Since the mid-1990s, researchers have been trying to use machine-learning-based
approaches to solve a number of different compiler optimization problems. These …

Binary code fingerprinting is crucial in many security applications. Examples include
malware detection, software infringement, vulnerability analysis, and digital forensics. It is …

Program autotuning has been shown to achieve better or more portable performance in a
number of domains. However, autotuners themselves are rarely portable between projects …

Many modern computations (such as video and audio encoders, Monte Carlo simulations,
and machine learning algorithms) are designed to trade off accuracy in return for increased …

Building on concepts drawn from control theory, self-adaptive software handles
environmental and internal uncertainties by dynamically adjusting its architecture and …

We present PowerDial, a system for dynamically adapting application behavior to execute
successfully in the face of load and power fluctuations. PowerDial transforms static …

In this paper, we address the question of how to automatically map computational kernels to
highly efficient code for a wide range of computing platforms and establish the correctness of …

This dissertation presents an automated system to generate highly efficient, platform-
adapted implementations of sparse matrix kernels. We show that conventional …

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

Approximating ideal program outputs is a common technique for solving computationally
difficult problems, for adhering to processing or timing constraints, and for performance …