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How do you really target FPGA and GPU when these are so different? e.g. what I find appealing about FPGA is (1) very low latency and (2) arbitrary precision da

How do you really target FPGA and GPU when these are so different? e.g. what I find appealing about FPGA is (1) very low latency and (2) arbitrary precision data paths, like if I want 6-bit data paths I can have them. The GPU on the other hand is throughput oriented and you get the data types that were baked in. I remember looking at https://www.intel.com/content/www/us/en/developer/tools/oneapi/overview.html https://www.intel.com/content/www/us/en/developer/tools/onea... and thinking "how could that possibly work?" and the fact that it's had basically zero uptake suggests to me that my hot take was the right take.

smoothdeveloper 9mo ago
For GPU, F# has prior art in targeting it, a notable one is https://developer.nvidia.com/blog/jet-gpu-powered-fulfillment/ https://developer.nvidia.com/blog/jet-gpu-powered-fulfillmen... I believe it is mostly about intrinsic libraries and developing the code to stick to the idioms of using such libraries; in the context of Clef, maybe the author of this infrastructure is aiming at close interaction between the compiler frontend, a standard GPU programming library and the Clef backend, but I can't really comment on this. What Clef seems to be doing is: * bringing semantics that are needed for native platform targeting, expressed with idioms familiar to F# developers (through the type system and the lowering during compilation stages, retaining target specific semantics) * making the broad F# programming idioms applicable to such targets (when applicable obviously) The website contains a lots of detailed articles about aspects of translating F# constructs to the type of environment this compiler targets, but AFAIR, nothing specific to GPU programming.