tuning of code variants, or alternative implementations of the same computation. Nitro provides a library interface that permits programmers to express code variants along with meta-information that aids the system in selecting among the set of variants at run time. Machine learning is employed to build

present a framework for improving duplicate detection using trainable measures of textual similarity. We propose to employ learnable text distance functions for each database field, and show that such measures are capable of adapting to the specific notion of similarity that is appropriate for the field

Abstract—In this paper, we present a runtime compilation and tuning framework for parallel programs. We extend our prior work on our auto-tuner, Active Harmony, for tunable parameters that require code generation (for example, different unroll factors). For such parameters, our auto-tuner generates

Abstract—We present an automatic, empirically tuned code genenrator for Real/Complex FFT and Trigonometric Transforms. The code generator is part of an adaptive and portable FFT computation framework- UHFFT. Performance portability over varying architectures is achieved by generating highly

, we propose a novel framework for dynamic program optimization, ADAPT (Automated De-coupled Adaptive Program Transformation) , that builds on the strengths of existing approaches. The key to our framework is the de-coupling of the dynamic compilation of new code variants from the dynamic selection

framework takes a most concise specification of stencil behavior from the user as a single formula, auto-generates tunable code from it, systematically searches for the best configuration and generates the code with optimal parameter configurations for different GPUs. This auto-tuning approach guarantees

existing polyhedral transformation models. This framework is a key element of a compiler we are developing which performs empirical optimization to evaluate a collection of alternative optimized variants of a code segment. A script interface to code generation and empirical search permits transformation

wisely profiled Dirac’s entropy coding statistics and tuned the initialisation and adaptation parameters the impact on the compression is very limited. Key-Words: Dirac video codec, arithmetic coding, M-coder 1

capabilities, and adaptive enforcement. We present FLIPS (Feedback Learning IPS), a hybrid approach to host security that prevents binary code injection attacks. It incorporates three major components: an anomaly-based classifier, a signature-based filtering scheme, and a supervision framework that employs

benchmark, SMG2000, a semi-coarsening multigrid on structured grids. We show that the proposed system first extracts computationally-intensive loop nests into separate executable functions, a code transformation called outlining. The outlined loop nests are then tuned by the framework and subsequently