High-Performance Computing Using FPGAs
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From a theoretical perspective, both hardware description languages and programming languages can be use to express any computation both are Turing complete , but the difference in engineering details is vast. However, even when using such languages, programming FPGAs is still an order of magnitude more difficult than programming instruction based systems. A large part of the difficulty of programming FPGAs are the long compilation times. This is due to the place-and-route phase : the custom circuit that we want needs to be mapped to the FPGA resources that we have , with paths as short as possible.
This is a complex optimization problem which requires significant computation. Intel does offer an emulator, so testing for correctness does not require this long step, but determining and optimizing performance does require these overnight compile phases.
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However, the situation is really not that clear cut, especially when it comes to floating point computations, but let us first consider situations where FPGAs are clearly more energy efficient than a CPU or GPU. Where FPGAs shine in terms of energy efficiency is at logic and fixed precision as opposed to floating point computations. In crypto-currency such as bitcoin mining, it is exactly this property that makes FPGAs advantageous. In fact, everyone used to mine bitcoin on FPGAs.
Which are special integrated circuits built for just one purpose. ASICs are an even more energy efficient solution but require a very large upfront investment for the design and large number of chips produced to be cost effective. But back to FPGAs. A lot of high performance computing use cases, such as deep learning, often depend on floating point arithmetic — something GPUs are very good at. In the past, FPGAs were pretty inefficient for floating point computations because a floating point unit had to be assembled from logic blocks, costing a lot of resources.
Does the addition of floating point units make FPGAs interesting for floating point computations in terms of energy efficiency? Are they more energy efficient than a GPU?
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The fastest professional GPU that is available now is the Tesla V , which has a theoretical maximum of 15 TFLOPS Tera-floating-point-operations per second, a standard means of measuring floating point performance and uses about Watts of power. This card has a theoretical maximum of 9.
However, the difference is small, and it is very possible that a new FPGA card, such as this upcoming card based on the Stratix 10 FPGA, is more energy efficient than the Volta on floating point computations. Moreover, the above comparison is between apples and oranges in the sense that the Tesla V is produced at a12 nanometer process, whereas the Stratix 10 is produced at the older 14 nanometer process.
While the comparison does show that if you want energy efficient floating point computations now that it is better to stick with GPUs, it does not show that GPUs are inherently more energy efficient for floating point computations.