in GPGPU, has this moment that they feel great about the progress and speedup, but then suddenly get totally overwhelmed by the endless paths to more optimizations. And ofcourse 90% of the potential optimizations don’t work well – it takes many years of experience (and mentors in the team) to excel at it. This was also a main reason why I like GPGPU so much: it remains difficult for a long time, and it never bores. My personal project where I had this overwhelmed+underwhelmed feeling, was with N-Queens – till then I could solve the problems in front of me.

I worked on this backtracking problem as a personal fun-project in the early days of the company (2011?), and decided to blog about it in 2016. But before publishing I thought the story was not ready to be shared, as I changed the way I coded, learned so many more optimization techniques, and (like many programmers) thought the code needed a full rewrite. Meanwhile I had to focus much more on building the company, and also my colleagues got better at GPGPU-coding than me – this didn’t change in the years after, and I’m the dumbest coder in the room now.

Today I decided to just share what I wrote down in 2011 and 2016, and for now focus on fixing the text and links. As the code was written in Aparapi and not pure OpenCL, it would take some good effort to make it available – I decided not to do that, to prevent postponing it even further. Luckily somebody on this same planet had about the same approaches as I had (plus more), and actually finished the implementation – scroll down to the end, if you don’t care about approaches and just want the code.

Note that when I worked on the problem, I used an AMD Radeon GPU and OpenCL. Tools from AMD were hardly there, so you might find a remark that did not age well.

Introduction in 2016

What do 1, 0, 0, 2, 10, 4, 40, 92, 352, 724, 2680, 14200, 73712, 365596, 2279184, 14772512, 95815104, 666090624, 4968057848, 39029188884, 314666222712, 2691008701644, 24233937684440, 227514171973736, 2207893435808352 and 22317699616364044 have to do with each other? They are the first 26 solutions of the N-Queens problem. Even if you are not interested in GPGPU (OpenCL, CUDA), this article should give you some background of this interesting puzzle.

An existing N-Queen implementation in OpenCL took N=17 took 2.89 seconds on my GPU, while Nvidia-hardware took half. I knew it did not use the full potential of the used GPU, because bitcoin-mining dropped to 55% and not to 0%. 🙂 I only had to find those optimizations be redoing the port from another angle.

This article was written while I programmed (as a journal), so you see which questions I asked myself to get to the solution. I hope this also gives some insight on how I work and the hard part of the job is that most of the energy goes into resultless preparations.

AFDS was full of talks on OpenCL. You missed them, just like me? Then you will be happy that they put many videos on Youtube!

Enjoy watching! As all videos are around 40 minutes, it is best to take a full day for watching them all. The first part is on openCL itself, second is on tools, third on OpenCL usages, fourth on other subjects.

Continue reading “OpenCL Videos of AMD’s AFDS 2012” →

In January 2010 I created the first steps of StreamComputing (redacted: rebranded to StreamHPC in 2017), by registering the website and writing a hello-world article. About 4 months of preparations and paperwork later the freelance-company was registered. Then 5 years later it got turned into a small company with still the strong focus on OpenCL, but with more employees and more customers.

I would like to thank the following people:

• My parents and grand-mother for (financially) supporting me, even though they did not always understand why I was taking all those risks.
• My friends, for understanding I needed to work in the weekends and evenings.
• My good friend Laura for supporting me during the hard times of 2011 and 2012.
• My girlfriend Elena for always being there for me.
• My colleagues and OpenCL-experts Anca, Teemu and Oscar, who have done the real work the past year.
• My customers for believing in OpenCL and trusting StreamComputing.

Without them, the company would never even existed. Thank you! Continue reading “StreamComputing exists 5 years!” →

Wanting to test your OpenCL-software on specific hardware? From now on, that is possible by logging in on StreamHPCs test-servers.

Update: new prices. Got feedback it should compete with Amazon EC2.

During the beta-period (February to April) we have available:

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• Dual FirePro S10000 (total ~11.8 TFLOPS single precision, ~2.9 TFLOPS double precision).
• Several embedded GPU-boards attached.

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By request more servers will be added, but for now we start lean.

You’ll get:

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• 64 bit Linux server.
• A secure SSH access with chrooted harddisk space.
• Full NDA in case StreamHPC staff needs to assist.
• Both shared and dedicated usage possible.
• Assistance via mail and skype.

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The costs for shared hours are €1,- per hour, for private hours €10,- €5,- per hour. Discounts are available for academics and existing customers (also from past trainings) – just contact us. Goal of the shared hours is to setup the software, do basic tests, but not to do intensive benchmarks.

We hope you can now make a better decision what hardware to choose, or to have your paper’s conclusions based on more recent hardware.

Want more info or have special requests? Call +31854865760 (office) or +31645400456 (cell), or use the contact form.

For your convenience: an overview of all ARM-GPUs and their driver-availability. Please let me know if something is missing.

I’ve added a rating, to friendly push the vendors to get to at least an 7. Vendors can contact me, if they think the rating does not reflect reality.

ZiiLabs

SDK-page@StreamHPC

Drivers can be delivered by Creative, when you pledge to order ZMS-40 processors. Mail us for a contact at Creative. Minimum order size is unknown.

This device can therefore only be used for custom devices.

[usr=4]

Vivante

SDK-page@StreamHPC

They are found on public devices. Android-drivers that work on FreeScale processors are openly available and can be found here.

[usr=8]

Even though the processors are not that powerfull, Vivante/FreeScale offers the best support.

Qualcomm

SDK-page@StreamHPC

Drivers are not shipped on devices, according various sources. Android-drivers are in the SDK-drivers though, which can be found here.

[usr=7]

Rating will go up, when drivers are publicly shipped on phones/tablets.

ARM MALI

Samsung SDK-page@StreamHPC

There are lots of problems around the drivers for Exynos, which only seem to work on the Arndale-board when the LCD is also ordered.Android-drivers can be downloaded here.

[usr=5]

All is in execution – half-baked drivers don’t do it. It is unclear whom to blame, but it certainly has had influence on creating a new version of Exynos 5, the octa.

Imagination Technologies

SDK-page@StreamHPC

TI only delivers drivers under NDA. Samsung has one board coming up with OpenCL 1.1 EP drivers.

[usr=5]

Rating will go up, when drivers from TI come available without obstacles, or Samsung delivers what they failed to do with the previous Exynos 5.

Exciting times coming up

Mostly because of a power-struggle between Google and the GPU-vendors, there is some hesitation to ship OpenCL drivers on phones and tablets. Unfortunately, Google’s answer to OpenCL RenderScript Compute, does not provide the needs wanted by developers. Google’s official answer is that it does not want fragmentation nor code that is optimised for a certain GPU. The interpreted answer is that Google wants vendor-lockin and therefore blocks the standard. Whatever the reason is, OpenCL is used as sword to show teeth who has a say about the future of Android – only the advertisement-company Google or also the group of named processor-makers and various phone/tablet-vendors?

In H2 2014 Nvidia will ship CUDA-drivers with their Tegra 5 GPUs, making the soap complete.

There are rumours Apple will intervene and will make OpenCL available on iOS. This would explain why there is put so much effort in showing OpenCL-results by Imagination and Qualcomm

And always keep a close watch on POCL, the vendor-independent OpenCL implementation.

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Need a programmer for any of the above devices? Hire us!

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