General articles on technical subjects.

Interest in OpenCL

Posted by Vincent Hindriksen on

Since more than a year I have this blog and I want to show the visitors around the world. Why? Then you know where OpenCL is popular and where not. I chose an unknown period, so you cannot really reverse engineer how many visitors I have – but the nice thing is that not much changes between a few days and a month. Unluckily Google Analytics is not really great for maps (Greenland as big as Africa, hard to compare US states to EU countries, cities disappear at world-views, etc), so I needed to do some quick image-editing to make it somewhat clearer.

At the world-view you see that the most interest comes from 3 sub-continents: Europe, North America and South-East Asia. Africa is the real absent continent here, except some Arab countries and South-Africa only some sporadic visits from the other countries. What surprises me is that the Arab countries are among my frequent visitors – this could be a language-issue, but I expected about the same number of visitors as from i.e. China. Latin America has mostly only interest from Brazil.

Continue reading “Interest in OpenCL” →

PDFs of Monday 12 September

Posted by Vincent Hindriksen on

As it got more popular that I shared my readings, I decided to put them on my site. I focus on everything that uses vector-processing (GPUs, heterogeneous computing, CUDA, OpenCL, GPGPU, etc). Did I miss something or you have a story you want to share? Contact me or comment on this article. If you tell others about these projects you discovered here, I would appreciate you mention my website or my twitter @StreamHPC.

The research-papers have their authors mentions; the other links can be presentations or overviews of (mostly) products. I have read all, except the long PhD-theses (which are on my non-ad-hoc reading-list) – drop me any question you have.

Bullet Physics, Autodesk style. AMD and Autodesk on integrating Bullet Physics engine into Maya.

MERCUDA: Real-time GPU-based marine scene simulation. OpenCL has enabled more realistic sea and sky simulation for this product, see page 7.

J.P.Morgan: Using Graphic Processing Units (GPUs) in Pricing and Risk. Two pages describing OpenCL/CUDA can give 10 to 100 times speedup over conventional methods.

Parallelization of the Generalized Hough Transform on GPU (Juan Gómez-Luna1a, José María González-Linaresb, José Ignacio Benavidesa, Emilio L. Zapatab and Nicolás Guil). Describing two parallel methods for the Fast Generalized Hough Transform (Fast GHT) using GPUs, implemented in CUDA. It studies how load balancing and occupancy impact the performance of an application on a GPU. Interesting article as it shows that you can choose in which limits you bump into.

Performance Characterization and Optimization of Atomic Operations on AMD GPUs (Marwa Elteir, Heshan Lin and Wu-chun Feng). Measurement of the impact of using atomic operations on AMD GPUs. It seems that even mentioning ‘atomic’ puts the kernel in atomic mode and has major influence on the performance. They also come up with a solution: software-based atomic operation. Work in progress.

On the Efficacy of a Fused CPU+GPU Processor (or APU) for Parallel Computing (Mayank Daga, Ashwin M. Aji, and Wu-chun Feng). Another one from Virginia Tech, this time on AMD’s APUs. This article measures its performance via a set of micro-benchmarks (e.g., PCIe data transfer), kernel benchmarks (e.g., reduction), and actual applications (e.g., molecular dynamics). Very interesting to see in which cases discrete GPUs have a disadvantage even with more muscle power.

A New Approach to rCUDA (José Duato, Antonio J. Peña, Federico Silla1, Juan C. Fernández, Rafael Mayo, and Enrique S. Quintana-Ort). On (remote) execution of CUDA-software within VMs. Interesting if you want powerful machines in your company to delegate heavy work to, or are interested in clouds.

Parallel Smoothers for Matrix-based Multigrid Methods on Unstructured Meshes Using Multicore CPUs and GPUs (Vincent Heuveline, Dimitar Lukarski, Nico Trost and Jan-Philipp Weiss). Different methods around 8 multi-colored Gauß-Seidel type smoothers using OpenMP and GPUs. Also some words on scalability!

Visualization assisted by parallel processing (B. Lange, H. Rey, X. Vasques, W. Puech and N. Rodriguez). How to use GPGPU for visualising big data. An important factor of real-time data-processing is that people get more insight in the matter. As an example they use temperatures in a server-room. As I see more often now, they benchmark CPU, GPU and hybrids.

A New Tool for Classification of Satellite Images Available from Google Maps: Efficient Implementation in Graphics Processing Units (Sergio Bernabéa and Antonio Plaza).  30 times speed-up with a new parallel implementation of the k-means unsupervised clustering algorithm in CUDA. Ity is used for classification of satellite images.

TAU performance System. Product-presentation of TAU which does, among other things, parallel profiling and tracing. Support for CUDA and OpenCL. Extensive collection of tools, so worth to spend time on. An paper released in March describes TAU and compares it with two other performance measurement systems: PAPI and VamirTrace.

An Experimental Approach to Performance Measurement of Heterogeneous Parallel Applications using CUDA (Allen D. Malony, Scott Biersdorff, Wyatt Spear and Shangkar Mayanglambam). Using a TAU-based (see above) tool TAUcuda this paper describes where to focus on when optimising heterogeneous systems.

Speeding up the MATLAB complex networks package using graphic processors (Zhang Bai-Da, Wu Jun-Jie, Tang Yu-Hua and Li Xin). Free registration required. Their conclusion: “In a word, the combination of GPU hardware and MATLAB software with Jacket Toolbox enables high-performance solutions in normal server”. Another PDF I found was: Parallel High Performance Computing with emphasis on Jacket based computing.

Profile-driven Parallelisation of Sequential Programs (Georgios Tournavitis). PhD-thesis on a new approach for extracting and exploiting multiple forms of coarse-grain parallelism from sequential applications written in C.

OpenCL, Heterogeneous Computing, and the CPU. Presentation by Tim Mattson of Intel on how to use OpenCL with the vector-extensions of Intel-processors.

MMU Simulation in Hardware Simulator Based-on State Transition Models (Zhang Xiuping, Yang Guowu and Zheng Desheng). It seems a bit off-chart to have a paper on the Memory Management Unit of a ARM, but as the ARM-processor gets more important some insights on its memory-system is important.

Multi-Cluster Performance Impact on the Multiple-Job Co-Allocation Scheduling (Héctor Blanco, Eloi Gabaldón, Fernando Guirado and Josep Lluí Lérida). This research-group has developed a scheduling-technique, and in this paper they discuss in which situations theirs works better than existing techniques.

Convey Computers: Putting Personality Into High Performance Computing. Product-presentation. They combine X86-CPUs with pre-programmed FPGAs to get high though-put. In short: if you make heavy usage of the provided algorithms, then this might be an alternative to GPGPU.

High-Performance and High-Throughput Computing. What it means for you and your research. Presentation by Philip Chan of Monash University. Though the target-group is their own university, it gives nice insights on how it goes around on other universities and research-groups. HPC is getting cheaper and accepted in more and more types of research.

Bull: Porting seismic software to the GPU. Presentation for oil-companies on finding new oil-fields. These seismic calculations are quite computation-intensive and therefore portable HPC is needed. Know StreamHPC is also assisting in porting such code to GPUs.

Dymaxion: Optimizing Memory Access Patterns for Heterogeneous Systems (Shuai Che, Jeremy W. Sheaffer and Kevin Skadron). This piece of software allows CUDA-programmers to optimize memory mappings to improve the efficiency of memory accesses on heterogeneous platforms.

Real-time volumetric shadows for dynamic rendering (MsC-thesis of Alexandru Teodor V.L. Voicu). Self-shadowing using the Opacity Shadow Maps algorithm is not fit for real-time processing. This thesis discusses Bounding Opacity Maps, a novel method to overcome this problem. Including code at the end, which you can download here.

Accelerating Foreign-Key Joins using Asymmetric Memory Channels (Holger Pirk, Stefan Manegold and Martin Kersten). Shows how to accelerate Foreign-Key Joins by executing the random table lookups on the GPU’s VRAM while sequentially streaming the Foreign-Key-Index through the PCI-E Bus. Very interesting on how to make clever usage of I/O-bounds.

Come back next Monday for more interesting research papers and product presentations. If you have questions, don’t hesitate to contact StreamHPC.

PDFs of Monday 5 September

Posted by Vincent Hindriksen on

Live from le Centre Pompidou in Paris: Monday PDF-day. I have never been inside the building, but it is a large public library where people are queueing to get in – no end to the knowledge-economy in Paris. A great place to read some interesting articles on the subjects I like.

CUDA-accelerated genetic feedforward-ANN training for data mining (Catalin Patulea, Robert Peace and James Green). Since I have some background on Neural Networks, I really liked this article.

Self-proclaimed State-of-the-art in Heterogeneous Computing (Andre R. Brodtkorb a , Christopher Dyken, Trond R. Hagen, Jon M. Hjelmervik, and Olaf O. Storaasli). It is from 2010, but just got thrown on the net. I think it is a must-read on Cell, GPU and FPGA architectures, even though (as also remarked by others) Cell is not so state-of-the-art any more.

OpenCL: A Parallel Programming Standard for Heterogeneous Computing Systems (John E. Stone, David Gohara, and Guochun Shi). A basic and clear introduction to my favourite parallel programming language.

Research proposal: Heterogeneity and Reconfigurability as Key Enablers for Energy Efficient Computing. About increasing energy efficiency with GPUs and FPGAs.

Design and Performance of the OP2 Library for Unstructured Mesh Applications. CoreGRID presentation/workshop on OP2, an open-source parallel library for unstructured grid computations.

Design Exploration of Quadrature Methods in Option Pricing (Anson H. T. Tse, David Thomas, and Wayne Luk). Accelerating specific option pricing with CUDA. Conclusion: FPGA has the least Watt per FLOPS, CUDA is the fastest, and CPU is the big loser in this comparison. Must be mentioned that GPUs are easier to program than FPGAs.

Technologies for the future HPC systems. Presentation on how HPC company Bull sees the (near) future.

Accelerating Protein Sequence Search in a Heterogeneous Computing System (Shucai Xiao, Heshan Lin, and Wu-chun Feng). Accelerating the Basic Local Alignment Search Tool (BLAST) on GPUs.

PTask: Operating System Abstractions To Manage GPUs as Compute Devices (Christopher J. Rossbach, Jon Currey, Mark Silberstein, Baishakhi Ray, and Emmett Witchel). MS research on how to abstract GPUs as compute devices. Implemented on Windows 7 and Linux, but code is not available.

PhD thesis by Celina Berg: Building a Foundation for the Future of Software Practices within the Multi-Core Domain. It is about a Rupture-model described at Ch.2.2.2 (PDF-page 59). [total 205 pages].

Workload Balancing on Heterogeneous Systems: A Case Study of Sparse Grid Interpolation (Alin Murarasu, Josef Weidendorfer, and Arndt Bodes). To my opinion a very important subject as this can help automate much-needed “hardware-fitting”.

Fraunhofer: Efficient AMG on Heterogeneous Systems (Jiri Kraus and Malte Förster). AMG stands for Algebraic MultiGrid method. Paper includes OpenCL and CUDA benchmarks for NVidia hardware.

Enabling Traceability in MDE to Improve Performance of GPU Applications (Antonio Wendell de O. Rodrigues, Vincent Aranega, Anne Etien, Frédéric Guyomarc’h, Jean-Luc Dekeyser). Ongoing work on OpenCL code generation from UML (Model Driven Design). [34 pag PDF]

GPU-Accelerated DNA Distance Matrix Computation (Zhi Ying, Xinhua Lin, Simon Chong-Wee See and Minglu Li). DNA sequences distance computation: bit.ly/n8dMis [PDF] #OpenCL #GPGPU #Biology

And while browsing around for PDFs I found the following interesting links:

  • Say bye to Von Neumann. Or how IBM’s Cognitive Computer Works.
  • Workshop on HPC and Free Software. 5-7 October 2011, Ourense, Spain. Info via j.anhel@uvigo.es
  • Basic CUDA course, 10 October, Delft, Netherlands, €200,-.
  • Par4All: automatic parallelizing and optimizing compiler for C and Fortran sequential programs.
  • LAMA: Library for Accelerated Math Applications for C/C++.

PDFs of Monday 29 August

Posted by Vincent Hindriksen on

This is the first PDF-Monday. It started as I used Mondays to read up on what happens around OpenCL and I like to share with you. It is a selection of what I find (somewhat) interesting – don’t hesitate to contact me on anything you want to know about accelerated software.

Parallel Programming Models for Real-Time Graphics. A presentation by Aaron Lefohn of Intel. Why a mix of data-, task-, and pipeline-parallel programming works better using hybrid computing (specifically Intel processors with the latest AVX and SSE extensions) than using GPGPU.

The Practical Reality of Heterogeneous Super Computing. A presentation of Rob Farber of NVidia on why discrete GPUs has a great future even if heterogeneous processors hit the market. Nice insights, as you can expect from the author of the latest CUDA-book.

Scalable Simulation of 3D Wave Propagation in Semi-Infinite Domains Using the Finite Difference Method (Thales Luis Rodrigues Sabino, Marcelo Zamith, Diego Brandâo, Anselmo Montenegro, Esteban Clua, Maurício Kischinhevksy, Regina C.P. Leal-Toledo, Otton T. Silveira Filho, André Bulcâo). GPU based cluster environment for the development of scalable solvers for a 3D wave propagation problem with finite difference methods. Focuses on scattering sound-waves for finding oil-fields.

Parallel Programming Concepts – GPU Computing (Frank Feinbube) A nice introduction to CUDA and OpenCL. They missed task-parallel programming on hybrid systems with OpenCL though.

Proposal for High Data Rate Processing and Analysis Initiative (HDRI). Interesting if you want to see a physics project where they did not have decided yet to use GPGPU or a CPU-cluster.

Physis: An Implicitly Parallel Programming Model for Stencil Computations on Large-Scale GPU-Accelerated Supercomputers (Naoya Maruyama, Tatsuo Nomura, Kento Sato and Satoshi Matsuoka). A collection of macros for GPGPU, tested on TSUBAME2.

MPI in terms of OpenCL

Posted by Vincent Hindriksen on with 7 Comments

OpenCL is a member of a family of Host-Kernel programming language extensions. Others are CUDA, IMPC and DirectCompute/AMP. It lets itself define by a separate function or set of functions referenced to as kernel, which are prepared and launched by the host to run in parallel. Added to that are deeply integrated language-extensions for vectors, which gives an extra dimension to parallelism.

Except from the vectors, there is much overlap between Host-Kernel-languages and parallel standards like MPI and OpenMP. As MPI and OpenMPI have focused on how to get software parallel for years now, this could give you an image of how OpenCL (and the rest of the family) will evolve. And it answers how its main concept message-passing could be done with OpenCL, and more-over how OpenCL could be integrated into MPI/OpenMP.

At the right you see bees doing different things, which is easy to parallellise with MPI, but currently doesn’t have the focus of OpenCL (when targeting GPUs). But actually it is very easy to do this with OpenCL too, if the hardware supports it such like CPUs.

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Is OpenCL coming to Apple iOS?

Posted by Vincent Hindriksen on with 3 Comments

Answer: No, or not yet. Apple tested Intel and AMD hardware for OSX, and not portable devices. Sorry for the false rumour; I’ll keep you posted.

Update: It seems that OpenCL is on iOS, but only available to system-libraries and not for apps (directly). That explains part of the responsiveness of the system.

At the thirteenth of August 2011 Apple askked the Khronosgroup to test 7 unknown devices if they are conformant with OpenCL 1.1. As Apple uses OpenCL-conformant hardware by AMD, NVidia and Intel in their desktops, the first conclusion is that they have been testing their iOS-devices. A quick look at the list of available iOS devices for iOS 5 capable devices gives the following potential candidates:

  • iPhone 3GS
  • iPhone 4
  • iPhone 5
  • iPad
  • iPad 2
  • iPod Touch 4th generation
  • Apple TV
If OpenCL comes to iOS soon (as it is already tested), iOS 5 would be the moment. iOS 5 processors are all capable of getting speed-up by using OpenCL, so it is no nonsense-feature. This could speed up many features among media-conversion, security-enhancements and data-manipulation of data-streams. Where now the cloud or the desktop has to be used, in the future it can be done on the device.

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Power to the Vector Processor

Posted by Vincent Hindriksen on

Reducing energy-consumption is “hot”

After reading this article “Nvidia is losing on the HPC front” by The Inquirer which mixes up the demand for low-power architectures with the other side of the market: the demand for high performance. It made me think that it is not that clear there are two markets using the same technology. Also Nvidia has proven it to be not true, since the super-computer “Nebuale” uses almost half the watts per flop as the #1. How come? I quote The Register from an article of one year old:

>>When you do the math, as far as Linpack is concerned, Jaguar takes just under 4 watts to deliver a megaflops at a cost of $114 per megaflops for the iron, while Nebulae consumes 2 watts per megaflops at a cost of $39 per megaflops for the system. And there is little doubt that the CUDA parallel computing environment is only going to get better over time and hence more of the theoretical performance of the GPU ends up doing real work. (Nvidia is not there yet. There is still too much overhead on the CPUs as they get hammered fielding memory requests for GPUs on some workloads.)<<

Nvidia is (and should) be very proud. But actually I’m already looking forward when hybrids get more common. They will really shake up the HPC-market (as The Register agrees) in lowering latency between GPU and CPU and lowering energy-consumption. But where we can find a bigger market is the mobile market.

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Aparapi: OpenCL in Java

Posted by Vincent Hindriksen on with 2 Comments

Edit: Aparapi has been open sourced and many issues have already been fixed and improved.

If you have an AMD GPU/APU, you should try Aparapi. This software lets you write OpenCL-code in Java pretty high-level. The idea is that is sort of that it processes the Java intermediate code to search for loops and then create optimised OpenCL-kernels. Just download Aparapi and try the two examples. As the current version is still in alpha, it is not flawless yet. What I think is important when having worked with Aparapi is that you learn how to keep it simple – like you know that you can gain most speed on straight roads and turns slow down.

The Aparapi-team tries to avoid explicit defining of local memory, but it is still possible by using the @Local annotation. Such decisions show the team wants Aparapi to be high-level. It also integrates well with JavaCL and JOCL, so for the kernels you already have created, you can mix. You can also check out a video introducing Aprapi (it is video 15, if #-linking doesn’t work).

Time to create your own project. As not all errors are documented or are solved in the upcoming version, below you will find a list of common errors and how to easily solve them.

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Keep The Hardware Focus

Posted by Vincent Hindriksen on
The real Apu

If you buy a car, the first choice is not often the kind of fuel. You first select on the engine-properties, the looks, the interior, the brand and for sure the total cost of ownership. The costs can be a reason to choose for a certain type of fuel though. In the parallel computation world it is different. There the fuel (CUDA or OpenCL) is the first decision and then the hardware is chosen. I think this is wrong and therefore speak a lot about CUDA-vs-OpenCL, while I think NVidia is a good choice for a whole list of algorithms.

If we give advise during a consult, we want to give the best advice. In case of CUDA, that would be based on budget to go for Tesla or the latest GTX; in case of OpenCL we can give much better advice on hardware. But actually starting with the technique is the worst thing you can do: focus on the hardware and then pick the technique that suits best.

IMPORTANT. The following is for understanding some concepts and limits only! It is pure theoretically, so I don’t claim any real-world results. Also what not is taken into account is how well different processors handle control-instructions (for, while, if, case, etc), which has quite some influence on actual performance.

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Exposing OpenCL on Android: Q&A with Tim Lewis of ZiiLabs

Posted by Vincent Hindriksen on

ZiiLabs has been offering an early access program for OpenCL SDK since last year. This program was very selective in choosing developers and little news has been put on their webpage. Now they are planning to make their Android NDK a standard component, it’s a good time to ask them some questions. GPGPU-consultant Liad Weinberger of Appilo also added a few questions.

The Q&A has been with Tim Lewis, director Marketing and Partner Relations of ZiiLabs, who has taken the time to give some insights in what we can expect around accelerated computations on Android. ZiiLabs has been better known as 3DLabs and has reinvented itself in 2009 (you can read the full history here). Like other companies in the ARM-industry they mostly design chips and let other parties manufacture devices using their schematics, drivers and software. Now to the questions.

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Basic concept: Hosts and devices

Posted by Vincent Hindriksen on

Time for some basic concepts of OpenCL. As I notice a growing number of visitors to this page, I notices I have actually not written much about coding and basics.

One of the first steps of an OpenCL program is selecting hosts and devices. If you program for a tablet, which has one chip and a screen, you don’t think of several devices. And if you log in on a server, your context is there is one host and that’s the one you logged into. If you have read my article about how to install all drivers on Ubuntu, you have gotten several clues. I added some tips&tricks, but not too many. If you know more stuff about this subject yourself, please share with others in the comments.

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Qt Creator OpenCL Syntax Highlighting

Posted by Vincent Hindriksen on

With highlighting for Gedit, I was happy to give you the convenience of a nice editor to work on OpenCL-files. But it seems that one of the most popular IDEs for C++-programming is Qt Creator. So you receive another free syntax highlighter. You need at least Qt Creator 2.1.0.

The people of Qt have written everything you need to know about their Syntax highlighting, which was enough help to create this file. You see that they use the system of Kate, so logically this file works with this editor too.

In this article there is all you need to know to use Qt Creator with OpenCL.

Installing

First download the file to your computer.

Under Windows and OSX you need to copy this file to the directory shareqtcreatorgeneric-highlighter in the Qt installation dir (i.e. c:Qtqtcreator-2.2.1shareqtcreatorgeneric-highlighter). Under Linux copy this file to ~/.kde/share/apps/katepart/syntax or to /usr/share/kde4/apps/katepart/syntax (all users). That’s all, have fun!

Install OpenCL on Debian, Ubuntu and Mint orderly

Posted by Vincent Hindriksen on with 22 Comments
Libraries – can’t have enough

If you read different types of manuals how to compile OpenCL software on Linux, then you can get dizzy of all the LD-parameters. Also when installing the SDKs from AMD, Intel and NVIDIA, you get different locations for libraries, header-files, etc. Now GPGPU is old-fashioned and we go for heterogeneous programming, the chances get higher you will have more SDKs on your machine. Also if you want to keep it the way you have, reading this article gives you insight in what the design is after it all. Note that Intel’s drivers don’t give OpenCL support for their GPUs, but CPUs only.

As my mother said when I was young: “actually cleaning up is very simple”. I’m busy creating a PPA for this, but that will take some more time.

First the idea. For developers OpenCL consists of 5 parts:

  • GPUs-only: drivers with OpenCL-support
  • The OpenCL header-files
  • Vendor specific libraries (needed when using -lOpenCL)
  • libOpenCL.so -> a special driver
  • An installable client driver

Currently GPU-drivers are always OpenCL-capable, so you only need to secure 4 steps. These are discussed below.

Please note that in certain 64-bit distributions there is not lib64, but only ‘lib’ and ‘lib32’. If that is the case for you, you can use the commands that are mentioned with 32-bit.

Continue reading “Install OpenCL on Debian, Ubuntu and Mint orderly” →

OpenCL vs CUDA Misconceptions

Posted by Vincent Hindriksen on with 47 Comments

Translation available: Russian/Русский. (Let us know if you have translated this article too… And thank you!)

Last year I explained the main differences between CUDA and OpenCL. Now I want to get some old (and partly) false stories around CUDA-vs-OpenCL out of this world. While it has been claimed too often that one technique is just better, it should be also said that CUDA is better in some aspects, whereas OpenCL is better in others.

Why did I write this article? I think NVIDIA is visionary in both technology and marketing. But as I’ve written before, the potential market for dedicated graphics cards is shrinking and therefore forecasting the end of CUDA on desktop. Not having this discussion opens the door for closed standards and delaying innovation, which can happen on top of OpenCL. The sooner people & companies start choosing for a standard that gives equal competitive advantages, the more we can expect from the upcoming hardware.

Let’s stand by what we have learnt at school when gathering information sources, don’t put all your eggs in one basket! Gather as many sources and references as possible. Please also read articles which claim (and underpin!) why CUDA has a more promising future than OpenCL. If you can, post comments with links to articles you think others should read too. We appreciate contributions!

Also found that Google Insights agrees with what I constructed manually.

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Intel’s OpenCL SDK examples for GCC

Posted by Vincent Hindriksen on with 3 Comments

Update august 2012: There is a new post for the latest Linux examples.

Note: these patches won’t work anymore! You can learn from the patches how to fix the latest SDK-code for GCC and Linux/OSX.

Code-examples are not bundled with the Linux OpenCL SDK 1.1 beta. Their focus is primarily Windows, so VisualStudio seems to be a logical target. I just prefer GCC/LLVM which you can get to work with all OSes. After some time trying to find the alternatives for MS-specific calls, I think I managed. Since ShallowWater uses DirectX and is quite extensive, I did not create a patch for that one – sorry for that.

I had a lot of troubles getting the BMP-export to work, because serialisation of the struct added an extra short. Feedback (such as a correct BMP-export of a file) is very welcome, since I the colours are correct. For the rest: most warnings are removed and it just works – tested with g++ (Ubuntu/Linaro 4.5.2-8ubuntu4) 4.5.2 on 64 bit (llvm-g++-4.2 seems to work too, but not fully tested).

THE PATCHES ARE PROVIDED AS IS – NO WARRANTIES!

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InsideHPC: SuperComputing. Where to from here?

Posted by Vincent Hindriksen on

In this video, Moderator Bob Feldman hosts a session entitled: Supercomputing: Where to from Here? Recorded at the National HPCC Conference 2011 in Newport.

Panelists:
Dr. Eng Lim Goh, SGI
Bill Feiereisen, Intel
Shumel Shottan, BlueARC
Steve Lyness, Appro International, Inc.
Marc Hamilton, HP Americas

Below is a summary of what is told. It is just my notes, so go to the times mentioned to listen to the exact answers. Some details I did not write down, you might think are important, but I did not (or missed as I English is not my mother-tongue).

Continue reading “InsideHPC: SuperComputing. Where to from here?” →

28 June: OpenCL course in Utrecht, NL

Posted by Vincent Hindriksen on

At 28 June 2011 StreamCompting will give a 1-day course on OpenCL in Utrecht. As it is quite new, the priced is reduced. Also if you want to learn CUDA or any other GPGPU-language, this course is also a good option for you. The most important thing about GPGPU are the concepts. In other words the “why” they chose to make GPGPU-languages ike this. In my course you will get it after a one-day training. Most of the day consists of lectures with a short lab-sessions. The training makes use of a unique block-method, so you learn the technique top-down and almost can fill in the spaces yourself. At least 2 years of thorough programming-experience in Java, C++ or Objective C is preferred, because of the level of the subjects. The following is discussed with the big why-question as leading:

[list1]

  • OpenCL debunked: getting to understand how OpenCL is engineered.
  • Algoritms: which can be sped-up with GPGPU/OpenCL and which not.
  • Architectures & Optimalisations: why does one OpenCL-program work better on one architecture and not on another.
  • Software-engineering: wrapper-languages, code re-use and integration in existing software.
  • Debugging: not the screenshots, but giving you insight in how the memory-models work.
[/list1]

The lab-sessions are very minimal; you get (fully documented) homework which you can do the subsequent week (with assistance via mail). If you prefer to have extensive lab-sessions, please inform to the possibilities. After the session and the homework you’ll be able to decide on your own what kind of software can be sped up by using OpenCL and which not. Als you will be able to integrate OpenCL into your own software and engineer OpenCL-kernels. Note that the advances you make depend heavily on your seniority in programming. If all attendees are Dutch, it is given in Dutch. Future sessions will be in other cities, so if you prefer to receive training more local or at your company, please ask for the possibilities.

If you want more information, contact us.

MS just did not port Windows 8 to ARM

Posted by Vincent Hindriksen on

With a lot of fanfare Microsoft said they would offer Windows 8 in both an X86 as an ARM version. I was happy to see that Microsoft was innovating again after 10 years, and even saw loads of advantages of their Java-clone .NET. But then I started to read into Windows CE, Windows Embedded Compact, Windows Embedded Standard, Windows Mobile and Windows 8 (Desktop). I want to share this with you, even if it has not anything to do with OpenCL.

So they did port Windows to ARM which evolved to the 2012 version of the OS, but did not port Windows 8.0 from scratch. Below you can read why.

Continue reading “MS just did not port Windows 8 to ARM” →

AMD OpenCL Presentation as OpenDocument

Posted by Vincent Hindriksen on with 1 Comment

You remember AMD’s OpenCL University Kit? It was for universities and completely written in PPTX. (For people who are on university: PPTX is a undocumented document-form which claims to be open and actually works well with an editor/viewer of only one vendor). So I took the freedom to convert all documents to ODF, so anybody can open them.

Download it here: AMD OpenCL University Kit as ODF.

It has 13 chapters, covering all the basics you need to know for further study. Say “thanks AMD” and enjoy!

StreamHPC’s Newsletter

Posted by Vincent Hindriksen on

stapel_krantenWanting to know what really happens in the world of OpenCL? StreamHPC’s monthly newsletter is the most complete and independent source around the business and techniques around OpenCL. Subscribe, because the written news doesn’t always end up on this blog.

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WebCL – a next step

Posted by Vincent Hindriksen on with 3 Comments

WebGL is already secured to be a success; only IE-users will not have the 3D-web without plugin. But once sites like Wikipedia starts to offer 3D-imagery of the human body and buildings (as we know in Google Earth’s KML-format), things can go really fast in favour of the WebGL-supported browsers. This is important, because the balance between the computers/smartphones and the servers (you know: internet) just got somewhat more connected. I was first somewhat critical, because I want the web to have content (text and images) and not be “an ultimate experience” – luckily it turned out to be good for the content. I’m looking forward to Wikipedia and hardware accelerated services like Streetview!

A possible next step would be WebCL. But is it technically possible? And what would the internet-landscape be to be ready for such thing? Khronos did mention to be working on such technique, according to this article. But not much attention was given to it. So I was happy to see a GSOC11 proposal WebCL-plugin for Firefox by Adrien Plagnol. They even have some code. But it was already finished for Firefox 4 (Windows and Linux), I learnt about a week ago.

WebCL by Nokia

It is very simple: it is a Javascript-version of the host-specific OpenCL code. Kernels are just kernels as we know them.

Nokia has put together a very nice WebCL homepage, which contains tutorials. And at lesson one we see how it looks like:

function detectCL() {
  // First check if the WebCL extension is installed at all

  if (window.WebCL == undefined) {
    alert("Unfortunately your system does not support WebCL. " +
          "Make sure that you have both the OpenCL driver " +
          "and the WebCL browser extension installed.");
    return false;
  }

  // Get a list of available CL platforms, and another list of the
  // available devices on each platform. If there are no platforms,
  // or no available devices on any platform, then we can conclude
  // that WebCL is not available.

  try {
    var platforms = WebCL.getPlatformIDs();
    var devices = [];
    for (var i in platforms) {
      var plat = platforms[i];
      devices[i] = plat.getDeviceIDs(WebCL.CL_DEVICE_TYPE_ALL);
    }
    alert("Excellent! Your system does support WebCL.");
  } catch (e) {
    alert("Unfortunately platform or device inquiry failed.");
  }
}

As you can see this is very understandable code, if you know the basics of OpenCL and JavaScript. It is built for stability, so it seems to crash less easily than I expected.

I’ve written/tweeted a lot about OpenCL wrappers and how I think the OpenCL-ecosphere advances mainly by the growing up of the wrappers. Complaints about the far too long initialisation of OpenCL-software can easily be put in just a few lines of code. We now start from scratch again, but I will not be wonder-struck if there will be a jQuery-plugin released soon.

Needs

In the first place, think real-time encryption which can be adapted per user without the browser knowing. There are many more reasons all going back to the demand to have a browser-based computer (like Google is trying with its ChromeOS). All OS-APIs need to be available in a HTML5-like language and this is exactly that.

What are you still doing here? Install the Opencl-plugin for Firefox 4 and try Nokia’s online OpenCL-sandbox now! +1 for crashing it, +2 for sending in a bug-report.