General articles on technical subjects.

Scientific Visualisation of Molecules

Posted by Vincent Hindriksen on with 2 Comments

In many hard sciences focus is on formulas and text, whereas images are mainly graphs or simplified representations of researched matters. Beautiful visualisations are mainly artist’s impressions in popular media targeting hobby-scientists. When Cyrille Favreau made the first good-working version of his real-time GPU-accelerated raytracer, he saw potential in exactly this area: beautiful, realistic visualisations to be used in serious science. This resulted in software called IPV.

He chose to focus on rendering molecules of proteins and this article discusses raytracing in molecular sciences, while highlighting the features of the software.

This project has been discussed on GPU Science, but this article looks at the the software from a slightly different perspective. If you don’t want to know how the software works and what it can do, scroll down for a download-link.

Raytracing introduction

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Targetting various architectures in OpenCL and CUDA

Posted by Vincent Hindriksen on

“Everything that *is* makes up one single world; but not everything is alike in this world” – Plato

The question we aim to answer in this post is: “How to do you make software that performs on several platforms?”.

Note: This article is not fully finished – I’ll add more information during the coming months. It’s busy here!

Even in many Java-code you’ll find hard-coded filename-delimiters in the file-names, which then work on one OS only. Portability is a problem that exists in various aspects of programming. Let’s look at some of the main goals software can have, and which portability-problems they have.

  • Functionality. This is the minimum requirement. Once a function is decided, changing functionality takes a lot of time. Writing code that is very flexible in requirements is hard.
  • User-interface. This is what one sees and which is not too abstract to talk about. For example, porting software to a touch-device requires a lot of rethinking of interaction-principles.
  • API and library usage. To lower development-time, existing and known APIs and libraries are used. This can work out three ways: separation of concerns, less development-time and dependency. The first two being good architectural choices, the latter being a potential hazard. Changing the underlying APIs is not easy.
  • Data-types. Handling video is different from handling video-formats. If the files can be handles in the intermediate form used by the software, then adding new file-types is relatively easy.
  • OS and platform. Besides many visible specifics, an OS is also a collection of APIs. Not only corporate operating systems tend to think of their own platform only, but also competing standards. It compares a lot to what is described under APIs.
  • Hardware-performance. Optimizing software for a specific platform makes it harder to port to other platforms. This will the main point of this article.

OpenCL is known for not being performance-portable, but it is the best we currently have when it comes to writing code with performance as a primary target. The funny thing is that with CUDA 5.0 it has become clearer that NVIDIA has the problem in their GPGPU-language too, whereas it was used before to differentiate CUDA from OpenCL. Also, CUDA 5.0 has many new features only available on the latest Kepler-GPUs.

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OpenCL Videos of AMD’s AFDS 2012

Posted by Vincent Hindriksen on with 4 Comments

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.

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OpenCL on Altera FPGAs

Posted by Vincent Hindriksen on

On 15 November 2011 Altera announced support for OpenCL. The time between announcements for having/getting OpenCL-support and getting to see actually working SDKs takes always longer than expected, so to get this working on FPGAs I did not expect anything before 2013. Good news: the drivers are actually working (if you can trust the demos at presentations).

There have been three presentations lately:

In this article I share with you what you should not have missed on these sheets, and add some personal notes to it.

Is OpenCL the key that finally makes FPGAs not tomorrow’s but today’s technology?

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4 October talk in Amsterdam on mobile compute

Posted by Vincent Hindriksen on with 2 Comments

Thursday 4 October I talk on mobile compute at Hackers&Founders Amsterdam on what mobile compute can do. The goal is to initiate new ideas for start-ups, as not many know their mobile phone and tablet is very powerful and next year can be used for compute intensive tasks.

The other talk is from Mozilla on Firefox OS (Edit: it was cancelled), which is actually reason enough to visit this Hackers&Founders Meetup. Entrance is free, drinks are not. Alternatively you could go to the Hadoop User Group Meetup at Science Park, Amsterdam.

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Avoiding false dependencies in only two steps

Posted by Vincent Hindriksen on

Let’s approach the concept of programming through looking at the brain, the code and the computer.

The idea of a program lives in the brain of a programmer. The way to get the program to the computer is using a system process called coding. When the program coded on the computer and the program embedded as an idea in the brain are alike, the programmer is happy. When over time the difference between the brain-version and the computer-version grows, then we go for a maintenance phase (although this is still this mostly from brain to computer).

When the coding-language or important coding-paradigms change, something completely different happens. In such case the program in the brain is updated or altered. Humans are not good at that, or at least not many textbooks discuss how to change from one model to another.

In this article I want to discuss one of these new coding-paradigm: dependencies in parallel software.
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Do you have GPU-brains? A poster-initiative.

Posted by Vincent Hindriksen on with 3 Comments

This is a message to GPU-programmers only.

It is a simple question, and has many answers: what are GPU-brains? How is it possible your brain can code GPUs and only few friends and colleagues understand what you are doing? Is it thinking in parallel, focusing on one kernel and having the architecture in the back of the head. Is it simple loop-unrolling? Is it a web of thoughts? Is it just cool, as not many people can do it?

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AMD’s answer to NVIDIA TESLA K10: the FirePro S9000

Posted by Vincent Hindriksen on with 8 Comments

Recently AMD announced their new FirePro GPUs to be used in servers: the S9000 (shown at the right) and the S7000. They use passive cooling, as server-racks are actively cooled already. AMD partners for servers will have products ready Q1 2013 or even before. SuperMicro, Dell and HP will probably be one of the first.

What does this mean? We finally get a very good alternative to TESLA: servers with probably 2 (1U) or 4+ (3U) FirePro GPUs giving 6.46 to up to 12.92 TFLOPS or more theoretical extra performance on top of the available CPU. At StreamHPC we are happy with that, as AMD is a strong OpenCL-supporter and FirePro GPUs give much more performance than TESLAs. It also outperforms the unreleased Intel Xeon Phi in single precision and is close in double precision.

Edit: About the multi-GPU configuration

A multi-GPU card has various advantages as it uses less power and space, but does not compare to a single GPU. As the communication goes via the PCI-bus still, the compute-capabilities between two GPU cards and a multi-GPU card is not that different. Compute-problems are most times memory-bound and that is an important factor that GPUs outperform CPUs, as they have a very high memory bandwidth. Therefore I put a lot of weight on memory and cache available per GPU and core.

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NVIDIA ended their support for OpenCL in 2012

Posted by Vincent Hindriksen on with 19 Comments

If you are looking for the samples in one zip-file, scroll down. The removed OpenCL-PDFs are also available for download.

This sentence “NVIDIA’s Industry-Leading Support For OpenCL” was proudly used on NVIDIA’s OpenCL page last year. It seems that NVIDIA saw a great future for OpenCL on their GPUs. But when CUDA began borrowing the idea of using LLVM for compiling kernels, NVIDIA’s support for OpenCL slowly started to fade instead. Since with LLVM CUDA-kernels can be loaded in OpenCL and vice versa, this could have brought the two techniques more together.

What is the cause for this decreased support for OpenCL? Did they suddenly got aware LLVM would decrease any advantage of CUDA over OpenCL and therefore decreased support for OpenCL? Or did they decide so long ago, as their last OpenCL-conformant product on Windows is from July 2010? We cannot be sure, but we do know NVIDIA does not have an official statement on the matter.

The latest action demonstrating NVIDIA’s reduced support of OpenCL is the absence of the samples in their GPGPU-SDK. NVIDIA removed them without notice or clear statement on their position on OpenCL. Therefore we decided to start a petition to get these OpenCL samples back. The only official statement on the removal of the samples was on LinkedIn:

All of our OpenCL code samples are available at http://developer.nvidia.com/opencl, and the latest versions all work on the new Kepler GPUs.
They are released as a separate download because developers using OpenCL don’t need the rest of the CUDA Toolkit, which is getting to be quite large.
Sorry if this caused any alarm, we’re just trying to make life a little easier for OpenCL developers.

Best regards,

Will.

William Ramey
Sr. Product Manager, GPU Computing
NVIDIA Corporation

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Processors that can do 20+ GFLOPS per Watt (2012)

Posted by Vincent Hindriksen on with 44 Comments

System for communicating power-efficiency of new equipment. “A” being best, “F” being worst. 2011-A is incomparable with 2012-A.

For yearly power-usage there is a rule-of-thumb which states that a device that is continuously on, costs the amount of Watt times 1.5 in Euro per year. So the computer in front of me, that takes around 107 Watt, costs me €160 a year if I would leave it on. A moderate cluster with several GPUs of a few hundred Watts each, would cost a few thousand Euros a year. I would say: very doable for most companies.

So why is the performance per Watt? There is more to a Watt than just the costs. The energy to cool a cluster is quite high, as most of the energy escapes via heat. And then there is the increase in demand for portable power. In cases you are thinking of sweeping you credit card for a top 10 supercomputer, then these energy-costs are extremely high.

In this article I try to get an overview of who is entering the 20+ GFLOPS/Watt area. All processors that do less than 20 GFLOPS/Watt, need to have other advantages to survive. And you’ll see that all the green processors are programmed with OpenCL, the technology StreamHPC is all about.

IMPORTANT: The total power used is sometimes including and sometimes excluding memory-transfers. So the comparison below IS NOT FAIR. The graphics cards are including memory-transfers, while the CPUs and SoCs are not.

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When Big Data needs OpenCL

Posted by Vincent Hindriksen on

Big Data in the previous century was the archive full of ring-binders/folders/ordners, which would grow each year at the same pace. Now the definition is that it should grow each year as much as all years before combined.

A few months ago SunGard named 10 Big Data trends transforming financial services. I have used their list as a base to have my own focus: on increased computation-demands and not specific for this one market. This resulted in 7 general trends where Big Data meets/needs OpenCL.

Since the start of StreamHPC we sought customers who could no compute through their whole data in time. Back then Big Data was still a buzz word catching on, but it best describes this one core businesses.

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The CPU is dead. Long live the CPU!

Posted by Vincent Hindriksen on with 2 Comments

Scene from Gladiator when is decided on the end of somebody’s life.

Look at the computers and laptops sold at your local computer shop. There are just few systems with a separate GPU, neither as PCI-device nor integrated on the motherboard. The graphics are handled by the CPU now. The Central Processing Unit as we knew it is dying.

To be clear I will refer to an old CPU as “GPU-less CPU”, and name the new CPU (with GPU included) as plain “CPU” or “hybrid Processor”. There are many names for the new CPU with all their own history, which I will discuss in this article.

The focus is on X86. The follow-up article is on whether the king X86 will be replaced by king ARM.

Know that all is based on my own observations; please comment if you have nice information.

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Intel OpenCL CPU-drivers 2013 beta with OpenCL 1.2 support

Posted by Vincent Hindriksen on with 7 Comments

Screenshot from Intel’s “God Rays” demo

This article is still work-in-progress

Intel has just released its OpenCL bit CPU-drivers, version 2013 bèta. It has support for OpenCL 1.1 (not 1.2 as for the CPU) on Intel HD Graphics 4000/2500 of the 3rd generation Core processors (Windows only). The release notes mention support for Windows 7 and 8, but the download-site only mentions windows 8. Support under Linux is limited to 64 bits.

The release notes mention:

  • General performance improvements for many OpenCL* kernels running on CPU.
  • Preview Tool: Kernel Builder (Windows)
  • Preview Feature: support of  kernel source code hotspots analysis with the Intel VTuneT Amplifier XE 2011 update 3 or higher.
  • The GNU Project Debugger (GDB) debugging support on Linux operating systems.
  • New OpenCL 1.2 extensions supported by the CPU device:
    • cl_khr_int64_base_atomics and cl_khr_int64_extended_atomics
    • cl_khr_fp16
    • cl_khr_gl_sharing
    • cl_khr_gl_event
    • cl_khr_d3d10_sharing
    • cl_khr_dx9_media_sharing
    • cl_khr_d3d11_sharing.
  • OpenCL 1.1 extensions that were changed in OpenCL 1.2:
    • Device Fission supports both OpenCL 1.1 EXT API’s and also OpenCL* 1.2 fission core features
    • Media Sharing support intel 1.1 media sharing extension and also the 1.2 KHR media sharing extension
    • Printf extension is aligned with OpenCL 1.2 core feature.

Check the release notes for full information.

The drivers can be found on http://software.intel.com/en-us/articles/vcsource-tools-opencl-sdk-2013/. Installation is simple. For Windows there is a installer. If you have Linux, make sure you remove any previous version of Intel’s openCL drivers. If you have a Debian-based Linux, use the command ‘alien’ to convert the rpm to deb, and make sure ‘libnuma1‘ is installed. There are requirements for libc 2.11 or 2.12 – more information on that later as Ubuntu 12.04 has libc6 2.15.

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Taking on OpenCL

Posted by Vincent Hindriksen on

Quote by Dr. Kelso (from the series “Scrubs”) – click for video

OpenCL is getting more and more important and for more developers a skill worth having. At StreamHPC we saw this coming in 2010 and have been training people in OpenCL since. A few weeks ago I got a question on how to take on OpenCL, which could be interesting for more people: how to take on OpenCL. In other words: the steps to take to learn OpenCL the quickest. Since the last time I wrote on learning OpenCL is almost two years ago, it is a good time to share more recent insights on this matter.

Taking on OpenCL takes four main steps in this order:
  1. Understanding the hardware and architectures.
  2. Thinking both in parallel and in vectors.
  3. Learning the OpenCL language itself.
  4. Profiling and debugging.

You see that is a whole difference from learning for instance Java with a Pascal-background. Learning VHDL for programming FPGAs comes closer, though you don’t need to tinker with timings when doing OpenCL. Let’s go through the steps.

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How expensive is an operation on a CPU?

Posted by Vincent Hindriksen on with 13 Comments

Programmers know the value of everything and the costs of nothing. I saw this quote a while back and loved it immediately. The quote by Alan Perlis is originally about Perl LISP-programmers, but only highly trained HPC-programmers seem to have obtained this basic knowledge well. In an interview with Andrew Richards of Codeplay I heard it from another perspective: software languages were not developed in a time that cache was 100 times faster than memory. He claimed that it should be exposed to the programmer what is expensive and what isn’t. I agreed again and hence this post.

I think it is very clear that programming languages (and/or IDEs) need to be redesigned to overcome the hardware-changes of the past 5 years. I talked about that in the article “Separation of compute, control and transfer” and “Lots of loops“. But it does not seem to be enough.

So what are the costs of each operation (on CPUs)?

This article is just to help you on your way, and most of all: to make you aware. Note it is incomplete and probably not valid for all kinds of CPUs.

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GPGPU-day materials – teaser

Posted by Vincent Hindriksen on

Just a quick teaser. More materials (photos, sheets, videos) are coming soon.

Don’t forget to subscribe to the mailing-list of Platform Parallel Netherlands to hear about more events around parallel programming in the Netherlands.

Click on the icon at bottom-right to watch the video full-screen.

If you have made photos during the day, please send them.

Music by Professor Kliq.

Below is the short version with photos only

StreamComputing is 2 years old! A personal story.

Posted by Vincent Hindriksen on

More than two years ago, on 13 January 2010, I wrote my first blog-post. Four months later StreamComputing (redacted: rebranded to StreamHPC in 2017) was both official and unknown. I want to share with you my personal story on how I got to start-up this company.

The push-factor

I wanted to create a company which was about innovative projects –  something I had hardly encountered until then. The years before I programmed parts of A-to-B-flows, as I call them. That is software that is in the base quite simple, but tediously discussed as very, very complex.

“Complex” software

The complexity is not the software, as you can see. It is undocumented APIs, forgotten knowledge, knowledge in heads of unknown people, bossy and demanding people who friendly ask for last-minute architecture changes, deadlines around promotion-rounds, new deadlines due to board-decisions, people being afraid of getting replaced if the software is finished, jealousy if another team makes version 2 of the software, etc. The rule of office-software is therefore understandable:

Software is either unfinished,
or turned into a platform for unintended functionality.

The fun in office-software is there for analyst, architect or manager – the developer just puts in his earphones and makes all the requested changes (hooray for services like Spotify). But as I did not want to become a manager and wished to keep improving my development skills, I had to conclude I was on the wrong track.

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AMD gDEBugger 6.2 for Linux

Posted by Vincent Hindriksen on with 1 Comment

The printf-funtion in kernels isn’t the solution to everything, so hence profilers and debuggers specially tailored for GPU-programming. On Windows there is a lot of choice, but mostly only if you have a paid version of Visual Studio. On Linux you have GDB, but that program is not really user-friendly for the GUI-lovers.

For AMD there is now gDEBugger again available for Linux. Again, as version 5.8 by Gremedy worked with Linux, after AMD bought the company it got Windows-only for version 6. A few weeks ago, 10 months after 6.0, Linux-binaries got back with version 6.2. It supports OpenCL 1.2, OpenGL 3.2 and quite some extensions. As only AMD is supported, later more on debugging OpenCL-applications on NVidia and Intel.

Installation is quite straightforward. For creating a menu-item, you’ll find an useful image in /opt/gDEBugger6.2.xxx/tutorial/images/.

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NVIDIA: mobile phones, tablets and HPC (cloud)

Posted by Vincent Hindriksen on
If you want to see what is coming up in the market of consumer-technology (PC, mobile and tablet), then NVIDIA can tell you the most. The company is very flexible, and shows time after time it really knows in which markets is currently operates and can enter. I sometimes strongly disagree with their marketing, but watch them closely as they are in the most important markets to define the near future in: PCs, Mobile/Tablet and HPC.
You might think I completely miss interconnects (buses between processors, devices and memory) and memory-technologies as clouds have a large need for high-speed data-transport, but the last 20 years have shown that this is a quite stable developing market based on IP-selling to the hardware-vendors. With the acquisition of Cray’s interconnect technology, we have seen this is serious business for Intel, so things might change indeed. For this article I want to focus on NVIDIA’s choices.

Neil Trevett on OpenCL

Posted by Vincent Hindriksen on with 4 Comments

The Khronos Group gave some talks on their technologies in Shanghai China on the 17th of March 2012. Neil Trevett did some interesting remarks on the position of NVidia on OpenCL I would like to share with you. Neil Trevett is both an important member of Khronos and employee of NVidia. To be more precise, he is the Vice President Mobile Content of NVidia and the president of Khronos. I think we can take his comments serious, but we must be very careful as these are mixed with his personal opinions.

Regular readers of the blog have seen I am not enthusiastic at all about NVidia’s marketing, but am a big fan of their hardware. And exactly I am very positive they are bold enough in the industry to position themselves very well with the fast-changing markets of the upcoming years. Having said that, let’s go to the quotes.

All quotes were from this video. Best you can do is to start at 41:50 till 45:35.

At 44:05 he states: “In the mobile I think space CUDA is unlikely to be widely adopted“, and explains: “A party API in the mobile industry doesn’t really meet market needs“. Then continues with his vision on OpenCL: “I think OpenCL in the mobile is going to be fundamental to bring parallel computation to mobile devices” and then “and into the web through WebCL“.

Also interesting at 44:55: “In the end NVidia doesn’t really mind which API is used, CUDA or OpenCL. As long as you are get to use great GPUs“. He ends with a smile, as “great GPUs” refers to NVidia’s of course. 🙂

At 45:10 he puts NVidia’s plans on HPC, before getting back to : “NVidia is going to support both [CUDA and OpenCL] in HPC. In Mobile it’s going to be all OpenCL“.

At 45:23 he repeats his statements: “In the mobile space I expect OpenCL to be the primary tool“.

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USB-stick sized ARM-computers

Posted by Vincent Hindriksen on

Now that smartphones get more powerful and internet makes it possible to have all functionality and documents with you anywhere, the computer needs to be reinvented. You see all big IT-companies searching for how that can be, from Windows Metro to complete docking stations to replace the desktop by your phone. A turbulent market.

One of the new products are USB-stick sized computers. Stick them into a TV or monitor, zap in your code and you have your personal working environment. You never need to carry laptops to your hotel-room or conference, as long as a screen is available – any screen.

There are several USB-computers entering the market, but I wanted to introduce you to two. Both of these see a future in a strong processor in a portable device, and both do not have a real product with these strong processors. But you can expect that in 2013 you can have a device that can do very fast parallel processing to have a smooth Photoshop experience… at your key-ring.

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