Academic hackatons for Nvidia GPUs

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Are you working with Nvidia GPUs in your research and wish Nvidia would support you as they used to 5 years ago? This is now done with hackatons, where you get one full week of support, to get your GPU-code improved and your CPU-code ported. Still you have to do it yourself, so it’s not comparable to services we provide.

To start, get your team on a decision to do this. It takes preparation and a clear formulation of what your goals are.

When and where?

It’s already April, so some hackatons have already taken place. For 2019, these are left where you can work on any language, from OpenMP to OpenCL and from OpenACC to CUDA. Python + CUDA-libraries is also no problem, as long as the focus is Nvidia.

Continue reading “Academic hackatons for Nvidia GPUs”

IWOCL 2019

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On Monday May 13, 2019 at 09:30 the latest edition of IWOCL starts, not taking into account any pre-events that might be spontaneously organized. This is the biggest OpenCL-focused event that discusses everything that would make any GPGPU-programmer, DSP-programmer and FPGA-programmer enthusiastic.

What’s new since last year, is that it’s actually also more interesting place for CUDA-developers who like to learn and discuss new GPU-programming techniques. This is because Nvidia’s GTC has moved more to AI, where it used to be mostly GPGPU for years.

Since it’s now the last week of the early-bird pricing, it’s a good time to make you think about buying your ticket and book the trip.

Where

Last year was in Europe, and this year’s edition is back in the USA. Northeastern University is hosting the conference spanning three days. It includes tutorials, many speakers and an always entertaining social event. For those who fly from Europe, look what https://flysmarter.de/ is suggesting, as they have some surprising suggestions for long flights. Another one that works for me well is https://skiplagged.com/flights/ams/bos/2019-05-12 that is quite strong with flights within the USA. Please share in the comments what you suggest other IWOCL-travellers!

The talks

The talks are very diverse, as are the speakers. Personally I’ve gotten some very good insights from IWOCL talks.

  • Profiling OpenCL Kernels Using Wavefront Occupancy with Radeon GPU Profiler
  • Advances in the OpenCL Offload Support in GROMACS
  • Comparative Performance Analysis of Vulkan Implementations of Computational Applications
  • Developing Performance-Portable OpenCL Code via Multi-Dimensional Homomorphisms
  • Evaluating Portability and Performance of OpenCL FPGA Kernels on Intel HARPv2
  • Khronos Update – OpenCL, SYCL and SPIR – The Next Steps
  • The Landscape of C++ Heterogeneous Computing and Safety Critical API
  • Khronos Panel Discussion
  • Blurring the Boundary between CPU and GPU
  • Accelerated Neural Networks on OpenCL Devices Using SYCL-DNN
  • How to Deploy AI Software to Self Driving Cars
  • Breaking the Last Line of Performance Border
  • Performance Evaluation of OpenCL Standard Support (and Beyond)
  • OpenCL vs: Accelerated Finite-Difference Digital Synthesis
  • The Challenge of Targeting Scratch-pad Memory Devices with OpenCL
  • Exploring Integer Sum Reduction using Atomics on Intel CPU
  • MGSim: a Flexible High-Performance Simulator for Multi-GPU Systems

One thing they all have in common: they are about sharing practical experience and giving useful technical insights. More detailed information can be found on the IWOCL program page.

The tutorials

The first day is full of tutorials

  • Advanced Hands-On-OpenCL: don’t know the difference between OpenGL and OpenCL? Start here.
  • Optimizing OpenCL for Intel FPGAs: for those who want to apply their OpenCL knowledge to FPGAs
  • DHPCC++ 2019: a mini-conference on how C++ in the future (and SYCL now) can be used to program GPUs.

The meeting others

Probably the most interesting thing in this conference is meeting others. You’ll find writers of books (be sure to get your copy of “Heterogeneous Computing with OpenCL 2.0” signed, or buy it at the conference), makers of the tools who can answer why that one feature is not in there and very experienced developers who like to learn from you as much as you would like to learn from them.

Want to know more? Go to the IWOCL homepage now.

Buy tickets

Use the below EventBrite ticket selection or go the IWOCL-page.


Interest rates for GBP:

Question: do we work with CUDA?

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Answer: Yes, actually a lot!

The company was built on OpenCL and we are still work with the language a lot – from embedded GPUs and FPGAs to high-end GPUs. Like OpenCL unjustly isn’t associated with clusters full of professional GPUs, we were not associated with CUDA. I can tell many of our customers have found us to build high performance software in CUDA.

Breaking with the past is not easy due to associations that seem to stick. With the name change from StreamComputing to Stream HPC some years ago, we wanted to enforce that break with being “the OpenCL company”. For some time we were much more pragmatic in solving the problems of our customers, which resulted in making software in MPI and CUDA – sometimes an unexpected direction as the customer initially chose OpenCL.

We also started hiring people who only knew CUDA (but expect them to learn OpenCL), as the right algorithm and the right processor is more important. Internships with CUDA, large CUDA-projects, seeking better relations with Nvidia and such – all have been going on for years. And we like it as much as we like OpenCL – both have unique advantages.

So if you have questions about CUDA, don’t be afraid that you hurt us – we’re happy to help you get fast software.

The 12 latest Twitter Poll Results of 2018

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Via our Twitter channel we have various polls. Not always have we shared the full background of these polls, so we’ve taken the polls of the past half year and put them here. The first half of the year there were no polls, in case you wanted to know.

As inclusive polls are not focused (and thus difficult to answer), most polls are incomplete by design. Still insights can be given. Or comments given.

Below’s polls have given us insight and we hope they give you insights too how our industry is developing. It’s sorted on date from oldest first.

It was very interesting that the percentage of votes per choice did not change much after 30 votes. Even when it was retweeted by a large account, opinions had the same distribution.

Is HIP (a clone of CUDA) an option?

Continue reading “The 12 latest Twitter Poll Results of 2018”

We don’t work for the war-industry

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Last week we emphasized that we don’t work for the war-industry. We did talk to a national army some years ago, but even though the project never started, we would have probably said no. Recently we got a new request, got uncomfortable and did not send a quote for the training.

This is because we like to think about the next 100 years, and investment in weapons is not something that would solve things for the long term.

To those, who liked the tweet or wanted to, thank you for your support to show us we’re not standing alone here. Continue reading “We don’t work for the war-industry”

OpenCL Basics: Running multiple kernels in OpenCL

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This series “Basic concepts” is based on GPGPU-questions we get via email more than once, or when the question is not clearly explained in the books. For one it is obvious, for the other just what they’re missing.

They say that learning a new technique is best done by playing around with working code and then try to combine it. The idea is that when you have Stackoverflowed and Githubed code together, you’ve created so many bugs by design that you’ll learn a lot if you make it work. When applying this to OpenCL, you quickly get to a situation that you want to run one.cl file and then another.cl file. Almost all beginner’s material discuss a single OpenCL-file, so how to do this elegantly?

Continue reading “OpenCL Basics: Running multiple kernels in OpenCL”

Start your GPU-career here

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GPUs have been our mysterious friends and known enemies for years, as they let us run code in expected and unexpected ways. GPUs have solved problems for many of our customers. GPUs have such a high rate of evolvement, that they’ll remain important for the years to come.

Problem is that programming GPUs is not an easy task. Where do you learn to program GPUs? We found these to be the main groups:

  • Universities
  • Research centers
  • GPU vendors (AMD, Nvidia, Intel, Qualcomm, ARM)
  • Self-study

This is far from enough. Add to that, that only a very select group learns the craft at a company. We’d like to change that, and we think now is the time for us to be able to deliver on this.

In January we’ll our internal training program will start with 4 to 8 developers. Focus in on fully understanding recent GPU-architectures, CUDA and OpenCL. It will consist of lectures, workshops, discussions, paper reading and ofcourse coding for one month. The months after that will have guidance, paper presentations, code reviews and time for self-study. The exact form will differ per person.

The hard side

The current measurable requirements are:

  • EU citizen or already having a working permit
  • Great at C/C++
  • High interest in algorithmic optimisations
  • Any performance improvement focus (i.e. Assembly, clean code) is a plus
  • Any GPU experience (i.e. OpenGL, DirectX, self-study) is a plus
  • High interest in performance
  • Willing to move to Amsterdam
  • Willing to work for Stream HPC for at least 2 years

The soft side

We’re looking for people that fit our culture and we think we can train. This means that the selection is based for a large part on “the spark”. Therefore the application starts with a speed date, and we’re sorry for not finding a better wording for this. This is a 20 minute discussion about what we like and what we don’t. This can be done via phone, Skype or in person, during the evening, in the weekends or during your lunch break.

How to apply

Read about our company culture. Look at the jobs we have open. These describe the requirements after the training. Then write us a motivational letter: explain us why this is exactly what you want, why you’re capable and why you’re a cultural fit. If you find it hard to write such letter, then just start with answering the list of requirements. It’s a big bonus to share code (Github, Gitlab, zip-file). Send your email to jobs@streamhpc.com

Other jobs

Feeling more senior? We have other jobs:

    What does it mean to work at Stream HPC?

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    High performance computing on many-core environments and low-level optimisations are very important concepts in large scientific projects nowadays. Stream HPC is one of the market’s more prominent clubs and is substantially expanding. As we often get asked how it is to work at the company, we’d like to give you a little peak into our kitchen.

    Stream HPC’s DNA

    To understand our DNA, you’d need to know how the company got started. In 2010 the company was born from the deep boredom that was born from within the corporate IT workspace. Stream’s founder Vincent Hindriksen had to maintain a piece of software that was often failing to process the daily reports. After documenting the internals and algorithms of the code by interviewing the key people and some reverse engineering, it was a lot easier to create effective solutions for the bugs within the software. After fixing a handful of bugs, there was simply a lot less to do except reading books and playing online games.

    To avoid becoming a master in Sudoku, he spent the following three weeks in rewriting all the code, using the freshly produced documentation. 2.5 hours needed to process the data was reduced to 19 seconds – yes, the kick for performance optimisation was already there. For some reason it took well over 6 months to port the proof-of-concept, which was simply unbearable as somebody had to make sure the old code was maintained for 40 hours a week.

    The reason to start the company was simple: to make intelligent use of time and provide software that is engineered for performance and maintainability. Lots of exciting projects for fantastic clients followed in the next 8 years that allowed us to broaden our expertise and build up confidence. GPUs were there at just the right time – without GPUs it would have probably been performance engineering on CPUs.

    Continue reading “What does it mean to work at Stream HPC?”

    Meet Vincent in Bay Area between 11 and 16 August

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    Our managing director, Vincent Hindriksen, is in San Francisco’s Bay Area from Saturday 11th up to Thursday 16th of August 2018. He’ll be visiting existing customers, but there is time left.

    Current schedule (excluding several unconfirmed meetings):

    • Saturday: social meetups
    • Monday: full
    • Tuesday: all day good availability,
    • Wednesday: all day good availability
    • Thursday: morning good availability

    Do you want to learn more about GPUs and how we can help you get there? Get in touch via our contact-page, and tell us address and time when you want to meet.

    If you seek a job in GPUs, also get in contact! Stream HPC is growing quickly now, and a good moment to onboard and still make a difference. For job-talks also the evenings are available.

    Help us find our future COO

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    Is this a motto that goes with your personality? Then we want to talk with you.
    After 8 years the time has come that we have continuous growth for almost 2 years, instead of dealing with the usual peaks and lows of consultancy. I’d like to get your help to find our future COO to help streamline this growth.
    You might have seen that there are hardly any new blog posts – now you know why. By helping us find that special person, there can be put more time of writing new blog posts again.
    If you know the perfect person for this job in Amsterdam, please let them know there is this unique company looking for her or him. Sharing this blog-post would help a lot.
    You can find more information in this job-post:

    We all know that quality comes with attention to detail, but also that with growth the details are the first to be postponed. We seek help in handling daily operations during our growth. The most important tasks are:

    • Customer contact. You make sure the communication is regular and smooth with all our customers, making them more engaged and happy with us.
    • Sales follow up. You take over to discuss the needs of potential customers pre-sales has had contact with.
    • Team support. You help the development-teams to get even better by helping them to solve their daily and long-term problems.

    The job is very broad, but is all around a listening ear and getting things done.

    You have studied business administration or alike, and have a can-do attitude. You know how to work with technical people and are a real team-player. You understand how to develop and engage group dynamics.

    Do you think this is a job written for you, then we would like to hear more from you! Send an email to jobs@streamhpc.com with a motivational letter and listing relevant experience.

    Thanks for helping out!

    If you got sent here, we hope to hear from you!

    How to speed up Excel in 6 steps

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    After the last post on Excel (“Accelerating an Excel Sheet with OpenCL“), there have been various request and discussions how we do “the miracle”. Short story: we only apply proper engineering tactics. Below I’ll explain how you can also speed up Excel and when you actually have to call us.

    Excel is a special piece of software from a developer’s perspective. An important rule of software engineering is to keep functionality (code) and data separate. Excel mixes these two as no other, which actually goes pretty well in many cases unless the data gets too big or the computations too heavy. In that case you’ve reached Excel’s limits and need to properly solve it.

    Below are the steps to go through, of which most you can do yourself! Continue reading “How to speed up Excel in 6 steps”

    Call for speakers: IEEE eScience Conference in Amsterdam

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    We’re in the program committee of the 14th IEEE eScience Conference in Amsterdam, organized by the Netherlands eScience Center. It will be held from 29 October to 1 November 2018, and the deadlines for sending the abstracts is Monday 18 June.

    The conference brings together leading international researchers and research software engineers from all disciplines to present and discuss how digital technology impacts scientific practice. eScience promotes innovation in collaborative, computationally- or data-intensive research across all disciplines, throughout the research lifecycle.

    Continue reading “Call for speakers: IEEE eScience Conference in Amsterdam”

    Do you want to join StreamHPC?

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    As of this month Stream exists 8 years. 8 full years of helping our customers with fast software.In Chinese numerology 8 is a very lucky number, and we notice that.

    Over the years we’ve kept focus on quality and that was a good decision. The only problem is that we don’t have enough time to write on the blog, to organise events or even send the “monthly” newsletter. With over 200 drafts for the blog (subjects that really should be shared), we need extra people to help us out.

    Dear developers who are good with C,C++, OpenCL/CUDA and algorithms, please take a look at the following vacancies. I know you are frequenting our blog.

    We’re also seeking an all-rounder that supports in daily operations, that includes management, customer contact, team-support, etc.

    See below for more details.

      We’re looking forward to your application! We accept both remote and Amsterdam-based.

      Selecting Applications Suitable for Porting to the GPU

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      Assessing software is never comparing apples to apples

      The goal of this writing is to explain which applications are suitable to be ported to OpenCL and run on GPU (or multiple GPUs). It is done by showing the main differences between GPU and CPU, and by listing features and characteristics of problems and algorithms, which can make use of highly parallel architecture of GPU and simply run faster on graphic cards. Additionally, there is a list of issues that can decrease potential speed-up.

      It does not try to be complete, but tries to focus on the most essential parts of assessing if code is a good candidate for porting to the GPU.

      GPU vs CPU

      The biggest difference between a GPU and a CPU is how they process tasks, due to different purposes. A CPU has a few (usually 4 or 8, but up to 32) ”fat” cores optimized for sequential serial processing like running an operating system, Microsoft Word, a web browser etc, while a GPU has a thousands of ”thin” cores designed to be very efficient when running hundreds of thousands of alike tasks simultaneously.

      A CPU is very good at multi-tasking, whereas a GPU is very good at repetitive tasks. GPUs offer much more raw computational power compared to CPUs, but they would completely fail to run an operating system. Compare this to 4 motor cycles (CPU) of 1 truck (GPU) delivering goods – when the goods have to be delivered to customers throughout the city the motor cycles win, when all goods have to be delivered to a few supermarkets the truck wins.

      Most problems need both processors to deliver the best value of system performance, price, and power. The GPU does the heavy lifting (truck brings goods to distribution centers) and the CPU does the flexible part of the job (motor cycles distributing doing deliveries).

      Assessing software for GPU-porting fitness

      Software that does not meet the performance requirement (time taken / time available), is always a potential candidate for being ported to a GPU. Continue reading “Selecting Applications Suitable for Porting to the GPU”

      DOI: Digital attachments for Scientific Papers

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      Ever saw a claim on a paper you disagreed with or got triggered by, and then wanted to reproduce the experiment? Good luck finding the code and the data used in the experiments.

      When we want to redo experiments of papers, it starts with finding the code and data used. A good start is Github or the homepage of the scientist. Also Gitlab. Bitbucket, SourceForge or the personal homepage of one of the researchers could be a place to look. Emailing the authors is often only an option, if the university homepage mentions such option – we’re not surprised to get no reaction at all. If all that doesn’t work, then implementing the pseudo-code and creating own data might be the only option – not if that will support the claims.

      So what if scientific papers had an easy way to connect to digital objects like code and data?

      Here the DOI comes in.

      Continue reading “DOI: Digital attachments for Scientific Papers”

      Learn about AMD’s PRNG library we developed: rocRAND – includes benchmarks

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      When CUDA kept having a dominance over OpenCL, AMD introduced HIP – a programming language that closely resembles CUDA. Now it doesn’t take months to port code to AMD hardware, but more and more CUDA-software converts to HIP without problems. The real large and complex code-bases only take a few weeks max, where we found that solved problems also made the CUDA-code run faster.

      The only problem is that CUDA-libraries need to have their HIP-equivalent to be able to port all CUDA-software.

      Here is where we come in. We helped AMD make a high-performance Pseudo Random Generator (PRNG) Library, called rocRAND. Random number generation is important in many fields, from finance (Monte Carlo simulations) to Cryptographics, and from procedural generation in games to providing white noise. For some applications it’s enough to have some data, but for large simulations the PRNG is the limiting factor. Continue reading “Learn about AMD’s PRNG library we developed: rocRAND – includes benchmarks”

      GPU and FPGA challenge for MSc and PhD students

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      While going through my email, I found out about the third “HiPEAC Student Heterogeneous Programming Challenge”. Unfortunately the deadline was last week, but just got an email: if you register by this weekend (17 September), you can still join.

      EDIT: if you joined, be sure to comment in early November how it was. This would hopefully motivate others to join in next year. Continue reading “GPU and FPGA challenge for MSc and PhD students”

      The single-core, multi-core and many-core CPU

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      Multi-core CPU from 2011

      CPUs are now split up in 3 types, depending on the number of cores: single (1), multi (2-8) and many (10+).

      I find it more important now to split up into these three types, as the types of problems to be solved by each is very different. Based on the problem-differences I’m even expecting that the number of cores between multi-core CPUs and many-core CPUs will grow.

      Below are the three types of CPUs discussed and a small discussion on many-core processors we see around. Continue reading “The single-core, multi-core and many-core CPU”

      HPC centre EPCC says: “Better software, better science”

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      The University of Edinburgh houses the HPC centre EPCC. Neelofer Banglawala wrote about a programme which funds the development and improvement of scientific software, and also discussed about the results.

      Many of the 10 most used application codes on ARCHER have been the focus of an eCSE project. Software with more modest user bases have improved user uptake and widened their impact through eCSE-funded work. Furthermore, performance improvements can lead to tens of thousands of pounds of savings in compute time.

      Saving tens of thousands of pounds is certainly worth the investment. This also means more users can work on the same supercomputer, thus reducing waiting times. Continue reading “HPC centre EPCC says: “Better software, better science””

      Demo: cartoonizer on an Altera Arria 10 FPGA

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      It takes quite some effort to program FPGAs using VHDL or Verilog. Since several years Intel/Altera has OpenCL-drivers, with the goal to reduce this effort. OpenCL-on-FPGAs reduced the required effort to a quarter of the time, while also making it easier to alter the specifications during the project. Exactly the latter was very beneficiary when creating the demo, as the to-be-solved problem was vaguely defined. The goal was to make a video look like a cartoon using image filters. We soon found out that “cartoonized” is a vague description, and it took several iterations to get the right balance between blur, color-reduction and edge-detection. Continue reading “Demo: cartoonizer on an Altera Arria 10 FPGA”

      CPU Code modernisation – our hidden expertise

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      You’ve seen the speedups possible on GPUs. We secretly know that many of these techniques would also work on modern multi-core CPUs. If after the first optimisations the GPU still gets an 8x speedup, the GPU is the obvious choice. When it’s 2x, would the better choice be a bigger CPU or a bigger GPU? Currently the GPU is chosen more often.

      Now AMD, Intel and AMD have 28+ core CPUs, the answer to that question might now lean towards the CPU. With a CPU that has 32 cores and 256bit vector-computations via AVX2, each clock-cycle 32 double4 can be computed. A 16-core AVX1 CPU could work on 16 double2’s, which is only a fourth of that performance. Actual performance compared to peak-performance is comparable to GPUs here. Continue reading “CPU Code modernisation – our hidden expertise”