Tag Archives: OpenCL

Why use OpenCL on FPGAs?

9781118942208.pdfAltera has just released the free ebook FPGAs for dummies. One part of the book is devoted to OpenCL, so we’ll quote some extracts here  from one of the chapters. The rest of the book is worth a read, so if you want to check the rest of the text, just fill in the form on Altera’s webpage.

In Stream Computing we’re interested in OpenCL on FPGAs for one reason: many companies run their software on GPUs, when they should be using FPGAs instead; and at the same time, others stick to FPGAs and ignore GPUs completely. The main reason, we think, is that converting CUDA to VHDL, or Verilog to CPU intrinsics, is simply too painful. Another reason can be seen in the a amount of investment put on a certain technology. We believe that OpenCL can solve both of these issues. OpenCL is much more portable and can be converted to a new architecture in a relatively short time (if the developer is familiar with the project, the hardware and OpenCL). We have high familiarity with these two latter, which means we’re used to get new projects up-and-running.

Since both Altera and Xilinx have invested in OpenCL, the two FPGAs code has become more portable now. Altera has a public SDK (and they’re proudly loud about it), while Xilinx offers it in their latest tools (although they’re unfortunately much more silent about it).

Now, let us now go back to the quotes from the book that we wanted to share with you.

Andrew Moore describes OpenCL effectively in just a few sentences:

The need for heterogeneous computing is leading to new programming languages to exploit the new hardware. One example is the OpenCL first developed by Apple, Inc. OpenCL is a framework for writing programs that execute across heterogeneous platforms consisting of CPUs, GPUs, DSPs, FPGAs, and other types of processors. OpenCL includes a language for developing kernels (functions that execute on hardware devices) as well as application programming interfaces (APIs) that define and control the various platforms. OpenCL allows for parallel computing using task-based and data-based parallelism.

The author also shares some interesting insights around the reasons why OpenCL should be used on FPGA:

FPGAs are inherently parallel, so they’re a perfect fit with OpenCL’s parallel computing capabilities. FPGAs give you an alternative to the typical data or task parallelism by offering a pipeline parallelism where tasks can be spawned in a push-pull configuration with each task using different data from the previous task with or without host interaction. OpenCL allows you to develop your code in the familiar C programming language but using the additional capabilities provided by OpenCL. These kernels can be sent to the FPGAs without your having to learn the low-level HDL coding practices of FPGA designers. Generally, there are several benefits for software developers and system designers to use OpenCL to develop code for FPGAs:

  • Simplicity and ease of development: Most software developers are familiar with the C programming language, but not low-level HDL languages. OpenCL keeps you at a higher level of programming, making your system open to more software developers.
  • Code profiling: Using OpenCL, you can profile your code and determine the performance-sensitive pieces that could be hardware accelerated as kernels in an FPGA.
  • Performance: Performance per watt is the ultimate goal of system design. Using an FPGA, you’re balancing high performance in an energy-efficient solution.
  • Efficiency: The FPGA has a fine-grain parallelism architecture, and by using OpenCL you can generate only the logic you need to deliver one fifth of the power of the hardware alternatives.
  • Heterogeneous systems: With OpenCL, you can develop kernels that target FPGAs, CPUs, GPUs, and DSPs seamlessly to give you a truly heterogeneous system design.
  • Code reuse: The holy grail of software development is achieving code reuse. Code reuse is often an elusive goal for software developers and system designers. OpenCL kernels allow for portable code that you can target for different families and generations of FPGAs from one project to the next, extending the life of your code.

Today, OpenCL is developed and maintained by the technology consortium Khronos Group. Most FPGA manufacturers provide Software Development Kits (SDKs) for OpenCL development on FPGAs.

You can continue here if you want to read of this ebook. And  of course, whenever you want to learn some more more, feel free to write to us, or follow this conversation on Twitter, which goes on through our special account: @OpenCLonFPGAs.

A 2-day OpenCL training on 13+14 October in Amsterdam for €500

openclbooksWhat you’ll get

For €500 you’ll get a two day training on OpenCL in Amsterdam. It handles just the basics on the concepts and start writing your first OpenCL programs. The goal is to take away the first obstacles for writing software for GPUs. You’ll learn unique insights from the StreamComputing team, you won’t read in any book.

The focus is on how to think differently – a CPU is not a GPU, and therefore many CPU-algorithms have to be written differently. Using group discussions, lab-sessions and lectures, we try to get you at the point where you are confident with the new technology.

We also included some basics of C such as how to work with #ifdef, pointers and file-handling. This should help people who have a background in higher level languages like Java, Python or dotNET. We start with simple examples, slowly getting harder and end the two days with an overview of what kind of algorithms can be ported (and what are the limitations) as an open discussion.

We won’t discuss more advanced subjects like OpenCL-on-FPGAs, debugging, OpenGL/DirectX, kernel-encryption, multi-device targeting and advanced algorithm design. Subjects like OpenCL 2.0, CUDA and OpenCL-on-CPUs will be lectured, but not practised. These subjects can be discussed during lunch or if you choose to join the dinner at the second day.

Why the split and affordable price?

During the past trainings we have learned a lot from the feedback. One was that we should split the training, as new concepts always take a while to land – a break would solve this. Another reason was that 4 days is too long to be away from a project.

With the split, we also have the opportunity to target a wider audience for the beginner’s course. To stress this, we lowered the price to €250 a day, including a book (one of the above), a charged travel-card for public transport (worth around €25), lunch, coffee/tea, cookies and fruit like apples and bananas (so you can tell at home you got a healthy training).

This training the base in GPU-computing everybody needs who wants to explore this new area. From there you can continue to learn yourself, take an advanced course from us, or take a follow-up course from another specialist.


Location is 30 minutes from the airport, at Amsterdam Science Park. We try to get a location around 10 minutes from the airport.

You need to bring your own laptop, which has OpenCL running on it – we’ll provide you with information how to test your configuration. You can also rent one from us, for €100.

Apply today

Apply by using the contact form or call to +31854865760 (during West European office hours) or mail to trainings@streamcomputing.eu. To help us better understand your needs, please fill in the pre-training questionnaire.

If you want an in-house training, or want to invite us to your self-organised training anywhere on the world, please contact us.

The 13 application areas where OpenCL can be used

Did you find your specialism in the list? The formula is the easiest introduction to GPGPU I could think of, including the need of auto-tuning.

Which algorithms map is best to GPUs and other vector-processors? In other words: What kind of algorithms are faster when using accelerators and OpenCL?

Professor Wu Feng and his group from VirginiaTech took a close look at which types of algorithms were a good fit for vector-processors. This resulted in a document: “The 13 (computational) dwarfs of OpenCL” (2011). It became an important document here in StreamComputing, as it gave a good starting point for investigating new problem spaces.

The document is inspired by Phil Colella, who identified seven numerical methods that are important for science and engineering. He named “dwarfs” these algorithmic methods. With 6 more application areas in which GPUs and other vector-accelerated processors did well, the list was completed.

As a funny side-note, in Brothers Grimm’s “Snow White” there were 7 dwarfs and in Tolkien’s “The Hobbit” there were 13. Read more …

Verify your OpenCL kernel online for data-races

gpuverifyGPUVerify is a tool for formal analysis of GPU kernels written in OpenCL and CUDA. The tool can prove that kernels are free from certain types of defect, such as data races and bugs. This is quite useful feedback for any GPU-programmer.

Below you find a online version of the tool (please don’t break it!). Play around and test your kernels. Be aware the number of groups is the global worksize divided by local worksize.

For demo-purposes some values have been pre-filled with a simple kernel – press “Check my OpenCL kernel” to find the results. Did you expect this from this kernel? Can you explain the result?

After the LEAP-conference I’ll extend this article – till then I’m too time-limited. For now I wanted to share the online version with you, especially with the people who will attend the tutorial at LEAP. Be sure to check out the GPUVerify website and paper to learn more about this fantastic tool! Read more …

NVIDIA’s Industry-Leading “Support” For OpenCL

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,


William Ramey
Sr. Product Manager, GPU Computing
NVIDIA Corporation

Read more …

Intel OpenCL CPU-drivers 2013 beta with OpenCL 1.2 support

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.

Read more …

NVIDIA: mobile phones, tablets and HPC (cloud)
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

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“.

Read more …

By exception, another PDF-Monday.

OpenCL vs. OpenMP: A Programmability Debate. The one moment OpenCL and the other mom ent OpenMP produces faster code. From the conclusion: “OpenMP is more productive, while OpenCL is portable for a larger class of devices. Performance-wise, we have found a large variety of ratios between the two solutions, depending on the application, dataset sizes, compilers, and architectures.”

Improving Performance of OpenCL on CPUs. Focusing on how to optimise OpenCL. From the abstract: “First, we present a static analysis and an accompanying optimization to exclude code regions from control-flow to data-flow conversion, which is the commonly used technique to leverage vector instruction sets. Second, we present a novel technique to implement barrier synchronization.”

Variants of Mersenne Twister Suitable for Graphic Processors. Source-code at http://www.math.sci.hiroshima-u.ac.jp/~m-mat/MT/MTGP/

Accelerating the FFTD method using SSE and GPUs. “The Finite-Difference Time-Domain (FDTD) method is a computational technique for modelling the behaviour of electromagnetic waves in 3D space”. This is a project-plan, but describes the theories pretty well. Read more …

Basic concepts: Function Qualifiers

Optimisation of one’s thoughts is a complex problem: a lot of interacting processes can be defined, if you think of it.

In the OpenCL-code, you have run-time and compile-time of the C-code. It is very important to make this clear when you talk about compile-time of the kernel as this can be confusing. Compile-time of the kernel is at run-time of the software after the compute-devices have been queried. The OpenCL-compiler can make better optimised code when you give as much information as possible. One of the methods is using Function Qualifiers. A function qualifier is notated as a kernel-attribute:

__kernel __attribute__((qualifier(qualification)))  void foo ( …. ) { …. }

There are three qualifiers described in OpenCL 1.x. Let’s walk through them one by one. You can also read about them here in the official documentation, with more examples.

Read more …

Basic Concepts: online kernel compiling

Typos are a programmers worst nightmare, as they are bad for concentration. The code in your head is not the same as the code on the screen and therefore doesn’t have much to do with the actual problem solving. Code highlighting in the IDE helps, but better is to use the actual OpenCL compiler without running your whole software: an Online OpenCL Compiler. In short is just an OpenCL-program with a variable kernel as input, and thus uses the compilers of Intel, AMD, NVidia or whatever you have installed to try to compile the source. I have found two solutions, which both have to be built from source – so a C-compiler is needed.

  • CLCC. It needs the boost-libraries, cmake and make to build. Works on Windows, OSX and Linux (needs possibly some fixes, see below).
  • OnlineCLC. Needs waf to build. Seems to be Linux-only.

Read more …

Basic Concepts: OpenCL Convenience Methods for Vector Elements and Type Conversions

In the series Basic Concepts I try to give an alternative description to what is said everywhere else. This time my eye fell on alternative convenience methods in two cases which were introduced there to be nice to devs with i.e. C/C++ and/or graphics backgrounds. But I see it explained too often from the convenience functions and giving the “preferred” functions as a sort of bonus which works for the cases the old functions don’t get it done. Below is the other way around and I hope it gives better understanding. I assume you have read another definition, so you see it from another view not for the first time.



Read more …

AMD OpenCL coding competition

The AMD OpenCL coding competition seems to be Windows 7 64bit only. So if you are on another version of Windows, OSX or (like me) on Linux, you are left behind. Of course StreamComputing supports software that just works anywhere (seriously, how hard is that nowadays?), so here are the instructions how to enter the competition when you work with Eclipse CDT. The reason why it only works with 64-bit Windows I don’t really get (but I understood it was a hint).

I focused on Linux, so it might not work with Windows XP or OSX rightaway. With little hacking, I’m sure you can change the instructions to work with i.e. Xcode or any other IDE which can import C++-projects with makefiles. Let me know if it works for you and what you changed.

Read more …

Already the fourth PDF-Monday. It takes quite some time, so I might keep it to 10 in the future – but till then enjoy! Not sure which to read? Pick the first one (for the rest there is not order).

Edit: and the last one, follow me on twitter to see the  PDFs I’m reading. Reason is that hardly anyone clicked on the links to the PDFs.

I would like if you let others know in the comments which PDF you liked a lot.

Adding Physics to Animated Characters with Oriented Particles (Matthias Müller and Nuttapong Chentanez). Discusses how to accelerate movements of pieces of cloth attached to the bodies. Not time to read? There are nice pictures.

John F. Peddy’s analysis on the GPU market.

Hardware/Software Co-Design. Simple Solution to the Matrix Multiplication Problem using CUDA.

CUDA Based Algorithms for Simulating Cardiac Excitation Waves in a Rabbit Ventricle. Bioinformatics.

Real-time implementation of Bayesian models for multimodal perception using CUDA.

GPU performance prediction using parametrized models (Master-thesis by Andreas Resios)

A Parallel Ray Tracing Architecture Suitable for Application-Specific Hardware and GPGPU Implementations (Alexandre S. Nery, Nadia Nedjah, Felipe M.G. Franca, Lech Jozwiak)

Rapid Geocoding of Satellite SAR Images with Refined RPC Model. An ESA-presentation by Lu Zhang, Timo Balz and Mingsheng Liao.

A Parallel Algorithm for Flight Route Planning with CUDA (Master-thesis by Seçkîn Sanci). About the travelling salesman problem and much more.

Color-based High-Speed Recognition of Prints on Extruded Materials. Product-presentation on how to OCR printed text on cables.

Supplementary File of Sequence Homology Search using Fine-Grained Cycle Sharing of Idle GPUs (Fumihiko Ino, Yuma Munekawa, and Kenichi Hagihara). They sped up the BOINC-system (Folding@Home). Bit vague what they want to tell, but maybe you find it interesting.

Parallel Position Weight Matrices Algorithms (Mathieu Giraud, Jean-Stéphane Varré). Bioinformatics, DNA.

GPU-based High Performance Wave Propagation Simulation of Ischemia in Anatomically Detailed Ventricle (Lei Zhang, Changqing Gai, Kuanquan Wang, Weigang Lu, Wangmeng Zuo). Computation in medicine. Ischemia is a restriction in blood supply, generally due to factors in the blood vessels, with resultant damage or dysfunction of tissue

Per-Face Texture Mapping for Realtime Rendering. A Siggraph2011 presentation by Disney and NVidia.

Introduction to Parallel Computing. The CUDA 101 by Victor Eijkhout of University of Texas.

Optimization on the Power Efficiency of GPU and Multicore Processing Element for SIMD Computing. Presentation on what you find out when putting the volt-meter directly on the GPU.

NUDA: Programming Graphics Processors with Extensible Languages. Presentation on NUDA to write less code for GPGPU.

Qt FRAMEWORK: An introduction to a cross platform application and user interface framework. Presentation on the Qt-platform – which has great #OpenCL-support.

Data Assimilation on future computer architectures. The problems projected for 2020.

Current Status of Standards for Augmented Reality (Christine Perey1, Timo Engelke and Carl Reed). not much to do with OpenCL, but tells an interesting purpose for it.

Parallel Computations of Vortex Core Structures in Superconductors (Master-thesis by Niclas E. Wennerdal).

Program the SAME Here and Over There: Data Parallel Programming Models and Intel Many Integrated Core Architecture. Presentation on how to program the Intel MIC.

Large-Scale Chemical Informatics on GPUs (Imran S. Haque, Vijay S. Pande). Book-chapter on the design and optimization of GPU implementations of two popular chemical similarity techniques: Gaussian shape overlay (GSO) and LINGO.

WebGL, WebCL and Beyond! A presentation by Neil Trevett of NVidia/Khronos.

Biomanycores, open-source parallel code for many-core bioinformatics (Mathieu Giraud, Stéphane Janot, Jean-Frédéric Berthelot, Charles Delte, Laetitia Jourdan , Dominique Lavenier , Hélène Touzet, Jean-Stéphane Varré). A short description on the project http://www.biomanycores.org.

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 @StreamComputing.

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 StreamComputing 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 StreamComputing.

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++.

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

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.

Read more …

Is OpenCL coming to Apple iOS?

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.

Read more …

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

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

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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