StreamHPC is best-known for its OpenCL services, including development. We have hit records of over 250’000 times in optimising code using OpenCL. During those projects several techniques have been used to get to these high numbers – well-designed projects we can still speed-up 2 to 8 times.

Advantages

OpenCL works on most types of hardware of any language. Compare it to C and C++, which is used to program all kinds of software on very different hardware.

The basic OpenCL is used to make portable software that performs. The advanced OpenCL is used to optimise for maximum performance for specific accelerators.

Hardware

Projects can be targeting one or more operating systems, focusing on one or several processors:

• CPUs, by Intel, AMD and/or ARM
• NVidia GPUs
• AMD GPUs
• Embedded GPUs
  • Vivante
  • ARM MALI
  • Imagination
  • Qualcomm
• Altera FPGAs
• Xilinx FPGAs
• Several special focus processors, mostly implementing a subset of OpenCL.

We use modern coding techniques to make code for maximum flexibility and maximum performance.

How OpenCL works

OpenCL is an extension to existing languages. It makes it possible to specify a piece of code that is executed multiple times independently from each other. This code can run on various processors – not only the main one. Also there is an extension for vectors (float2, short4, int8, long16, etc), because modern processors have support for that.

So for example you need to calculate Sin(x) of a large array of one million numbers. OpenCL detects which devices could compute this for you and gives some statistics of each device. You can pick the best device, or even several devices, and send the data to the device(s). Normally you would loop over the million numbers, but now you say something like: “Get me Sin(x) of each x in array A”. When finished, you take the data back from the device(s) and you are finished.

As the compute-devices can do more in parallel and OpenCL is better in describing independent functions, the total execution time is much lower than conventional methods.

4 common questions on OpenCL

Q: Why is it so fast?
A: Because a lot of extra hands make less work, the hundreds of little processors on a graphics card being the extra hands. But cooperation with the main processor keeps being important to achieve maximum output.

Q: Does it work on any type of hardware?
A: As it is an open standard, it can work on any type of hardware that targets parallel execution. This can be a CPU, GPU, DSP or FPGA.

Q: How does it compare to OpenMP/MPI?
A: Where OpenMP and MPI try to split loops over threads/servers and is CPU-oriented, OpenCL focuses on getting threads being data-position aware and making use of processor-capabilities. There are several efforts to combine the two worlds.

Q: Does it replace C or C++?
A: No, it is an extension which integrates well with C, C++, Python, Java and more.

So you want your software to be much faster than the competition?

In 4 days your software team learns all techniques to make extremely fast software.

Your team will learn how to write optimal code for GPUs and make better use of the existing hardware. They will be able to write faster code immediately after the training – doubling the speed is minimal, 100 times is possible. Your customers will notice the difference in speed.

We use advanced, popular techniques like OpenCL and older techniques like cache-flow optimisation. At the end of the training you’ll receive a certificate from StreamHPC.

Want more information? Contact us.

About the training

Location and Time

OpenCL is a rather new subject and hard-coding the location and time has not proved to be successful in the past years for trainers in this subject. Therefore we chose for flexible dates and initially offer the training in large/capital cities and technology centres world-wide.

A final date for a city will be picked once there are 5 to 8 attendees, with a maximum of 12. You can specify your preferences for cities and dates in the form below.

Some discounts are available for developing countries.

Agenda

Day 1: Introduction

Learn about GPU architectures and AVX/SSE, how to program them and why it is faster.

• Introduction to parallel programming and GPU-programming
• An overview of parallel architectures
• The OpenCL model: host-programming and kernel-programming
• Comparison with NVIDIA’s CUDA and Intel’s Array Building Blocks.
• Data-parallel and task-parallel programming

Day 2: Tools and advanced subjects

Learn about parallel-programming tactics, host-programming (transferring data), IDEs and tools.

• Static kernel analysis
• Profiling
• Debugging
• Data handling and preparation
• Theoretical backgrounds for faster code
• Cache flow optimisation

Day 3: Optimisation of memory and group-sizes

Day 4: Optimisation of algorithms

Enrolment

When filling in this form, you declare that you intend to follow the course. Cancellation can be done via e-mail or phone at any time.

StreamHPC will keep you up-to-date for the training at your location(s). When the minimum of 5 attendees has been reached, a final date will be discussed. If you selected more locations, you have the option to wait for a training at another city.

Put any remarks you have in the message. If you have any question, mail to trainings@streamhpc.com.

VHDL and Verilog are not the right tools when it comes to developing on FPGAs fast.

• It is time-consuming. If the first cycle takes 3 months, then each subsequent cycle easily takes 2 weeks. Time is money.
• Porting or upgrading a design from one FPGA device to another is also time-consuming. This makes it essential to choose the final FPGA vendor and family upfront.
• Dual-platform development on CPU and FPGA needs synchronisation. The code works on either the CPU or the FPGA, which makes the functional tests made for the CPU-version less trustworthy.

Here is where OpenCL comes in.

• Shorter development cycles. Programming in OpenCL is normally much faster than in VHDL or Verilog. If you are porting C/C++ code onto FPGA the development cycles will be dramatically shorter. Think weeks instead of months – as this news article explains. This means a radically reduced investment as well as providing time for architectural exploration.
• OpenCL works on both CPUs and FPGAs, so functional tests can be run on either. As a bonus the code can be optimised for GPUs, within a short time-frame.
• The performance is equal to VHDL and Verilog, unless FPGA-specific optimisations are used, such as vector-widths not equal to a power of two.
• Vendor Agnostic solution. Porting to other FPGAs takes considerably less time and the compiler solves this problem for you.
• Both Xilinx and Altera have OpenCL compilers. Altera were the first to come out with an OpenCL offering and have a full SDK, which is an add-on to Quartus II. Xilinx also have a stand-alone OpenCL development environment solution called SDAccel.

Support for OpenCL is strong by both Altera and Xilinx

Both vendors suggest OpenCL to overcome existing FPGA design problems. Altera suggest to use OpenCL to speed-up the process for existing developers. So OpenCL is not a third party tool, you need to trust separately.

OpenCL allows a user to abstract away the traditional hardware FPGA development flow for a much faster and higher level software development flow – Altera

Xilinx suggests that OpenCL can enable companies without the needed developer resources to start working with FPGAs.

Teams with limited or no FPGA hardware resources, however, have found the transition to FPGAs challenging due to the RTL (VHDL or Verilog) development expertise needed to take full advantage of these devices. OpenCL eases this programming burden – Xilinx

Why choose StreamHPC?

There are several reasons to choose letting us to do the porting and protoyping of your product.

• We have the right background, as our team consists of CPU, GPU and FPGA developers. Our code is therefore designed with easy porting in mind.
• Our costs are lower than having the product done in Verilog/VHDL.
• We give guarantees and support for our products on all platforms the product is ported on.
• We can port the final OpenCL code to Verilog/VHDL, keeping the same performance. In case you don’t trust a high-level language, we have you covered.
• Optionally you can get both the code and a technical report with a detailed explanation of how we did it. So you can learn from this and modify the code yourself.
• You get free advice on when (and not) to use OpenCL for FPGAs.

There are three ways to get in contact quickly:

call: +31 854865760 (European office hours)

e-mail: contact@streamhpc.com

Fill in this form – mention when you want to be called back (possible outside normal office hours):

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Want to read more?

We wrote about OpenCL-on-FPGAs on our blog in the previous years.

• Why use OpenCL on FPGAs? (2014)
• Starting with Altera OpenCL-on-FPGAs (2013)
• OpenCL on Altera FPGAs (2012)

We transform your prototype into a performant product

Protoypes are very useful to proof the concepts are working and to test out various variations in a short time. Often Matlab code, Python, Excel or Labview are used, or libraries like OpenCV. The main problems with that code are often performance and portability. We solve that. Our software development projects are exactly very like the software projects you are used to; the main difference is that the code is much faster than if somebody else does it.

An overview of our advantages:

• As we only have senior programmers, the code quality is higher.
• We are very much in control and can therefore have more variety:
  • We make our software seamlessly work together with any code that doesn’t need a rewrite.
  • Our code can work on various hardware, like CPU, GPU and FPGA.
  • Our code works on Windows, Linux and OSX.
• You will get the fastest software on the market. We have often have delivered speed-ups above expectation.