FPBench Measures 1.1

FPBench is a standard benchmark suite for the floating point community. The benchmark suite contains a common format for floating-point computation and metadata and a common set of accuracy measures:

FPBench Standard Measures

FPBench standardizes several measures of accuracy; tools that measure accuracy should state which of the FPBench accuracy measures they use, so that the community can more easily compare tools.

FPBench analyses floating-point accuracy along several axes: scaling vs non-scaling error, forward vs. backward error, and maximum vs. average error. Tools that measure error may use sound vs. statistical techniques, and tools that transform programs have several options for how to measure accuracy improvement.

Scaling vs. non-scaling error (\(\varepsilon\))

There are several ways to measure the error of producing the inaccurate value \(\hat x\) instead of the true value \(x\). Two common mathematical notions are the absolute and relative error:

\[ \varepsilon_{\text{abs}}(x, x') = \left|x - \hat x\right| \quad \text{and} \quad \varepsilon_{\text{rel}}(x, x') = \left|\frac{x - \hat x}{x}\right| \]

Relative error scales with the quantity being measured, and thus makes sense for measuring both large and small numbers, much like the floating-point format itself. A notion of error more closely tied to the floating-point format is the Units in the Last Place (ULPs) difference.

Some tools instead use the logarithm of the ULPs, which approximately describes the number of incorrect low-order bits in \(\hat x\). These two measures are defined as:

\[ \varepsilon_{\text{ulps}}(x, x') = |\mathbb{F} \cap [\min(x, \hat x), \max(x, \hat x)]| \quad \text{and} \quad \varepsilon_{\text{bits}}(x, x') = \log_2 \varepsilon_{\text{ulps}}(x, x') \]

The floating-point numbers are distributed roughly exponentially, so this measure of error scales in a similar manner to relative error; however, it is better-behaved in the presence of denormal numbers and infinities.

Forward vs. backward error (\(\epsilon\))

Forward error and backward error are two common measures for the error of a mathematical computation. For a true function \(f\) and its approximation \(\hat f\), forward error measures the difference between outputs for a fixed input, while backward error measures the difference between inputs for a fixed output. Formally:

\[ \epsilon_{fwd}(f, \hat{f}, x) = \varepsilon(f(x), \hat{f}(x)) \] \[ \epsilon_{bwd}(f, \hat{f}, x) = \min \left\{ \varepsilon(x, x') : x' \in \mathbb{F}^n \land {\hat f}(x') = f(x) \right\} \]

Backward error is important for evaluating the stability of an algorithm, and in scientific applications where multiple sources of error (algorithmic error vs. sensor error) must be compared. Existing tools typically measure forward error because backward error can be tricky to compute for floating-point computations, where there may not be an input that produces the true output.

Average vs. maximum error (\(E\))

Describing the error of a floating-point computation means summarizing its behaviour across multiple inputs. Existing tools use either maximum or average error for this task. Formally:

\[ E_{\text{max}}(f, \hat{f}) = \max \left\{\epsilon(f, \hat{f}, x) : x \in \mathbb{F}^n\right\} \quad \text{and} \quad E_{\text{avg}}(f, \hat{f}) = \frac{1}{N} \sum_{x\in \mathbb{F}^n} \epsilon(f, \hat{f}, x) \]

Worst-case error tends to be easier to measure soundly, while average error tends to be easier to measure statistically.

Sound vs. statistical techniques

Running a floating-point program on all valid inputs is intractable. Existing tools either soundly overapproximate the error using static analysis, or approximate the error using statistical sampling.

Most static techniques are based on interval or affine arithmetic to over-approximate floating-point arithmetic, often using abstract interpretation. Abstract interpretation may use either non-relational or relational abstract domains, and may use acceleration techniques (widenings) to over-approximate loops without unrolling them. While such techniques tend to provide loose over-approximations of the floating-point error of programs, they are fast and provide sound error bounds. In some embedded applications, correctness is critical and unsound techniques will not do.

In domains where correctness is not absolutely critical, sampling can provide tight approximations of error. Many sampling techniques are used, including naive random samples and Markov-chain Monte Carlo. These techniques involve running a program multiple times, so they tend to be slower than static analysis.

Measuring improvement (\(\iota\))

Tools that transform floating-point programs need to compare the accuracy of two floating-point implementations of a function, the original and the transformed. Several comparison measures are possible. Comparisons can use the improvement in worst-case or average error between the original \(\hat f\) and improved \(\hat f'\) implementation of the same mathematical function \(f\):

\[ \iota_{\text{imp}} = E(f, \hat{f}) - E(f, \hat{f}') \]

However, one cannot usually improve the accuracy of a computation simultaneously on all inputs. It is thus often necessary to make a computation less accurate on some points to make it more accurate overall. In this case, it may be useful to report the largest unimprovement, which measures the cost of improving accuracy:

\[ \iota_{\text{wrs}}(\hat{f},\hat{f}') = \max \left\{ \epsilon(f, \hat{f}', x) - \epsilon(f, \hat{f}', x) \ :\ x\in \mathbb{F}^n\right\} \]