Our training concepts for GPGPU

It’s almost time for more nerdy stuff we have in the pipe-line, but we’ll keep for some superficial blah for a moment. We concentrate on training (and consultancy). There is a lot of discussion here about “how to design training-programs about difficult concepts for technical people”, or better: “how to learn yourself something difficult”. At the end of this blog, we’ll show you a list how to learn OpenCL yourself, but before that we want to share how we look at training you.

Disclaimer: this blog item is positive about our own training-program for obvious reasons. We are aware people don’t want (too much) spam, so we’ll keep this kind of blogs to the minimum. If you want to tell the world that your training-program is better, first mail us for our international partner-program. If you want the training, come back on 14 June or mail us.

OpenCL and CUDA are not the easiest programming languages due to incomparable concepts in software-land (You can claim Java is “slightly” different). Can the usual ways of training give you the insights and facts you need to know?

Current programs

Most training-programs are vendor-supported. People who follow us on Twitter, know we are not the best supporters of vendor locked products. So lets get a list of a typical vendor-supported training-programs, I would like to talk about:

  • They have to be difficult, so the student accomplishes something.
  • The exam are expensive to demotivate trial-on-error-students.
  • You get an official certificate, which guarantees a income-raise.
  • Books and trainings focus on facts you must learn.
  • It’s very clear what you must learn and what you can skip.

So in short, you chose you wanted to know the material and put a lot of effort in it. You get back more than just the knowledge.

Say you get the opposite:

  • They are easy to accomplish.
  • Exams is an assignment which you only need to finish. You can try endless times.
  • You don’t get a certificate, but you might get feedback and homework for self-study.
  • You get a list of facts you must learn; the concepts are explained to support this.
  • You are free to pick which subject you like.

That sucks! You cannot brag about your accomplishments and after the training you still cannot do anything with it; it will probably take years to actually finish it. So actually it’s very clear why the programs are like this, or can we learn from this opposing list? Just like with everything else, you never have to just copy what’s available but pick out the good parts.

Learning GPGPU

If you want to learn GPGPU, you have to learn (in short) shader-concepts, OpenCL, CUDA and GPU-architectures. What would be needed to learn it, according to us?

  • A specified list of subjects you can check when understood.
  • An insight story of the underlying concepts to better understand the way stream-computing works. Concepts are the base of everything, to actually make it sound simple.
  • Very practical know-how. Such as how to integrate stream-computing-code into your current software.
  • A difficult assignment that gets you in touch with everything you learned. The training gave you the instruments you need to accomplish this step.

So there’s no exam and no certificate; these are secondary reasons for finishing the course. The focus should be getting the brain wrapped around the concepts and getting experience. As the disclaimer warned you, our training-program has a high focus on getting you up-and-running in one day. And you do get a certificate after your assignment gets approved, so bragging is easy.

If you want to learn stream-computing and you won’t use our training-program, what then?

  • Read our blog (RSS) and follow us on Twitter.
  • Make yourself a list of subjects you think you have to learn. Thinking before doing helps in getting a focus.
  • Buy a book. There are many.
  • Play around with existing examples. Try to break it. Example: what happens if the kernel uses more and more local/private memory.
  • Update the list of subjects; the more extensive, the better. Prioritise.
  • Find yourself an assignment. For example: try to compress or decompress a large JPG using OpenCL. If you succeed, get yourself a harder assignment. Do you want to be good or the best?

If you know OpenCL, CUDA is easy to learn! We will have some blogs which support your quest on learning OpenCL, so just start to dig in today and see you next time.

Related Posts

Kabul - An Afghan National Police (ANP) students study basic logistics training at the Afghan Ministry in Interior (MOI) August 1, 2010. The Afghan MOI is providing the first ever computer based logistics training for ANP personnel. (U.S. Air Force photo/ Staff Sergeant Matt Davis)

Updated: OpenCL and CUDA programming training – now online

...  due to Corona, the Amsterdam training has been cancelled. We'll offer the training online on dates that ...  this is the ...

chess-finish

2011 Q&A with NVIDIA’s David Kirk on CUDA and OpenCL, analysed two years later

...  of the mind-set, the unlearning of CPU-programming concepts. But the rewards are significant. Change two percent ...

default

PDFs of Monday 29 August

...  with the latest AVX and SSE extensions) than using GPGPU. The Practical Reality of Heterogeneous Super Computing. A ...  ...

default

28 June: OpenCL course in Utrecht, NL

...  for you. The most important thing about GPGPU are the concepts. In other words the "why" they chose to make GPGPU-languages ike this. ...