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 (last step).

A computer can handle 10s of gigabytes per second. Now look how big your Excel-sheet is and how much time it takes. Now you understand that the problem is probably not your computer.

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!

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” →

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” →

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” →

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” →

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”” →

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” →

OpenCL remains to be a popular programming language for accelerators, from embedded to HPC. Good examples are consumer software and embedded devices. With Vulkan potentially getting OpenCL-support in the future, the supported devices will only increase.

For multicore-CPUs and GPUs we now have monthly training dates for the rest of the year:

• OpenCL CPU/GPU Training (4 days) from 17 to 20 July 2017 in Amsterdam
• OpenCL CPU/GPU Training (4 days) from 21 to 24 August 2017 in Amsterdam
• OpenCL CPU/GPU Training (4 days) from 18 to 21 September 2017 in Amsterdam
• OpenCL CPU/GPU Training (4 days) from 16 to 19 October 2017 in Amsterdam
• OpenCL CPU/GPU Training (4 days) from 20 to 23 November 2017 in Amsterdam
• OpenCL CPU/GPU Training (4 days) from 18 to 21 December 2017 in Amsterdam

Minimum number of participants is two. By request the location and date can be changed.

The first day of the training is the OpenCL Foundations training, which can be booked separately.

For more information call us at +31854865760.

A month ago IWOCL (OpenCL workshop) and DHPCC++ (C++ for GPUs) took place. Meanwhile many slides and posters have been published online. As of today 23 talks are with slides.

The proceedings are available via the ACM Digital Library. This needs a ACM Digital Library subscription of $198, if your company/university does not have access yet.

IWOCL 2018 will be in Edinburgh (Scotland, UK), 15-17 May 2018 (provisional).

Most of our projects are around performance optimisation, but we’re cleaning up bugs too. This is because you can only speed up software when certain types of bugs are cleared out. A few months ago, we got a different type of request. If we could solve bugs in MESA 3D that appear in games.

Yes, we wanted to try that and got a list of bugs to solve. And as you can read, we were successful.

Below you found a detailed description of one of the 5 bugs we solved by digging deep into the different games and the MESA 3D drivers. At the end of the blog post you’ll find the full list with links to issues in MESA’s bugtracker. Continue reading “Bug fixing the MESA 3D drivers” →

In the perfect world all software is fast, giving us time to do actual work. Unfortunately we live in an unperfect world, and we have to spend extra time controlling our anger as the software keeps us waiting.

Therefore we have opened the Slow Software Hotline – reachable via both phone and email. It has one goal: make you feel happy again.

Reporting is easy. Just name the commercial software that needs to be sped up and why. We’ll do the rest. If you need help with initial anger management due to the slow (and/or buggy) software, we’re happy to help with breathing practises.

Phone: +31 854865760

Email: hotline@streamhpc.com

We will not sit still until all software is fast. Speeding up all software out there. One at the time.

AMD open sourced the OpenCL driver stack for ROCm in the beginning of May. With this they kept their promise to open source (almost) everything. The hcc compiler was open sourced earlier, just like the kernel-driver and several other parts.

Why this is a big thing?
There are indeed several open source OpenCL implementations, but with one big difference: they’re secondary to the official compiler/driver. So implementations like PortableCL and Intel Beignet play catch-up. AMD’s open source implementations are primary.

It contains:

• OpenCL 1.2 compatible language runtime and compiler
• OpenCL 2.0 compatible kernel language support with OpenCL 1.2 compatible runtime
• Support for offline compilation right now – in-process/in-memory JIT compilation is to be added.

For testing the implementation, see Khronos OpenCL CTS framework or Phoronix openbenchmarking.org.

Why is it open sourced?

There are several reasons. AMD wants to stand out in HPC and therefore listened carefully to their customers, while taking good note on where HPC was going. Where open source used to be something not for businesses, it is now simply required to be commercially successful. Below are the most important answers to this question.

Give deeper understanding of how functions are implemented

It is very useful to understand how functions are implemented. For instance the difference between sin() and native_sin() can tell you a lot more on what’s best to use. It does not tell how the functions are implemented on the GPU, but does tell which GPU-functions are called.

Learning a new platform has never been so easy. Deep understanding is needed if you want to go beyond “it works”.

Debug software

When you are working on a large project and have to work with proprietary libraries, this is a typical delay factor. I think every software engineer has this experience that the library does not perform as was documented and work-arounds had to be created. Depending on the project and the library, it could take weeks of delay – only sarcasm can describe these situations, as the legal documents were often a lot better than the software documents. When the library was open source, the debugger could step in and give the “aha” that was needed to progress.

When working with drivers it’s about the same. GPU drivers and compilers are extremely complex and ofcourse your project hits that bug which nobody encountered before. Now all is open source, you can now step into the driver with the debugger. Moreover, the driver can be compiled with a fix instead of work-around.

Get bugs solved quicker

A trace now now include the driver-stack and the line-numbers. Even a suggestion for a fix can be given. This not only improves reproducibility, but reduces the time to get the fix for all steps. When a fix is suggested AMD only needs to test for regression to accept it. This makes the work for tools like CLsmith a lot easier.

Have “unimportant” specific improvements done

Say your software is important and in the spotlight, like Blender or the LuxMark benchmark, then you can expect your software gets attention in optimisations. For the rest of us, we have to hope our special code-constructions are alike one that is targeted. This results in many forums-comments and bug-reports being written, for which the compiler team does not have enough time. This is frustrating for both sides.

Now everybody can have their improvements submitted, giving it does not slow down the focus software ofcourse.

Get the feature set extended

Adding SPIR-V is easy now. The SPIRV-frontend needs to be added to ROCm and the right functions need to be added to the OpenCL driver. Unfortunately there is no support for OpenCL 2.x host-code yet – I understood by lack of demand.

For such extensions the AMD team needs to be consulted first, because this has implications on the test-suite.

Get support for complete new things

It takes a single person to make something completely new – this becomes a whole easier now.

More often there is opportunity in what is not there yet, and research needs to be done to break the chicken-egg. Optimised 128 bit computing? Easy complex numbers in OpenCL? Native support for Halide as an alternative to OpenCL? All high performance code is there for you.

Initiate alternative implementations (?)

Not a goal, but forks are coming for sure. For most forks the goals would be like the ones above, to later be merged with the master branch. There are a few forks that go their own direction – for now hard to predict where those will go.

Improve and increase university collaborations

If the software was protected, it was only possible under strict contracts to work on AMD’s compiler infrastructure. In the end it was easier to focus on the open source backends of LLVM than to go through the legal path.

Universities are very important to find unexpected opportunities, integrate the latest research in, bring potential new employees and do research collaborations. Added bonus for the students is that the GPUs might be allowed to used for games too.

Timour Paltashev (Senior manager, Radeon Technology Group, GPU architecture and global academic connections) can be reached via timour dot paltashev at amd dot com for more info.

Get better support in more Linux distributions

It’s easier to include open source drivers in Linux distributions. These OpenCL drivers do need a binary firmware (which were disassembled and seem to do as advertised), but the discussion is if this is part of the hardware or software to mark it as “libre”.

There are many obstacles to have ROCm complete stack included as the default, but with the current state it makes much more chance.

Performance

Phoronix has done some benchmarks on ROCm 1.4 OpenCL in January on several systems and now ROCm 1.5 OpenCL on a Radeon RX 470. Though the 1.5 benchmarks were more limited, the important conclusion is that the young compiler is now mostly on par with the closed source OpenCL implementation combined with the AMDGPU-drivers. Only Luxmark AMDGPU was (much) better. Same comparison for the old proprietary fgrlx drivers, which was fully optimised and the first goal to get even with. You’ll see that there will be another big step forward with ROCm 1.6 OpenCL.

Get started

You can find the build instructions here. Let us know in the comments what you’re going to do with it!

Last week AMD released ports of Caffe, Torch and (work-in-progress) MXnet, so these frameworks now work on AMD GPUs. With the Radeon MI6, MI8 MI25 (25 TFLOPS half precision) to be released soonish, it’s ofcourse simply needed to have software run on these high end GPUs.

The ports have been announced in December. You see the MI25 is about 1.45x faster then the Titan XP. With the release of three frameworks, current GPUs can now be benchmarked and compared.

Especially the expected good performance/price ratio will make this very interesting, especially on large installations. Another slide discussed which frameworks will be ported: Caffe, TensorFlow, Torch7, MxNet, CNTK, Chainer and Theano.

This leaves HIP-ports of TensorFlow, CNTK, Chainer and Theano still be released. Continue reading “Caffe and Torch7 ported to AMD GPUs, MXnet WIP” →

Since 2010 the name StreamComputing has been used and is widely known now in the GPU-computing industry. But the name has three problems: we don’t have the .com domain, it does not directly show what we do, and the name is quite long.

Some background on the old domain name

While the initial focus was Europe, for years our projects are done for 95% for customers outside the Netherlands and over 50% outside Europe – with the .eu domain we don’t show our current international focus.

But that’s not all. The name sticks well in academics, as they’re more used to have longer names – just try to read a book on chemistry. Names I tested as alternatives were not well-received for various reasons. Just like “fast” is associated with fast food, computing is not directly associated with HPC. So fast computing gets simply weird. Since several customers referred to us as Stream, it made much sense to keep that part of the name.

Not a new begin, but a more focused continuation

Stream HPC defines more what we are: we build HPC software. Stream HPC combines the well-known name combined with our diverse specialization.

• Programming GPUs with CUDA or OpenCL.
• Scaling code to multiple CPU and GPUs
• Creating AI-based software
• Speeding up code and optimizing for given architectures
• Code improvement
• Compiler tests and compiler building (LLVM)

With the HPC-focus we were more able to improve ourselves. We have put a lot of time in professionalizing our development-environment and project-management by implementing suggestions from current customers and our friends. We were already used to work fully independent and be self-managed, but now we were able to standardize more of it.

The rebranding process

Rebranding will take some time, as our logo and name is in many places. For the legal part we will take some more time, as we don’t want to get into problems with i.e. all the NDAs. Email will keep working on both domains.

We will contact all organizations we’re a member of over the coming weeks. You can also contact us, if you read this.

StreamComputing will never really go away. The name was with us for 7 years and stands for growing with the upcoming of GPU-computing.