On Numerical Error Propagation with Sensitivity

An emerging area of research is to automatically compute reasonably accurate upper bounds on numerical errors, including roundoffs due to the use of a finite-precision representation for real numbers such as floating point or fixed-point arithmetic. Previous approaches for this task are limited in their accuracy and scalability, especially in the presence of nonlinear arithmetic. Our main idea is to decouple the computation of newly introduced roundoff errors from the amplification of existing errors. To characterize the amplification of existing errors, we use the derivatives of functions corresponding to program fragments. We implemented this technique in an analysis for programs containing nonlinear computation, conditionals, and a certain class of loops. We evaluate our system on a number of benchmarks from embedded systems and scientific computation, showing substantial improvements in accuracy and scalability over the state of the art

Towards a Compiler for Reals

Numerical software, common in scientific computing or embedded systems, inevitably uses a finite-precision approximation of the real arithmetic in which most algorithms are designed. In many applications, the roundoff errors introduced by finite-precision arithmetic are not the only source of inaccuracy, and measurement and other input errors further increase the uncertainty of the computed results. Adequate tools are needed to help users select suitable data types and evaluate the provided accuracy, especially for safety-critical applications. We present a source-to-source compiler called Rosa that takes as input a real-valued program with error specifications and synthesizes code over an appropriate floating-point or fixed-point data type. The main challenge of such a compiler is a fully automated, sound, and yet accurate-enough numerical error estimation. We introduce a unified technique for bounding roundoff errors from floating-point and fixed-point arithmetic of various precisions. The technique can handle nonlinear arithmetic, determine closed-form symbolic invariants for unbounded loops, and quantify the effects of discontinuities on numerical errors. We evaluate Rosa on a number of benchmarks from scientific computing and embedded systems and, comparing it to the state of the art in automated error estimation, show that it presents an interesting tradeoff between accuracy and performance

Bit-precise Verification of Numerical Properties in Fixed-point Programs

Numerical software is prone to inaccuracies due to the finite representation of numbers. These inaccuracies propagate, possibly non-linearly, throughout the statements of a program, making it hard to predict the accumulated errors. Moreover, in programs that contain control structures, numerical errors can affect the control flow. As a result of these inaccuracies, reachability, and thus safety, may be altered with respect to the intended infinite-precision computation. This thesis considers programs that use fixed-point arithmetic to compute over non-integer quantities in finite precision. We first define a semantics of fixed-point operations in terms of operations over bit-vectors. The proposed semantics generalizes current attempts to a standardization of fixedpoint arithmetic. We then consider the problem of bit-precise numerical accuracy certification of fixed-point programs with control structures and arithmetic over variables of arbitrary, mixed precision and possibly non-deterministic value. By applying a set of parametrized transformation rules based on computable expressions for the errors incurred by single program statements, we reduce the problem of assessing whether a fixed-point program can exceed a given error bound to a reachability problem in a bit-vector program. We present an experimental evaluation of the certification technique, implemented in a prototype analyzer in a bounded model checking-based verification workflow. Our experiments on a set of fixed-point arithmetic routines commonly used in the industry show that the proposed technique can successfully certify numerical errors and can do so bitprecisely, making it the only such verification technique

Programming with Numerical Uncertainties

Numerical software, common in scientific computing or embedded systems, inevitably uses an approximation of the real arithmetic in which most algorithms are designed. In many domains, roundoff errors are not the only source of inaccuracy and measurement as well as truncation errors further increase the uncertainty of the computed results. Adequate tools are needed to help users select suitable approximations (data types and algorithms) which satisfy their accuracy requirements, especially for safety- critical applications. Determining that a computation produces accurate results is challenging. Roundoff errors and error propagation depend on the ranges of variables in complex and non-obvious ways; even determining these ranges accurately for nonlinear programs poses a significant challenge. In numerical loops, roundoff errors grow, in general, unboundedly. Finally, due to numerical errors, the control flow in the finite-precision implementation may diverge from the ideal real-valued one by taking a different branch and produce a result that is far-off of the expected one. In this thesis, we present techniques and tools for automated and sound analysis, verification and synthesis of numerical programs. We focus on numerical errors due to roundoff from floating-point and fixed-point arithmetic, external input uncertainties or truncation errors. Our work uses interval or affine arithmetic together with Satisfiability Modulo Theories (SMT) technology as well as analytical properties of the underlying mathematical problems. This combination of techniques enables us to compute sound and yet accurate error bounds for nonlinear computations, determine closed-form symbolic invariants for unbounded loops and quantify the effects of discontinuities on numerical errors. We can furthermore certify the results of self-correcting iterative algorithms. Accuracy usually comes at the expense of resource efficiency: more precise data types need more time, space and energy. We propose a programming model where the scientist writes his or her numerical program in a real-valued specification language with explicit error annotations. It is then the task of our verifying compiler to select a suitable floating-point or fixed-point data type which guarantees the needed accuracy. Sometimes accuracy can be gained by simply re-arranging the non-associative finite-precision computation. We present a scalable technique that searches for a more optimal evaluation order and show that the gains can be substantial. We have implemented all our techniques and evaluated them on a number of benchmarks from scientific computing and embedded systems, with promising results