Understanding Peak Floating-Point Performance Calculations

Michael Parker, Altera
EETimes (10/20/2014 02:03 PM EDT)

DSPs, GPUs, and FPGAs serve as accelerators for many CPUs, providing both performance and power efficiency benefits. Given the variety of computing architectures available, designers need a uniform method to compare performance and power efficiency. The accepted method is to measure floating-point operations per second (FLOPs), where a FLOP is defined as either an addition or multiplication of single (32 bit) or double (64 bit) precision numbers in conformance with the IEEE 754 standard. All higher-order functions, such as division, square root, and trigonometric operators, can be constructed using adders and multipliers. As these operators, as well as other common functions such as fast Fourier transforms (FFTs) and matrix operators, require both adders and multipliers. There is commonly a 1:1 ratio of adders and multipliers in all these architectures.

Let's look at how we go about comparing the performance of the DSP, GPU, and FPGA architectures based on their peak FLOPS rating. The peak FLOPS rating is determined by multiplying the sum of the adders and multipliers by the maximum operation frequency. This represents the theoretical limit for computations, which can never be achieved in practice, since it is generally not possible to implement useful algorithms that can keep all the computational units occupied all the time. It does however provide a useful comparison metric.

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