I found OpenCL useful for a narrowly focused project at work. I used the Python OpenCL bindings. My experience is somewhat dated as I did most of this about 4-5

I found OpenCL useful for a narrowly focused project at work. I used the Python OpenCL bindings. My experience is somewhat dated as I did most of this about 4-5 years ago and then stopped paying much attention to it. That code continues to run in production without any real maintenance, which I consider a point in favor of it. I.e. it just gets redeployed to newer OS and Python installs and continues to work. From others' comments here, it seems there hasn't been much development since then in OpenCL land. I imagine details of device support might vary over generations of hardware and driver releases... I made use of half-precision float storage but ran compute at single-precision since my devices did not offer fast half-precision math. This was small "hpc", i.e. a task that would run on one workstation or modest single socket or dual socket x86_64 server or VM. The most performance-sensitive aspect was that it was also used during the data loading/startup phase of an interactive tool. So a human user was impatiently waiting for results. The first prototype was just using Python numpy routines for convolutions, etc. I used OpenCL to get running time from many minutes down to tens of seconds and called it good enough. I enjoyed that I could run the same code on a workstation GPU with the NVIDIA driver or via x86 multithreaded SIMD using the Intel driver. I did not do any real work with Intel nor AMD GPUs because of the hardware selection we had on hand. I also needed more than 6GB GPU RAM for it to be worth using. Even my Titan X with 12GB was only about 2-3x faster than using x86 SIMD for my problem, due to the complex tradeoffs between RAM and bus bandwidths for data transfers. This is after I'd already done some algorithmic optimizations to bring down the compute/IO ratio. A big thing I hear others talk about is the rich development tools with CUDA and the relatively impoverished OpenCL tooling. I am old school enough to get along without it. I was able to compensate for limited Python OpenCL tools by doing some of my development and debugging cycle embedded in hacked up variants of my own viz tools. You might think of this a bit like debugging with printf, except my print statement could send a dense 3D array into an OpenGL-based renderer on my workstation.