There has been quite some “find OpenCL” code for CMake around. If you haven’t heard of CMake, it’s the most useful cross-platform tool to make cross-platform software.

Put this into CMakeLists.txt, changing the names for the executable.

#Minimal OpenCL CMakeLists.txt by StreamHPC

cmake_minimum_required (VERSION 3.1)

project(GreatProject)

# Handle OpenCL
find_package(OpenCL REQUIRED)
include_directories(${OpenCL_INCLUDE_DIRS})
link_directories(${OpenCL_LIBRARY})

add_executable (main main.cpp)
target_include_directories (main PUBLIC ${CMAKE_CURRENT_SOURCE_DIR})
target_link_libraries (main ${OpenCL_LIBRARY})

Then do the usual:

• make a build-directory
• cd build
• cmake .. (specifying the right Generator)

Adding your own CMake snippets and you’re one happy dev!

Cmake 3.7

CMake 3.7 makes it even easier! You can do the following:

find_package(OpenCL REQUIRED)
add_executable(test_tgt main.c)
target_link_libraries(test_tgt OpenCL::OpenCL)

This automatically sets up the include paths and target library to link against. No need to use the ${OpenCL_INCLUDE_DIRS} and ${OpenCL_LIBRARIES} any more.

(Thanks Matthäus G. Chajdas for improving this!)

Getting CMake 3.1 or higher

• Ubuntu/Debian: Get the PPA.
• Other Linux: Get the latest tar.gz and compile.
• Windows/OSX: Download the latest exe/dmg from the CMake homepage.

If you have more tips to share, put them in the comments.

Approximation computing is allowing larger errors when performing calculations. While most programmers might go the other way (lower error rate) by using doubles for instance, this field is interesting for quite some of us. The reason is that you can get more performance, more bandwidth space and lower power usage in return.

In Neural Networks high precision is not required, but also Big Data approximation computing is very useful. Most important is that you actually think of the possibility to trade in precision when designing your OpenCL software. For example, does your window function need to be very precise or can there be rounding errors? Or do you do iterative steps (more precision needed), or calculate relatively from the starting point (less precision needed)? You can even use relatively more expensive algorithms that compensate with a smaller overall error. Here at StreamHPC we think this through as one of the main optimisation techniques.

Let’s look into what is possible in OpenCL and which is the hardware support. Continue reading “How to do Approximation Computing in OpenCL” →

There are many research papers that claim enormous speed-ups using an accelerator. From our experience a large part is because of code-modernisations (parallisation & optimisation), which makes the claim look false. That’s why we offer peer-reviews for half our rate for CUDA and OpenCL software. The final costs depend on the size and complexity of the code.

We will profile your CPU and Accelerator code on our machines and review the code. The results are the effect of the code-modernisations and the effect of using the accelerator (GPU, XeonPhi, FPGA). With this we hope that we stimulate the effect of code-modernization gets more research attention over using “miracle hardware”.

Don’t misunderstand: GPUs can still get an average of 8x speedup (or 700% speed improvement) over optimised code, which is still huge! But it’s simply not the 30-100x speed-up claimed in the slide at the right.

 

Most systems nowadays have more than just one OpenCL device and often from different vendors. How can they all coexist from a programming standpoint? How do they interact?

OpenCL platforms and OpenCL devices

Firstly, please bear with me for a few words about OpenCL devices and OpenCL platforms.

An OpenCL platform usually corresponds to a vendor. This is responsible for providing the OpenCL implementation for its devices. For instance, a machine with an i7-4790 Intel CPU is going to have one OpenCL platform, probably named “Intel OpenCL” and this platform will include two OpenCL devices: one is the Intel CPU itself and the other is the Intel HD Graphics 4600 GPU. This Intel OpenCL platform is providing the OpenCL implementation for the two devices and is responsible for managing them.

Let’s have another example, but this time from outside the Windows ecosystem. A MacBook running OS X and having both the Intel Iris Pro GPU and a dedicated GeForce card will show one single OpenCL platform called “Apple”. The two GPUs and the CPU will appear as devices belonging to this platform. That’s because the “Apple” platform is the one providing the OpenCL implementation for all three devices.

Last but not least, keep in mind that:

• An OpenCL platform can have one or several devices.
• The same device can have one or several OpenCL implementations from different vendors. In other words, an OpenCL device can belong to more than just one platform.
• The OpenCL version of the platform is not necessarily the same with the OpenCL version of the device.

The OpenCL ICD

ICD stands for Installable Client Driver and it refers to a model allowing several OpenCL platforms to coexist. It is actually not a core-functionality, but an extension to OpenCL.

• For Windows and Linux the ICD has been available since OpenCL 1.0.
• OSX doesn’t have an ICD at all. Apple chose to put all the drivers themselves under one host.
• Android did not have the extension under OpenCL 1.1, but people ported its functionality. With OpenCL 2.0 the ICD is also on Android.

How does this model work?

While a machine can have several OpenCL platforms, each with its own driver and OpenCL version, there is always just one ICD Loader. The ICD Loader acts as a supervisor for all installed OpenCL platforms and provides a unique entry point for all OpenCL calls. Based on the platform id, it dispatches the OpenCL host calls to the right driver.

This way you can compile against the ICD (opencl.dll on Windows or libOpenCL.so on Linux), not directly to all the possible drivers. At run-time, an OpenCL application will search for the ICD and load it. The ICD in turn looks in the registry (Windows) or a special directory (Linux) to find the registered OpenCL drivers. Each OpenCL call from your software will be resolved by the ICD, which will further dispatch requests to the selected OpenCL platform.

A few things to keep in mind

The ICD gets installed on your system together with the drivers of the OpenCL devices. Hence, a driver update can also result in an update of the ICD itself. To avoid problems, an OS can decide to handle the OpenCL itself.

Please note that the ICD, the platform and the OpenCL library linked against the application may not necessarily correspond to the same OpenCL version.

I hope this explains how the ICD works. If you have any question or suggestion, just leave a comment. Also check out the Khronos page for the ICD extension. And if you need the sources to build your own ICD (with license that allows you to distribute it with your software), check the OpenCL registry on Khronos.

At StreamHPC we mostly work for companies in the bigger countries of Europe and North America. We hardly work for companies in the Netherlands. But it seems that after 5 years of sleeping, there is some shaking. Below is a (translated) article with the above quote by Prof. Dr. Ir. Henri Bal, professor at the Computer section at the Vrije University of Amsterdam.

Lack of knowledge of parallel programming will cause a situation where only one thousandth of the capacity of computers will be used. This makes computations unnecessarily slow and inaccurate. That in turn will slow down the development of the Dutch knowledge economy.

Sequential programming, instructing computers to perform calculations in a queue, is now the standard. Computers processors, however, are much more sophisticated and able to perform thousands or even millions of computations simultaneously. But the programming of such many-cores “is still in its infancy, industries that rely heavily on data, can not perform optimally”, claims Ball.

The value of parallel programming, according to Ball, is of enormous importance, for example, meteorology and forensics. “For weather forecasting data from the dense network of computers need to be quickly and accurately processed to have a weather forecast for tomorrow, not after 48 hours,” he says. “In forensics all data should be explored in the first 24 hours after a crime as soon as possible and through pattern recognition all data, for no trace to be lost. The video material of 80,000 security cameras which was manually searched through after the attack on the London Underground in 2005 – with parallel computing methods this can now rapidly be executed by the computer.”

If the Netherlands wants to widen the gap investments are necessary, says Bal. The focus should be on research and teaching. “Investments in research on programming new massively-parallel machines are required to gain knowledge. Thus it must be examined how programs should be written for parallel computing methods and what extent of parallel calculations can be performed automatically. In teaching our future programmers need also to be prepared for the new standards of parallel programming. Only then the Netherlands can make optimal use of the available computer capacity. “

I think my fellow countrymen will be surprised they can find help just around the corner. And if they wait two more years, then 1000x speed-up from sequential programs are indeed becoming possible.

Have you seen similar articles that sequential programming is slowing the knowledge economy?

Several Chinese phones bring OpenCL to millions of users, as MediaTek offers their drivers to all phone vendors who use their (recent) chipsets.

Mediatek said that you just need a phone with one of the below chipsets and you can run your OpenCL-app, as they provide the driver-stack with the hardware to their customers. I’ve added a few phone names, but there is no guarantee OpenCL drivers are actually there. So be on the safe side and don’t buy the cheapest phone, but a more respected China-brand. Contact us if you got a phone with the chipset that doesn’t work – then I’ll contact Mediatek. Share you experience with the chipset in the comments.

In case you want to use the phone for actual use, be sure it supports your 4G frequencies. Also check this Gizchina article on the below chipsets. There are more MediaTek-chipsets that support OpenCL, but not openly – they prefer to focus on their latest 64-bit series.

Important note on conformance: Mediatek is an adopter and does conform for a few processors. Of the ones listed below, only MT6795 is certain to have official support. Continue reading “MediaTek’s partners deliver OpenCL on their phones” →

The green500 is out and one unknown processor takes the number one position with a huge improvement over last year. It is a new super-computer installed at RIKEN with an incredible 7 GFLOPS/Watt. It is powered by the processor-boards at the right: two Xeons, 4 PEZY-SC 1.4 accelerators and 128GB DRAM, which have a combined performance of about 6.2 TFLOPS. It has been designed for immersive cooling.

The second and third positions are also powered by the PEZY-SC, before we find the winner of last year: the AMD FirePro S9150 and a bit after that the rest (mostly NVidia Tesla). One constant is the CPUs used: Intel XEON is taking most. To my big surprise no ARM64.

From the third to the first PEZY-SC installation there is an improvement of 13%. It seems the first two are the new type, called “bricks”, while the third is the same as last year. Comparing with that super from last year (4.4945 GFLOPS/W) there is an improvement of 42% and 25%. The 13% improvement from the previous version is interesting enough, but the 25% improvement on exactly the same system raised questions. Probably it is due to compiler-optimisations. As the November-version of the Green500 is much more strict, it will be clear if the rules were bent – let’s hope it’s for real!

It supports OpenCL!

When new accelerators support OpenCL, it gets accepted more easily. So it is very interesting the PEZY-SC runs on OpenCL. I asked at ISC and got explained it was a subset of OpenCL, but could not get the finger on which subset, nor could I get access to test it. It does mean that code that would run well on this machine is easy to port. And then I mean the same “easy” Intel uses for explaining the easyness of porting OpenMP software to XeonPhi: PEZI-specific optimisations and writing around the missing functionality would still take effort – the typical stuff we do at StreamHPC.

RIKEN Shoubu

Some information on “Shoubu” (“Iris” in Japanese), the top 1 on the Green 500. According to the Green500 it is 353.8 TFLOPS (based on 50kW, using an actual benchmark). On 25 June RIKEN announced the Shoubu is 2 PFLOPS (theoretical). If the full machine is used for the Green500, then the efficiency was only 18%!

Below are some images of the installation.

Source: http://www.exascaler.co.jp/wp-content/uploads/2015/06/20150625.pdf

An important part is Exascaler’s immersion technology, what I understood is a spin-off of PEZY. I’m very curious what the AMD FirePro S9150 does when it uses immersion-cooling – I think we have to do some frying at the office to find out.

PEZY-SC1.4 and PEZY-SC2

PEZY started with a multi-core processor of 512 cores, the PEZY-1. The PEZY-SC has 1024 cores and has had a few gradual upgrades – currently PEZY-SC 1.4 (“the brick”) is installed.

PEZY-SC Specification:

Logic Cores(PE)	1,024
Core Frequency	733MHz
Peak Performance	Floating Point. Single 3.0TFlops / Double 1.5TFlops
Host Interface	PCI Express GEN3.0 x8Lane x 4Port (x16 bifurcation available) JESD204B Protocol support
DRAM Interface	DDR4, DDR3 combo 64bit x 8Port Max B/W 1533.6GB/s +Ultra WIDE IO SDRAM (2,048bit) x 2Port Max B/W 102.4GB/s
Control CPU	ARM926 dual core
Process Node	28nm
Package	FCBGA 47.5mm x 47.5mm, Ball Pitch 1mm, 2,112pin

Source: http://pezy.co.jp/en/products/pezy-sc.html

Development on PEZY-SC2 is ongoing, which will have a staggering 4096 cores. Ofcourse efficiency has to go up (if the 18% is correct), to make this a good upgrade.

There is no promise on when the PEZY-SC2 will be announced, but it will certainly surprise us again hen it arrives.

Intel has assumed a lot if it comes to XeonPhi’s. One was that you will use it on dual-Xeon servers or workstations and that you already have a professional supplier of motherboards and other computer-parts. We can only guess why they’re not supporting non-professional enthusiasts who got the cheap XeonPhi.

After browsing half the internet to find an overview of motherboards, I eventually emailed Gigabyte, Asus and ASrock for more information for a desktop-motherboard that supports the blue thing. With the information I got, I could populate the below list. Like usual we share our findings with you.

Quote that applies here: “The main reason business grade computer supplies can be sold at a higher price is that the customers don’t know what they’re buying“. When I heard, I did not know why the customer is not well-informed – now I do. Continue reading “Xeon Phi Knights Corner compatible workstation motherboards” →

Date: 17 September 2015, 17:00
Location: Naritaweg 12B, Amsterdam
Costs: free

Selecting the right hardware for your OpenCL-powerd product is very important. We therefore organise a three hour open house where we you can test, benchmark and discuss many available chipsets that support OpenCL. For each showcased board you can read and hear about the advantages, disadvantages and preferred types of algorithms.

Board with the following chipsets will be showcased:

• ARM Mali
• Imagination PowerVR
• Qualcomm Snapdragon
• NVidia Tegra
• Freescale i.MX6 / Vivante
• Adapteva

Several demo’s and benchmarks are prepared that will continuously run on each board. We will walk around to answer your questions.

During the evening drinks and snacks are available.

Test your own code

There is time to test OpenCL code for free in our labs. Please get in contact, as time that evening is limited.

Registration

Register by filling in the below form. Mention with how many people you will come, if you come by car and if you want to run your own code.

[contact_form]

After two events in the Netherlands, the third GPGPU-day will be held in Copenhagen!

Speakers are researchers and companies from the Nordic and Scandinavian countries. If you’re interested in speaking at the conference, get in touch.

Date: 29 September 2015
Location: Copenhagen, DK
Venue: Danish Architecture Centre (route)
Price, early bird: €225
Price, student: €100
Price, normal: €275

The main goal is to connect GPGPU specialist and researchers from the Nordic countries. In the Netherlands, a small country of 17 million, this has helped a lot to interlink 13 research groups. The Scandinavian/Nordic countries have a combined population of 26 million, potentially having around 20 GPU-related research groups. We hope this event will help in starting several collaborations and with that more activity around GPGPU in the north of Europe.

Sponsor packages start at €500 including one ticket.

For an impression, see the below video.

Call for speakers

Are you are doing research using OpenCL or CUDA, and are situated in Denmark, Iceland, Norway, Sweden or Finland, you are invited to speak about your work in front of 60 to 70 people. We expect visitors from Germany and several other European countries too.

Time-slots are 25 minutes, with a maximum of 8 slots available.

Why should you attend?

• Meet others in the parallel programming industry and research.
• Learn about research that is done with GPUs today.
• Find out if you can use GPUs for your own challenges.

See you in Copenhagen!

For more information, see the official page. For tickets, see below.

Update: C++ has been moved from OpenCL 2.1 to 2.2, being released in 2017. Title and text have been updated to reflect this. A reason why Apple released Metal might be the reason that Khronos was too slow in releasing C++ kernels into OpenCL, given the delays.

Apple Metal in one sentence: one queue for both OpenCL and OpenGL, using C++11. They now brought it to OSX. The detail they don’t tell: that’s exactly what the combination of Vulkan + OpenCL 2.1 2.2 does. Instead it is compared with OpenCL 1.x + OpenGL 4.x, which it certainly can compete with, as that combination doesn’t have C++11 kernels nor a single queue.

Apple Metal on OSX – a little too late, bringing nothing new to the stage, compared to SPIR and OpenCL 2.1 2.2.

The main reason why they can’t compete with the standards, is that there is an urge to create high-level languages and DSLs on top of lower-level languages. What Apple did, was to create just one and leaving out the rest. This means that languages like SYCL and C++AMP (implemented on top of SPIR-V) can’t simply run on OSX, and thus blocking new innovations. To understand why SPIR-V is so important and Apple should adopt for that road, read this article on SPIR-V.

Yet another vendor lock-in?

Now Khronos is switching its two most important APIs to the next level, there is a short-term void. This is clearly the right moment for Apple to take the risk and trying to get developers interested in their new language. If they succeed, then we get the well-known “pffff, I have no time to port it to other platforms” and there is a win for Apple’s platforms (they hope).

Apple has always wanted to have a different way of interacting with OpenCL-kernels using Grand Central Dispatch. Porting OpenCL between Linux and Windows is a breeze, but from and to OSX is not. Discussions over the past years with many people from the industry thought me one thing: Apple is like Google, Microsoft and NVidia – they don’t really want standards, but want 100% dedicated developers for their languages.

Yes, now also Apple is on the list of Me-too™ languages for OpenCL. We at StreamHPC can easily translate your code from and too Metal, but we would like it that you can put your investments in more important matters like improving the algorithms and performance.

Still OpenCL support on OSX?

Yes, but only OpenCL 1.2. A way to work around is to use SPIR-to-Metal translators and a wrapper from Vulkan to Metal – this will not make it very convenient though. The way to go, is that everybody starts asking for OpenCL 2.0 support on OSX forums. Metal is a great API, but that doesn’t change the fact it’s obstructing standardisation of likewise great, open standards. If they provide both Metal and Vulkan+OpenCL 2.1 2.2 then I am happy – then the developers have the choice.

Metal debuts in “OSX El Capitan”, which is available per today to developers, and this fall to the general public.

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!” →

From all the news that came out of GDC, I’m most eager to talk about SPIR-V. This intermediate language will make a big difference for the compute-industry. In this article I’d like to explain why. If you need a technical explanation of what SPIR-V is, I suggest you first read gtruc’s article on SPIR-V and then return here to get an overview of the advantages.

Currently there are several shader and c ompute languages, which SPIR-V tries to replace/support. We have GLSL, HLSL for graphics shaders, SPIR (without the V), OpenCL, CUDA and many others for compute shaders.

If you have questions after reading this article, feel free to ask them in a comment or to us directly. Continue reading “8 reasons why SPIR-V makes a big difference” →

Remember the times that the OpenCL compilers where not that good as they’re now? Correct source-code being rejected, typos being accepted, long compile times, crashes during compiling and other irritating bugs. These made the work of an OpenCL developer in “the old days” quite tiresome – you needed a lot of persistence and report bugs. Lucky on desktops the drivers have improved a lot.

Apple’s buggy OpenCL compiler

Now to Apple. There have always been complaints about the irritating bugs that were in Apple’s compiler. Recently the Luxrender community started to make more complaints, as the guy responsible for the OSX port decided to quit. This was due to utter frustration: code that worked on every other OS, simply did not work on OSX. Luxrender’s Paolo Ciccone stood up and made this extremely public, by writing an open letter to Apple’s CEO Tim Cook (posted below).

The letter is not specific about the kind of bugs and and therefore asked him via Twitter which were the bugs he was talking about. He explained me that it’s very simple:

@StreamHPC It's not a few speciific bugs. The drivers simply don't work. Whenever we render anything the OCL compiler aborts.

— Paolo Ciccone (@RealityPaolo) May 6, 2015

Here at StreamHPC we could write around those bugs in most cases, but Luxrender has bigger and more complex kernels than we used in our projects – then it’s simply impossible to write around, as the compiler simply crashes. It seems that OSX still has those old compilers, Linux and Windows used to have years ago.

Metal

Metal is the OpenCL-alternative on iOS 8 and up.

If you’re thinking that Metal could be a reason – that language looks very much like OpenCL, as it’s simply OpenCL as Apple would like it to be. Porting between the two languages is therefore quite simple. This also means that with some small fixes a Metel-kernel could be compiled by existing OpenCL-compiler. Ok, there is much more than the compute part, but the message is that more complex Metal wouldn’t be possible using this driver-stack.

If we end up in a situation that Metal comes to OSX and is more stable than OpenCL, only then we can say that Apple tries to block OpenCL in favour of their own APIs.

The letter

I’m really happy that Paolo Ciccone had the guts to publicly complain. This is the letter he wrote:

Dear Mr. Cook.

I’m sorry to bother you but we have tried all other channels and nothing worked.

I’m part of a group of developers of a physically-based renderer called LuxRender. LuxRender has been written to use OpenCL to accelerate its enormous amount of computation necessary to generate photo-realistic scenes. You can see some of the images generated by Lux at http://luxrender.net. Lux is an Open Source program.

Apple has defined OpenCL and we have adopted this API instead of the proprietary CUDA in order to be able to work with all kind of hardware on all major platforms. It made sense for an OSS to use an open standard.

The reason why I’m writing to you is that, after waiting for years, we still have broken GPU drivers on OS X. Scenes that render perfectly well on Windows and even on Linux simply abort on OS X. This is happening with both AMD and nVidia GPUs.

The problem is unsolvable from our side. We need updated, fixed drivers for OS X. The problem is so bad hat our main OS X developer has announced, today, that he is giving up OS X. He simply can’t do his job.

I kindly request that you look into this and give us working AMD and nVidia drivers in an upcoming, possibly soon, update of OS X. We are more than willing to work with your engineers, if you need any kind of specific help in identifying the problem.

Thank you for your attention.

Paolo Ciccone

If you want to help, also post this letter on your blog or in a forum. The more this is shared, the better. Especially Apple’s forum, asking for the official statement.

Our business is largely around making software faster. For that we use OpenCL, but do you know what this programming language is? Why can’t this speeding-up be done using other languages like Java, C#, C++ or Python?

OpenCL the answer to high-level languages, where we were promised superfast software that was very quick to write. After 20 years this was still a promise, as compilers had to guess too much what was intended. OpenCL gives the programmer more control in the places where more control is needed to get high-performing code and leave less guesses for the compiler.

It’s C with some extra power

It’s like normal C with three extra concepts, all with the aim to make the software run faster.

Explicit Data Transfer

In other introductions to OpenCL the data-transfers are mentioned as one of the last parts, but I find this the most important one. Reason: in most cases this is the main bottleneck in performance-targeted code.

When moving your stuff to another house, you pack all in boxes first before loading the truck. Or would you load each item into the truck one-by-one? Transport-costs would be much higher that way.

While it would be great that the fastest data-transfers should be done automatically, it simply doesn’t work like that. This means that designing the data-transfers is an important task when making fast software. OpenCL lets you do this.

Multiple cores

Most people have heard of “cores”, as made famous by Intel. Each core can do a part of a computation and effectively reduce runtime. OpenCL implements this by isolating the code that runs on each core – what goes in and out the protected code is done explicitly. This way the code is really easy to scale up to thousands of cores.

Would you choose the best-in-class to write the multiplication tables from 1 to 20, or have each student write one of them? Even though the slowest student will limit the rest, the total time is still lower.

Where a normal processor has 1, 2, 4 or 8 cores, a graphics processor has hundreds or even thousands of cores. OpenCL-software works on both.

Vectors

Modern processors can do computations on more than one data-item at the same time. They can be described as sub-cores. This means that each core has parallelism on its own.

When reading, do you read one word at once or character by character? Your brains can parse multiple characters at the same time.

OpenCL has support for “vectors” ( bundles of alike data) to be able to program these sub-cores.

It runs on many types of devices

OpenCL is famous for being the standard programming model for a lot of modern processors. There is no other programming language that can do the same. Support is available on:

• CPUs; standard processors by Intel, AMD and ARM
• GPUs; graphics cards by Intel, AMD and NVIDIA
• FPGAs; processors that are programmed on the hardware-level, by Altera and Xilinx.
• DSPs; digital signal processors by TI
• Mobile graphics processors by ARM, Imagination, Qualcomm, etc.
• See the rest of the list here.

This means that code can be ported to new devices in days or weeks instead of having to rewrite everything from scratch.

How does translating to OpenCL work?

When software needs to be faster, the first step is to find out its bottlenecks – these “hot spots” will be ported to OpenCL, while the rest remains the same. Then comes the hardest part: changing the algorithms such that data-transfers are more efficient and all cores are used. The last step is to look into low-level optimisations like the vectors.

Above is a very simplified representation of OpenCL. Still you’ve seen that the language is very unique and powerful. That will change, as its concepts are slowly getting embedded into existing languages – till then OpenCL is the only standard which fully enables all hardware features.

A while ago I found the slide at the right, claiming that AMD did not have any direct GPU-GPU communication. I found at several sources there was, but it seems not to be a well-known feature. The feature is known as SDI (mostly on network-cards, SSDs and FPGAs), but not much information is found on PCI+SDI. More often RDMA is used: Remote Direct Memory Access (wikipedia).

Questions I try to answer:

• Which server-grade GPUs support direct GPU-GPU communication when using OpenCL?
• What are other characteristics interesting for OpenCL-devs besides direct communication GPU-GPU, GPU-FPGA, GPU-NIC?
• How do you code such fast communication?

Enjoy reading! Continue reading “GPUDirect and DirectGMA – direct GPU-GPU communication via RDMA” →

Here in Amsterdam a lot is going on around HPC. Including StreamHPC, we have companies like Vancis, Netherlands eScience Centre, and ClusterVision, the research institute for Dutch HPC, Surf SARA, (hosting the Dutch supercomputer) and the very busy Amsterdam IX.

Here in Amsterdam we’re focused on building up more local companies around big compute and big data. I’d like to give two examples. One is Scyfer, an academic startup specialised in deep learning. They’ve developed algorithms to more efficiently train neural networks and help their customers find answers quicker. The second is Euvision Technologies, who developed unique computer vision solutions. Last year it has been sold to Qualcomm, for tens of millions.

We welcome new companies to Amsterdam, to further build up the HPC-ecosphere. If you have a company and are seeking a good location, contact us to talk about HPC in Amsterdam.There are many opportunities to develop in Europe, and we’re open for partnerships in new markets.

If you want to start your own HPC-related startup, Amsterdam thinks of you! There are three steps to do:

1. Go to the Venture café on 30 April
2. Apply for the bootcamp
3. Become our neighbours
4. Build your own HPC startup

Ping me, if you want advice on which preparations you need to make, before you can make such big decision. I like to have an open discussion, so please use the comment-area below for what you think of HPC in Amsterdam and building companies.

(if you get to Palo Alto, Manchester, Karlsruhe and Copenhagen)

We’re supporters of open standards and open discussions. When those thow come together, we melt. Therefore I’d like to share four hot conferences with you: IWOCL (Palo Alto, SF, USA), EMiT (Manchester, UK), ParallelCon (Karlsruhe, Germany), GPGPU-day 2015 (Copenhagen, Denmark).

On all these conferences I’ll be there too and are happy to meet you.

This post was shared first via the newsletter. Subscribe here.

Continue reading “Four conferences that will interest you” →

Getting your Windows machine ready for OpenCL is rather straightforward. In short, you only need the latest drivers for your OpenCL device(s) and you’re ready to go. Of course, you will need to add an OpenCL SDK in case you want to develop OpenCL applications but that’s equally easy.

Before we start, a few notes:

• The steps described herein have been tested on Windows 8.1 only, but should also apply for Windows 7 and Windows 8.
• We will not discuss how to write an actual OpenCL program or kernel, but focus on how to get everything installed and ready for OpenCL on a Windows machine. This is because writing efficient OpenCL kernels is almost entirely OS independent.

If you want to know more about OpenCL and you are looking for simple examples to get started, check the Tutorials section on this webpage.

Running an OpenCL application

If you only need to run an OpenCL application without getting into development stuff then most probably everything already works.

If OpenCL applications fail to launch, then you need to have a closer look to the drivers and hardware installed on your machine:

• Check that you have a device that supports OpenCL. All graphics cards and CPUs from 2011 and later support OpenCL. If your computer is from 2010 or before, check this page. You can also find a list with OpenCL conformant products on Khronos webpage.
• Make sure your OpenCL device driver is up to date, especially if you’re not using the latest and greatest hardware. With certain older devices OpenCL support wasn’t initially included in the drivers.

Here is where you can download drivers manually:

• Intel has hidden them a bit, but you can find them here with support for OpenCL 2.0.
• AMD’s GPU-drivers include the OpenCL-drivers for CPUs, APUs and GPUs, version 2.0.
• NVIDIA’s GPU-drivers mention mostly CUDA, but the drivers for OpenCL 1.1 1.2 are there too.

In addition, it is always a good idea to check for any other special requirements that the OpenCL application may have. Look for device type and OpenCL version in particular. For example, the application may run only on OpenCL CPUs, or conversely, on OpenCL GPUs. Or it may require a certain OpenCL version that your device does not support.

A great tool that will allow you to retrieve the details for the OpenCL devices in your system is Caps Viewer.

Developing OpenCL applications

Now it’s time to put the pedal to the metal and start developing some proper OpenCL applications.

The basic steps would be the following:

• Make sure you have a machine which supports OpenCL, as described above.
• Get the OpenCL headers and libraries included in the OpenCL SDK from your favourite vendor.
• Start writing OpenCL code. That’s the difficult part.
• Tell the compiler where the OpenCL headers are located.
• Tell the linker where to find the OpenCL .lib files.
• Build the fabulous application.
• Run and prepare to be awed in amazement.

Ok, so let’s have a look into each of these.

OpenCL SDKs

For OpenCL headers and libraries the main options you can choose from are:

• NVIDIA – CUDA Toolkit. You can grab the OpenCL samples here.
• AMD – AMD APP SDK. Also works with Intel’s CPUs.
  • Headers and OpenCL.lib are here: https://github.com/GPUOpen-LibrariesAndSDKs/OCL-SDK/releases
  • Samples are here: https://github.com/OpenCL/AMD_APP_samples
  • Math libraries are here: https://github.com/clMathLibraries
    
• Intel – the previous Intel SDK for OpenCL is now integrated into Intel’s new tools, such as Intel INDE (which has a free starters edition) or Intel Media Server Studio. Grab any of these in order to have everything ready for building OpenCL code.

As long as you pay attention to the OpenCL version and the OpenCL features supported by your device, you can use the OpenCL headers and libraries from any of these three vendors.

OpenCL headers

Let’s assume that we are developing a 64bit C/C++ application using Visual Studio 2013. To begin with, we need to check how many OpenCL platforms are available in the system:

[raw]

#include<stdio.h>
#include<CL/cl.h>

int main(void)
{
    cl_int err;
    cl_uint numPlatforms;

    err = clGetPlatformIDs(0, NULL, &numPlatforms);
    if (CL_SUCCESS == err)
         printf("\nDetected OpenCL platforms: %d", numPlatforms);
    else
         printf("\nError calling clGetPlatformIDs. Error code: %d", err);

    return 0;
}

[/raw]

We need to specify where the OpenCL headers are located by adding the path to the OpenCL “CL” is in the same location as the other CUDA include files, that is, CUDA_INC_PATH. On a x64 Windows 8.1 machine with CUDA 6.5 the environment variable CUDA_INC_PATH is defined as “C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v6.5\include”

If you’re using the AMD SDK, you need to replace “$(CUDA_INC_PATH)” with “$(AMDAPPSDKROOT)/include” or, for Intel SDK, with “$(INTELOCLSDKROOT)/include“.

OpenCL libraries

Similarly, we need to let the linker know about the OpenCL libraries. Firstly, add OpenCL.lib to the list of Additional Dependencies:

Secondly, specify the OpenCL.lib location in Additional Library Directories:

As in the case of the includes, If you’re using the AMD SDK, replace “$(CUDA_LIB_PATH)” with “$(AMDAPPSDKROOT)/lib/x86_64” , or in the case of Intel with “$(INTELOCLSDKROOT)/lib/x64“.

And you’re good to go! The application should now build and run. Now, just how difficult was it? Happy OpenCL-coding on Windows!

If you have any question or suggestion, just leave a comment.

UPDATE: We now only offer thesis support (“externs”), for students who want to use OpenCL in their research, but don’t have such support at their university. for the rest the below applies.

From July there are several internships available here at StreamHPC, all around self-driving vehicles (or even self-flying drones). This means that with an interest in AI, embedded programming and sensors, you’re all set.

You can work as an intern for a period from 1 to 6 months, and combine it with your thesis. We will assist you with planning, thesis correction and technical support (especially OpenCL). There are also a few other startups in the building, who you’d like to talk with.

Your time will exist of literature studies, programming, testing, OpenCL-optimisations and playing. We’ll work with bikes and toy-cars, so no big cars that are expensive to crash. Study fields are road-location, obstacles, driving-style detection, etc.

If you want to do an internship purely to gain experience, we can offer you a combination of research and working for real customers.

Some targets:

• Create a small test-car full with sensors:
  • radar for distance
  • multi cameras
  • laser
  • other sensors, like touch
• Programming an embedded board with OpenCL-capability.
• Programming pointcloud algorithms in OpenCL.
• Defining the location on the road, also in OpenCL. (taken)
• Detecting pedestrians, signs.
• Have fun creating this.

Please contact us and tell your ideas and plan.

We were too busy lately to tell you about it: OpenCL 2.0 is getting ready for prime time! As it makes use of the more recent hardware features, it’s therefore more powerful than OpenCL 1.x could ever be.

To get you up to speed, see this list of new OpenCL 2.0 features:

• Shared Virtual Memory: host and device kernels can directly share complex, pointer-containing data structures such as trees and linked lists, providing significant programming flexibility and eliminating costly data transfers between host and devices.
• Dynamic Parallelism: device kernels can enqueue kernels to the same device with no host interaction, enabling flexible work scheduling paradigms and avoiding the need to transfer execution control and data between the device and host, often significantly offloading host processor bottlenecks.
• Generic Address Space: functions can be written without specifying a named address space for arguments, especially useful for those arguments that are declared to be a pointer to a type, eliminating the need for multiple functions to be written for each named address space used in an application.
• Improved image support: including sRGB images and 3D image writes, the ability for kernels to read from and write to the same image, and the creation of OpenCL images from a mip-mapped or a multi-sampled OpenGL texture for improved OpenGL interop.
• C11 Atomics: a subset of C11 atomics and synchronization operations to enable assignments in one work-item to be visible to other work-items in a work-group, across work-groups executing on a device or for sharing data between the OpenCL device and host.
• Pipes: memory objects that store data organized as a FIFO and OpenCL 2.0 provides built-in functions for kernels to read from or write to a pipe, providing straightforward programming of pipe data structures that can be highly optimized by OpenCL implementers.
• Android Installable Client Driver Extension: Enables OpenCL implementations to be discovered and loaded as a shared object on Android systems.

I could write many articles about the above subjects, but leave that for later. This article won’t get into these technical details, but more into what’s available from the vendors. So let’s see what toys we were given!

A note: don’t start with OpenCL 2.0 directly, if you don’t know the basic concepts of OpenCL. Continue reading “Overview of OpenCL 2.0 hardware support, samples, blogs and drivers” →