According to the latest newsletter of the Mont-Blanc Project, it was explained that the GPU on a Samsung Exynos 5 is much faster and greener than its CPU: 3.5 times faster with half the energy. They built a supercomputer using 810 Exynos SoCs, that can deliver a 26 TFLOPS of peak performance. With the upcoming mobile GPUs becoming exponentially faster, they have all the expertise to build an even faster and greener ARM-supercomputer after this.

The Mont-Blanc compute cards deliver considerably higher performance; at 50% lower energy consumption, compared with previous ARM-based developer platforms.

The Mont-Blanc prototype is based on the Samsung Exynos 5 Dual SoC, which integrates a dual-core ARM Cortex-A15 and an on-chip ARM Mali-T604 GPU, and has been featured and market proven in advanced mobile devices. The dual-core ARM Cortex-A15 delivers twice the performance of the quad-core ARM Cortex-A9, used in the previous generation of ARM-based prototype, whilst consuming 20% less energy for the same workload. Furthermore, the on-chip ARM Mali-T604 GPU provides 3.5 times higher performance than the dual-core Cortex-A15, whilst consuming half the energy for the same workload.

Each Mont-Blanc compute card integrates one Samsung Exynos 5 Dual SoC, 4 GB of DDR3-1600 DRAM, a microSD slot for local storage and a 1 GbE NIC, all in an 85x56mm card (3.3×2.2 inches). A single Mont-Blanc blade integrates fifteen Mont-Blanc compute cards and a 1 GbE crossbar switch, which is connected to the rest of the system via two 10 GbE links. Nine Mont-Blanc blades fit into a standard BullX 9-blade INCA chassis. A complete Mont-Blanc rack hosts up to six such chassis, providing a total of 1620 ARM Cortex-A15 cores and 810 on-chip ARM Mali-T604 GPU accelerators, delivering 26 TFLOPS of peak performance.

“We are only scratching the surface of the Mont-Blanc potential”, says Alex Ramirez, coordinator of the Mont-Blanc project. “There is still room for improvement in our OpenCL algorithms, and for optimizations, such as executing on both the CPU and GPU simultaneously, or overlapping MPI communication with computation.”

Continue reading “ARM Mali-T604 GPU has 3.5x more performance than dual core Cortex-A15” →

More and more professional media software now has support for OpenCL. It starts to be a race where you cannot stay behind. If the competitor runs more than twice as fast on the same hardware, then you just can’t say “Sorry, you should buy NVIDIA hardware”. I expected this to happen, but could not tell in what industry they would run fastest. Seems it is fluid dynamics, video-editors and photo-editors.

AMD and Intel mostly have been selected as collaboration partners. Apple has been a main drive, especially with the introduction of their new MAC Pro with two high-end AMD FirePro GPUs.

Sony Catalyst Family

Sony released three new software packages to support video professionals in pre- and post-production.

This new family of products, Catalyst Browse (media management), Catalyst Prepare (video preproduction assistant) and Catalyst Edit (4K and Sony RAW video editing) has OpenCL support from the start.

Colorfront Express Dailies and On-Set Dailies

This software is an on-set dailies processing system (playback and sync, QC, colour grading, audio and metadata management).

The 2014 versions have OpenCL support in their transcoder plugin, Transkoder.

RED REDCINE-X PRO

REDCINE-X is a coloring toolset, integrated timeline, and post effects collection in a professional, flexible environment for your 4K or 5K .R3D files. RED has added support for OpenCL in build 22.

The Foundry Nuke Blink framework

As presented on GPUconf, The Foundry has opened their framework for running OpenCL kernels. It creates OpenCL-kernels (optimised for AMD or NVIDIA) from C++ Blink kernels.

NUKE studio is a node-based VFX, editorial and finishing studio. As with most products on this page, look for the “reel” to get a nice demo of its capabilities.

Magix Hybrid Video Engine

Video Deluxe and Movie Edit both have OpenCL support since 2012, thanks to the new shared video engine.

Adobe CS6 creative suite

Adobe has entered the OpenCL market publicly with . With Premiere Pro (video editing) and Photoshop (photo-editing) two main products with advanced GPU-acceleration via OpenCL.

Video on GPU-effects on Premiere Pro CS5.

FAQ on GPU-acceleration on Photoshop CS6.

Sony Vegas Pro

Vegas Pro is a video editing software package for non-linear editing systems, and has OpenCL support since version 10d. Also in the consumer version (Sony Movie Studio) there is OpenCL-support.

RealFlow Hybrido2 engine

RealFlow is fluid dynamics software and its new engine Hybrido2 has support for OpenCL since this year. And you just have to love their commercial videos.

Autodesk Maya

Maya is a toolsets to help create and maintain the modern, open pipelines you need to address today’s challenging 3D animation, visual effects, game development and post-production projects. Since the 2013 version it is accelerated for physics simulations via Bullet and OpenCL.

ArcSoft SimHD and Sim3D engine

ArcSoft media-engines SimHD and Sim3D have OpenCL support since several years and are used in several of their  products.

BlackMagic Design

BMD has two suites which use OpenCL, Resolve and Fusion. DaVinci was acquired in 2009 and EyeOn in 2014.

(DaVinci) Resolve

Resolve has real-time colour correction thanks to OpenCL.

(Eyeon) Fusion

Fusion is an image compositing software program created by eyeon Software Inc. It is typically used to create visual effects and digital compositing for film, HD and commercials.

It uses OpenCL since version 6.

Roxio Creator Suite

Roxio uses OpenCL for accelerated rendering in their suite. They were one of the first to implement OpenCL – I think already in 2010, before OpenCL was even cool.

Unluckily they don’t have much information – just a mention that they have support.

Apple Final Cut Pro and iMovie

Apple has support in Final Cut Pro X, Motion 5 and Compressor 4.

Also iMovie works a lot faster when you have an OpenCL capable MAC.

Blender Cycles & Bullet

You cannot find any demonstration of new video hardware without Big Bucks Bunny, the short CG movie created with Blender.

It uses OpenCL in two parts: physics simulations (Bullet) and compositor (Cycles).

Side Effects Houdini

Houdini is a procedural node based 3D animation and visual effects tools for film, broadcast, entertainment and visualisation production.

DigitalFilmTools

There is support for OpenCL in zMatte, Composite Suite Pro and Film Stocks since Q4 2013.

zMatte is a keyer for blue and green screen composites. Composite Suite Pro is a collection of visual effects plug-ins. Film Stocks simulates color and black and white still photographic film stocks, motion picture films stocks and historical photographic processes.

OTOY OctaneRender 3

OctaneRender is a GPU-based, real-time 3D, unbiased rendering application. In March 2015 OTOY announced OctaneRender 3, which has full OpenCL support:

OpenCL support: OctaneRender 3 will support the broadest range of processors possible using OpenCL to run on Intel CPUs with support for out-of-core geometry, OpenCL FPGAs and ASICs, and AMD GPUs.

Below is a reel of OcateRender 2 with CUDA. According to OTOY the performance on AMD and NVidia is comparable.

SAM Alchemist XF

Alchemist XF supports format and framerate conversion from SD up to 4K for a wide variety of file formats at high speed.

More?

There is a lot more OpenCL-powered software coming up rapidly (we hear things). But we also missed (or accidentally forgot) software. Please help making this list complete and send us an email.

OpenCL SPIR (Standard Portable Intermediate Representation) is an intermediate representation for OpenCL-code, comparable to LLVM IL and HSAIL. It is a search for what would be a good representation, such that parallel software runs well on all kinds of accelerators. LLVM IL is too general, but SPIR is a subset of it. I’ll discuss HSAIL, on where it differs from SPIR – I thought SPIR was a better way to start introducing these. In my next article I’d like to give you an overview of the whole ecosphere around OpenCL (including SPIR and HSAIL), to give you an understanding what it all means and where we’re going to, and why.

Know that the new SPIR-V is something completely different in implementation, and we are only discussing the old SPIR here.

[raw]

__kernel void sum(const int size, __global float * vec1, __global float * vec2){
  int ii = get_global_id(0);

  if(ii < size) vec2[ii] += vec1[ii];

}

[/raw]

Just to let you know that there should be even more OpenCL and related technologies on SC14

Are you interested in hosting a workshop at SC14?

Please mark your calendars as SC will be accepting proposals from 1 January – 7 February for independently planned full-, half-, or multi-day workshops.

Workshops complement the overall SC technical program. The goal is to expand the knowledge base of practitioners and researchers in a particular subject area providing a focused, in-depth venue for presentations, discussion and interaction. workshop proposals will be peer-reviewed academically with a focus on submissions that wil inspire deep and interactive dialogue in topics of interest to the HPC community.

For more information, please consult: http://sc14.supercomputing.org/program/workshops

Important Submission Info

Web Submissions Open: 1 January 2014
Submission Deadline: 7 February 2014
Notification of acceptance: 28 March 2014

Submit proposals via: https://submissions.supercomputing.org/
Questions: workshops@info.supercomputing.org

We’re thinking of proposing one too. Want to collaborate? Mail us! Don’t forget, to go to HiPEAC (20 January) and IWOCL (12 May) to meet us!

Last week I needed to get Fortran working with OpenCL. As the example-page is not up-to-date and not much documentation is on the interwebs outside the official page, this was not as straight-forward as I hoped. The test-suite and this article provided code I could actually use. First I wanted to have things in a module, second I needed to control which device I wanted to use, third I needed function-names that could be used in a larger project. The result is below, and hopefully usable for the Fortran folks around who want to add some OpenCL-kernels to their existing code.

It uses the two-step initialisation we know from C, for safe memory allocation. It is based on the utils.f90 from the test-suite.

The only good way to translate is the Rose-compiler – which is a pain to install. I tried various f2c-scripts (from the 90’s, but they all failed. I must say that continuous switching between Fortran-mode and C-mode was the hardest part of the porting.

If you have tips&tricks to use OpenCL from Fortran, let everybody know in the comments. Also let me know if the code doesn’t work for you, or you have improvements (like better error-handling).

The rest of utils.f90 (which I renamed to clutils.f90 for better integration) is mostly the same – only this subroutine needed changes:

(...)

subroutine cl_initialize(platform_id, device_id, device, context, command_queue)
!use ISO_C_BINDING
type(cl_device_id),     intent(out)     :: device
type(cl_context),       intent(out)     :: context
type(cl_command_queue), intent(out)     :: command_queue
integer                                 :: platform_id
integer                                 :: device_id

integer :: platform_count, device_count, ierr
character(len = 100) :: info
type(cl_platform_id) :: platform
type(cl_platform_id), allocatable, target :: platform_ids(:)
type(cl_device_id), allocatable, target :: device_ids(:)

! get the platform ID
call clGetPlatformIDs(platform_count, ierr)
if(ierr /= CL_SUCCESS) call error_exit('Cannot get CL platform.')
allocate(platform_ids(platform_count))
call clGetPlatformIDs(platform_ids, platform_count, ierr)
if(ierr /= CL_SUCCESS) call error_exit('Cannot get CL platform.')

if (platform_id .gt. platform_count .or. platform_id .lt. 1) platform_id = 0
platform = platform_ids(platform_id)

! get the device ID
call clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, device_count, ierr)
if(ierr /= CL_SUCCESS) call error_exit('Cannot get CL device.')
allocate(device_ids(device_count))
call clGetDeviceIDs(platform, CL_DEVICE_TYPE_ALL, device_ids, device_count, ierr)
if(ierr /= CL_SUCCESS) call error_exit('Cannot get CL device.')

if (device_id .gt. device_count .or. device_id .lt. 1) device_id = 1
device = device_ids(device_id)

! get the device name and print it
call clGetDeviceInfo(device, CL_DEVICE_NAME, info, ierr)
print*, "CL device: ", info

! create the context and the command queue
context = clCreateContext(platform, device, ierr)
command_queue = clCreateCommandQueue(context, device, CL_QUEUE_PROFILING_ENABLE, ierr)

end subroutine cl_initialize

(...)

Continue reading “FortranCL working example” →

Khronos has supported and organised for the second time the International Workgroup on OpenCL (IWOCL, pronounced as “eye-wok-ul”). Last year the event took place at Georgia Tech, Atlanta, Georgia, in the United States. This year the event will be held in Europe: Bristol University, Bristol, England, UK.

IWOCL 2013 Presentations

Last year there was a varying programme:

• Porting a Commercial Application to OpenCL: A Case Study
• Demonstrating Performance Portability of a Custom OpenCL Data Mining Application to the Intel Xeon Phi Coprocessor
• Parallelization of the Shortest Path Graph Kernel on the GPU
• OpenCL-based Approach to Heterogeneous Parallel TSP Optimization
• clMAGMA: High Performance Dense Linear Algebra with OpenCL
• Multi-Architecture ISA-Level Simulation of OpenCL
• Optimizing OpenCL Applications on the Intel Xeon Phi

You can see and download these presentations here. This year the organisation tries to offer a equally exciting programme.

Workshop means it’s an active event

It’s all about sharing, but not just by letting you sit and listen. Below you’ll find some of the options.

Present your work

Did you use OpenCL in your software or research? You are very welcome to present your experience and results. IWOCL is the premier forum for the presentation and discussion of new designs, trends, algorithms, programming models, software, tools and ideas for OpenCL.

Abstract Submission Deadline: Friday 31 January, 2014

It can be in the form of:

• Research paper
• Technical presentation
• Workshops and Tutorial
• Poster

(StreamHPC’s Vincent Hindriksen is on the Conference Sessions Committee)

Communicate with the workgroup

The OpenCL workgroup likes to communicate with OpenCL’s users. IWOCL provides a formal channel for community feedback to the Khronos Group’s OpenCL workgroup. This is one of the best moments to be heard, discuss a hack/bug or share a great idea that should be in the next version of OpenCL.

Meet OpenCL developers and enthusiasts

During the breaks, social events and during presentations, you can discuss all your ideas and thoughts on on-topic and off-topic subjects, or you can also join existing talks.

If you are new into compute acceleration, you’ll find many people who are willing to explain what it does and add their personal view.

Test-drive software

We will bring some hardware, on which you can test your kernels. (We’ll put more info about this later!)

Sponsor and present your product

There will be booths available for the sponsors, where you can show your product to the public.

Stay up to date on the event

We will try keep you up-to-date as much as possible, but IWOCL has some channels to keep you informed:

• Blog
• Twitter
• Homepage
• Newsletter

We’ll put on a link when tickets are ready to be sold.

Let others know you plan to be on the event by saying hi in the comments.

Hope to see you there!

When the new supercomputer “Cartesius” of the Netherlands was presented to the public a few months ago, the buzz was not around FLOPS, but around users. SARA CEO Dr. Ir. Anwar Osseyran kept focusing on this aspect. The design of the machine was not pushed by getting into the TOP500, but by improving the performance of the current users’ software. This was applauded by various HPC experts, including StreamHPC. We need to get things done, not to win a virtual race of some number.

In the description about the supercomputer, the top500-position was only mentioned at the bottom of the page:

Cartesius entered the Top500 in November 2013 at position 184. This Top500 entry only involves the thin nodes resulting in a Linpack performance (Rmax) of 222.7 Tflop/s. Please note that Cartesius is designed to be a well balanced system instead of being a Top500 killer. This is to ensure maximum usability for the Dutch scientific community.

What would happen if you go for a TOP500 supercomputer? You might get a high energy bill and an overpriced, inefficient supercomputer. The first months you will not have full usage of the machine, and you won’t be able to easily turn off some parts, hence the spill of electricity. This results, finally, in that it is better to run unoptimized code on the cluster than to take time for coding.

The inefficiency is due to the fact that some software is data-transfer limited and other is compute-limited. No need to explain that if you go for a Top 500 and not for software optimized design, you end up buying extra hardware to get all kinds of algorithms performing. Cartesius therefore has “fat nodes” and “light nodes” to get the best bang per buck.

There is also a plan for expanding the machine over the years (on-demand growth), such that the users will remain happy instead of having an adrenaline-shot at once.

The rat-race

The HPC Top 500 is run by the company behind ISC-events. They care about their list being used, not if there is Exascale now or later. There is one company who has a particular interest in Exascale: Intel and IBM. It hardly matters anymore how it begun. What is interesting is that Intel has bought Infiniband and is collecting companies that could make them the one-stop shop for a HPC-cluster. IBM has always been strong in supercomputers with their BlueGene HPC-line. Intel has a very nice infographic on Intel+Exascale, which shows how serious they are.

But then the big question comes: did all this pushing speed up the road to Exascale? Well, no… just the normal peaks and lows round the logarithmic theoretic line:

What I find interesting in this graph is that the #500 line is diverging from the #1 line. With GPGPU is would was quite easy to enter the top 500 3 years ago.

Did the profits rise? Yes. While PC-sales went down, HPC-revenues grew:

Revenues in the high-performance computing (HPC) server space jumped 7.7 percent last year to $11.1 billion surpassing the $10.3 billion in revenues generated in 2011, according to numbers released by IDC March 21. This came despite a 6.8 percent drop in shipments, an indication of the growing average selling prices in the space, the analysts said. (eWeek.)

So, mainly the willingness of buying HPC has increased. And you cannot stay behind when the rest of the world is focusing on Exascale, can you?

Read more

• Five HPC Pitfalls – benchmarks, hardware generalisation, open software, system integration and storage aspects
• How to sell performance computing
• Green 500 – where FLOPS/Watt are important, not peak-performance.
• The 13 application areas where GPUs perform best.
• Chinese supercomputer tops the charts — two years early

Keep your feet on the ground and focus on what matters: papers and answers to hard questions.

Did you solve a compute problem and got published with an sub-top250 supercomputer? Share it in the comments!

During the last training I got a question how to do malloc in the kernel. It was one of those good questions, as it gives another view on a basic concept of OpenCL. Simply put: you cannot allocate (local or global) memory from within the kernel. Luckily it’s possible, but it is somewhat hidden in another function.

clSetKernelArg to the rescue

The way to do it is from the host, using one of the kernel arguments.

cl_int clSetKernelArg (	cl_kernel kernel,
	cl_uint arg_index,
	size_t arg_size,
	const void *arg_value)

This function allocates the memory on the device for you. Just as with normal malloc, it doesn’t clear the memory for you.

To make sure the host cannot access it (and you don’t accidentally pin/write/read it, when using host-generation scripts), you can use a flag for that: CL_MEM_HOST_NO_ACCESS. All the flags have been explained in a previous article about this same function, setting flags for creating kernel arguments.

The advantage of only allowing malloc to be done from the host, before the kernel is launched, is that the memory-planning can be done more efficiently.

Local memories

When you need a local space, you can specify that at the kernel-side. For example:

__kernel void foo(__local int* bar) { ... }

This mallocs an area in all local memories with size specified by arg_size.

Basic Concepts

This short article is in the basic concept series. It contains several subjects I did not see well-enough explained in books or the reference manual. If you see a subject that you would like to see in this series, just contact us.

For those working in a research-department at a company or university within the EU, Horizon 2020 might be a bit of a familiar sound.

For us this is an important program and a source possibilities for collaboration in the coming years. Our expertise in enabling ultra-fast computations, combined with your expertise can make Europe more competitive. We are interested in applied GPGPU, in the commercialization of tools, in co-developing new software with SMEs and also in universities based on the EU, Switzerland or Israel.

Fields Europe wants to focus on:

• Micro- and nano-electronics; photonics
• Nanotechnologies
• Advanced materials
• Biotechnology
• Advanced manufacturing  and processing

The development of these technologies requires a multi-disciplinary knowledge and a capital-intensive approach.

Each of these industries has opportunities for using GPus and Accelerators.

Contact us today for more information. We’ll have a lot to share!

Events are organised throughout Europe to inform universities and companies about the programme. Are you a Dutch university or company? Check this site of the Dutch government.

Every second Monday of the month StreamHPC offers an OpenCL training in Mathematics or Media-operations. The target is OpenCL 1.2 (or 1.1 when NVIDIA is discussed). OpenCL 2.0 trainings will start in Q2/Q3, or when on request. All trainings will be given by experienced OpenCL developers/trainers.

Trainings

Trainings take 3½ days, from basics on the first morning to the special requests on the 4th morning, either with your own laptop, or logging to a compute-server.

The Media-operations module is based on Heterogeneous Computing with OpenCL, second edition by Benedict Gaster, Lee Howes, David R. Kaeli, Perhaad Mistry & Dana Schaa.

It covers convolution, video-processing, histogram and mixed particle simulation. Extra subjects are OpenCL-OpenGL interop and code-optimisation.

A good fit if you work with images, sound and video.

The Mathematics module is based on  OpenCL in Action  by Matthew Scarpino.

It covers reduction, sorting, matrix-operations and signal processing.

If you work on graphs, matrices and data-manipulation, this is for you!

Continue reading “StreamHPC launches monthly trainings in Europe” →

Unluckily I am not at SC13, so I’ll enjoy from a distance. Luckily I don’t miss one of the most beautiful 15km runs in the Netherlands. When there is more news, I’ll add to this post – below is mostly taken from the Khronos website and the SC2013 website.

OpenCL Booth

Meet members of the OpenCL workgroup in Booth #4137 to get hot news from the OpenCL experts and an OpenCL reference card. Also learn about next year’s plans for IWOCL (International Workshop on OpenCL). Be sure not to miss the BOF on OpenCL 2.0 (in bold in the schedule).

Schedule

There are other interesting SC13-events around OpenCL, so be sure to check the schedule carefully.

Khronos Members Exhibiting at SC13

Complete floor plan is available here.

Below links are updated from the company-homepages to special SC13 landing-pages.

• Altera Corporation – Booth 4332.
  • BittWare will be showcasing their TeraBox High Performance Reconfigurable Computing Platform, which runs OpenCL.
  • Acceleware. See schedule above.
• AMD – Booth 1113. See this list for a schedule.
• ARM – Booth 3141.
  • OpenCL on MALI demos at their booth
  • AccelerEyes (#310) showcases ArrayFire (with OpenCL-on-ARM backend) running on Mali T604.
  • Many more – just look for it.
• Khronos OpenCL – Booth 4137.
  
• IBM – Booth 126, 2713. OpenCL on IBM PowerLinux 7R2 and IBM Flex System. Also collaboration with Altera.
• Intel – Booth 2501, 2701. OpenCL on their CPUs and GPUs.
• NEC Corporation – 3109. Vector supercomputer – Khronos probably hints to OpenCL running on this machine – see for yourself.
• NVIDIA – Booth 613. Have fun hearing them say: “Do you use CUDA or are you locked-in to OpenCL?” and variations on this.
• Texas Instruments – Booth 3725. OpenCL on DSP demo.
• XilinX – To schedule a private appointment (for an OpenCL-demo) visit Xilinx at the Convey booth (#3547) or the Alpha Data booth (#4237).

The floorplan can be downloaded here or here (mirrored on 15-Nov).

At SC13 and saw great OpenCL demos or news?

Share this info and photos in the comments, for others to pick up.

Let the competition on large memory GPUs begin!

Some algorithms and continuous batch processes will have the joy of the extra memory. For example when inverting a large matrix or doing huge simulations, you need as much memory as possible. or to avoid memory-bank conflicts by duplicating data-objects (possible only when the data is in memory for a longer time to pay for the time it costs to duplicate the data).

Another reason for larger memories is dual precision computations (this one has a total of 1.48 TFLOPS), which doubles memory-requirements. With Accelerators getting better fit for HPC (true support for IEEE-754 double precision storage format, ECC-memory), memory-size becomes one of limits that needs to be solved.

The other choice is swapping on GPUs or to use multi-core CPUs. Swapping is not an option as it nulls all the speed-up. A server with 4 x 16-core CPUs are as expensive as one Accelerator, but use more energy.

AMD seems to have identified this as an important HPC-market therefore just announced the new S10000 with 12GB of memory. To be mailed at AMD-partners in January, and on the market in April. Is AMD finally taking the professional HPC market serious? They now do have the first 12GB GPU-accelerator built for servers.

Old vs New

Still a few question-marks, unfortunately

The biggest differences are the doubling of memory and the passive cooling.

Competitors

Biggest competitor is the Quadro K6000, which I haven’t discussed at all. That card throws out 5.2 TFLOPS using one GPU, being able to access all 12GB of memory via a 384-bit bus at 288 GB/s (when all cores are used). It is actively cooled, so it’s not really fit for servers (like the S10000, 6GB version). The S10000 has a higher bandwidth, but cannot access only half the 12GB from one core at full speed. So the K6000 has the advantage here.

Intel is planning to have 12GB and 16GB XeonPhi’s. I’m curious to more benchmarks of the new cards, as the 5110P does not have very good results (benchmark 1, benchmark 2). It compares more to a high-end Xeon CPU than a GPU. I am more enthusiastic about the OpenCL-performance on their CPUs.

What’s next on this path?

A few questions I asked myself and tried to find answers on.

Extendible memory, like we have for CPUs? Probably not, as GDDR5 is not designed to be upgradable.

Unified memory for multi-GPUs? This would solve the disadvantage of multi-die GPU-cards, as 2, 4 or more GPUs could share the same memory. A reason to watch HSA hUMA‘s progress, which now specifies unified memory access between GPU and CPU.

24GB of memory or more? I’ve found below graph to have an idea of the costs of GDDR-memory, so it’s an option. These prices are of course excluding supplementary parts and R&D-costs for getting more memory accessible to the GPU-cores.

At least the question we are going to get answered now: is the market which needs this amount of memory large enough and thus worth serving.

Is there more need for wider memory-bus? Remember that GDDR6 is promised for 2014.

What do you think of a 12GB GPU? Do you think this is the path that distinguishes professional GPUs from desktop-GPUs?

I’d like to share two images.

The following image is being shared for quite some time, to show the technical capabilities of CUDA.

I replaced “CUDA source” by “OpenCL source” and worked from there. Result:

I know it is not optimised for a certain architecture, but neither is the CUDA-source for the two extra targets.

Upcoming soon is an article, where the real stuff is currently happening: the higher-level languages being built on top of OpenCL.

OpenCL on ARM is hot, but it just is getting started. Currently it takes some time to find needed information about the processors concerning

For OpenCL-discussions the best place is the Khronos OpenCL board. So where can you go when you want to ask questions specifically on ARM-based GPUS like MALI, PowerVR, Adreno and Vivante?

ARM’s new community site for all

ARM just launched the Connected Community (ARM CC). It is the place to connect to, when you have general information-needs of ARM-IP, such as ARM MALI, Cortex A9 and Cortex A15.

And here is how ARM themselves explains this initiative on one slide:

Be sure to connect to StreamHPC. We hope this will indeed be the central place for the whole ecosystem, including Imagination, Qualcomm and Vivante.

ARM MALI

The MALI Developer Center has its forums on ARM Connected Community.

Imagination PowerVR

The graphics-section of their developer forums seems to be the best place.

(Not @ ARM CC)

Qualcomm Adreno

Qualcomm has dev-forums too and has a section called Mobile Gaming & Graphics Optimization (Adreno™).

(Not @ ARM CC)

Vivante

Vivante does not have a forum, but Freescale does. The i.MX forums seem to be the best place to ask your questions.

@ARM CC

Others

Where do find a good source to find and share interesting information on mobile GPUs? Share it with the others via the comments – chances increase your questions gets answered when more people visit the forums.

Guest-blogger Jaap van de Loosdrecht wants to share his thesis with you. He leads the Centre of Expertise in Computer Vision department at NHL University of applied sciences and is the owner of his own company, and still managed to study and write a MSc-thesis. The thesis is interesting because it extensively compares OpenCL with OpenMP, especially chapters 7 an 8.

For those who are interested, my thesis “Acceleration sequential machine vision algorithms using commodity parallel hardware” is available at www.vdlmv.nl/thesis.

Keywords: Computer Vision, Image processing, Parallel programming, Multi-core CPU, GPU, C++, OpenMP, OpenCL.

Many other related research projects have considered using one domain specific algorithm to compare the best sequential implementation with the best parallel implementation on a specific hardware platform. This work was distinctive because it investigated how to speed up a whole library by parallelizing the algorithms in an economical way and execute them on multiple platforms.This work has:

• Examined, compared and evaluated 22 programming languages and environments for parallel computing on multi-core CPUs and GPUs.
• Chosen to use OpenMP as the standard for multi-core CPU programming and OpenCL for GPU programming.
• Re-implemented a number of standard and well-known algorithms in Computer Vision using both standards.
• Tested the performance of the implemented parallel algorithms and compared the performance to the sequential implementations of the commercially available software package VisionLab.
• Evaluated the test results with a view to assessing:
  • Appropriateness of multi-core CPU and GPU architectures in Computer Vision.
  • Benefits and costs of parallel approaches to implementation of Computer Vision algorithms.

Using OpenMP it was demonstrated that many algorithms of a library could be parallelized in an economical way and that adequate speedups were achieved on two multi-core CPU platforms. With a considerable amount of extra effort, OpenCL was used to achieve much higher speedups for specific algorithms on dedicated GPUs.

At the end of the project, the choice of standards was re-evaluated including newly emerged ones. Recommendations are given for using standards in the future, and for future research and development.

Algorithmic improvements are suggested for Convolution and Connect Component Labelling.

Your feedback and/or questions are welcome.

If you put comments here, I’ll make sure Jaap van de Loosdrecht will get to know and answer your questions on the subjects discussed in his thesis.

In the below list are the members of the OpenCL workgroup as of November 2013.

We can expect small changes each year, but this is close to the actual state. I need the rest of Q4 to finalise all the info – any help is appreciated.

This list has also been compiled in 2010, and you can see several differences. If the company has an SDK available, there is a link. That is a whole difference with the last list – this one is much more concrete. Continue reading “All the members of the OpenCL working group 2013” →

Something that creates extra value for Open CL is the flexibility with which it runs on an important variety of hardware. A famous strategy is running the code on CPUs to find data-races and debug the code more easily. Another is to develop on GPUs and port to FPGAs to reduce the development-cycles.

But there’s one, quite important, often forgotten: replacement of faulty hardware. You can blame the supplier, or even Murphy if you want, but what is almost certain is that there’s a high chance of facing downtime precisely when the hardware cannot be replaced right-away.

Fail to plan is planning to fail

To limit downtime, there are a few options:

• Have a good SLA in place for 24/7 hardware-replacement.
• Have spare-hardware in stock.
• Have over-capacity on your compute-servers.

But the problem is that all three are expensive in some form if you’re not flexible enough. If you use professional accelerators like Intel XeonPhi, NVidia Tesla or AMD FirePro, you risk having unexpected stock shortage at your supplier.

With OpenCL the hardware can be replaced by any accelerator, whereas with vendor-specific solutions this is not possible.

Flexibility by OpenCL

I’d like to share with you one example how to introduce flexibility in your hardware-management, but there are various others which are more tailored to your requirements.

To detect faulty hardware, you can think of a server with three GPUs and let selected jobs be run by all three – any hardware-problem will be detected and pin-pointed. Administrating which hardware has done which job completes the mechanism. Exactly this can be used to replace faulty hardware with any accelerator: let the replacement-accelerator run the same jobs as the other two as an acceptance-test.

If you need your software to be optimised for several accelerators, you’re in the right place. We can help you with both machine and hand optimizations. That’s a plan that cannot fail!