Ask HN: Best place to learn GPU programing?

I last did GPU programming around the transition from just-shaders to gpgpu... And I want to come back. However, I don't want to buy new hardware just yet. In addition to all the study materials posted, can anyone recommend a good hosted GPU setup for experiments? e.g. what kind of EC2/GCE instance would be a cost effective way to [re]learn GPU programming?

thanks for the resource !!

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Are you more interested in open or practical? OpenCL is open. That's important for a lot of people. But, on the practical side, Nvidia has invested more in improving CUDA than all actors combined have put into OpenCL. It's reached the point that people are implementing open-source CUDA compilers to bring CUDA to non-Nvidia platforms.

I imagine it was a typo, but... I personally have found PyOpenCL and the vispy.gloo OpenGL APIs to be very convenient together (along with Numpy and Scipy of course).

If it fits your goals, a useful mental model is that you are programming Numpy ndarray data, and all these tools just fit together to let you manage the data elements, apply transforms, and view them. You should be thinking about the math that solves your domain problem, using alternative mathematical formulations to optimize your algorithms, and only then worrying about using a tuned parallel implementation of that math.

I prototype new ideas and visualize results and intermediate data very easily, with easy transitions between using numpy/scipy routines, custom OpenCL kernels, and OpenGL shaders. I've even found myself using OpenGL to do the visual equivalent of "printf debugging" to look at intermediate array results when developing new array operations and wondering what I've done wrong or misunderstood about the problem. It's very instructive to create little sub-problems you can run as independent scripts during dev/test/microbenchmarking cycles. You should iterate on many small experiments, not assume you can design high-performance solution in one top-down adventure.

For high performance processing, you eventually need to understand architecture limitations and the impact of different problem decompositions. Non-trivial, multi-dimensional problems need to be decomposed into smaller blocks to get better cache locality for the vectorized/parallel code that will process each block. Otherwise, you won't enjoy the benefits of parallel hardware as all the compute units are stalled waiting for data fetches. I find myself doing more and more meta-programming in Python, where I reason about array shapes and sizes, compute slicing geometries, and then loop to extract and dispatch chunks of data to OpenCL jobs which in turn use a dense form of SIMD or SPMD execution on that block. Python is great for this kind of marshaling work.

Also, PyOpenCL makes it trivial to switch between backend drivers. The Intel OpenCL driver is quite good on recent laptop and server CPUs. You might be surprised how much performance you can get out of recent i5/i7 or Xeon E3/E5 CPUs when you use all cores and all SIMD units effectively. Plus, the CPU backend is able to use all system RAM and has a more uniform cache behavior, which makes it more forgiving for badly decomposed problems compared to GPU. This can be a big help for prototyping as well as for limited-run problems where you don't have time to invest in fully tuning your data structures for GPU requirements.