Existing search heuristics used to find input values that result in significant floating-point (FP)
errors or small ranges that cover them are accompanied by severe constraints, complicating …

Compiler-induced numerical inconsistencies present a significant challenge when testing
and verifying numerical software—they can arise in a variety of situations, such as when …

Aggregated roundoff errors caused by floating-point arithmetic can make numerical code
highly unreliable. Verified postconditions for floating-point functions can guarantee the …

Numerical software has many forms: from analyzing our heartbeat to simulations predicting
earthquakes. Some of these programs must produce very accurate results, as the cost of an …

Density Functional Theory (DFT) is used extensively in the computation of electronic
properties of matter, with applications in solid state physics, computational chemistry, and …

Floating-point precision tuning (FPPT) searches target programs for computations amenable
to reduced-precision, thereby trading accuracy for performance. FPPT does so by searching …

We introduce a floating-point (FP) error optimization approach called Arfa that partitions the
domain D of an FP expression fe into regimes and rewrites fe in each regime where fe …

Numerical programs are ubiquitous across many domains, including embedded systems,
scientific computing, and machine learning. These programs utilize finite precision …