When a computational task tolerates a relaxation of its specification or when an algorithm
tolerates the effects of noise in its execution, hardware, system software, and programming …

Today's floating-point arithmetic landscape is broader than ever. While scientific computing
has traditionally used single precision and double precision floating-point arithmetics, half …

The use of reduced precision to improve performance metrics such as computation latency
and power consumption is a common practice in the embedded systems field. This practice …

Scientific and engineering applications depend on floating point arithmetic to approximate
real arithmetic. This approximation introduces rounding error, which can accumulate to …

The performance of CPU-based and GPU-based systems is often low for PDE codes, where
large, sparse, and often structured systems of linear equations must be solved. Iterative …

Virtually all real-valued computations are carried out using floating-point data types and
operations. The precision of these data types must be set with the goals of reducing the …

The aggressive optimization of floating-point computations is an important problem in high-
performance computing. Unfortunately, floating-point instruction sets have complicated …

In modern low-power embedded platforms, the execution of floating-point (FP) operations
emerges as a major contributor to the energy consumption of compute-intensive …

Approximate computing trades off accuracy for better performance and energy efficiency. It
offers promising optimization opportunities for a wide variety of modern applications, from …

As parallel applications become more complex, auto-tuning becomes more desirable,
challenging, and time-consuming. We propose, Bliss, a novel solution for auto-tuning …