Best-Effort Lazy Evaluation for Python Software Built on APIs

This paper focuses on an important optimization opportunity in Python-hosted domain-specific languages (DSLs): the use of laziness for optimization, whereby multiple API calls are deferred and then optimized prior to execution (rather than executing eagerly, which would require executing each call in isolation). In existing supports of lazy evaluation, laziness is "terminated" as soon as control passes back to the host language in any way, limiting opportunities for optimization. This paper presents Cunctator, a framework that extends this laziness to more of the Python language, allowing intermediate values from DSLs like NumPy or Pandas to flow back to the host Python code without triggering evaluation. This exposes more opportunities for optimization and, more generally, allows for larger computation graphs to be built, producing 1.03-14.2X speedups on a set of programs in common libraries and frameworks

Towards Ontology-Based Program Analysis

Program analysis is fundamental for program optimizations, debugging, and many other tasks. But developing program analyses has been a challenging and error-prone process for general users. Declarative program analysis has shown the promise to dramatically improve the productivity in the development of program analyses. Current declarative program analysis is however subject to some major limitations in supporting cooperations among analysis tools, guiding program optimizations, and often requires much effort for repeated program preprocessing. In this work, we advocate the integration of ontology into declarative program analysis. As a way to standardize the definitions of concepts in a domain and the representation of the knowledge in the domain, ontology offers a promising way to address the limitations of current declarative program analysis. We develop a prototype framework named PATO for conducting program analysis upon ontology-based program representation. Experiments on six program analyses confirm the potential of ontology for complementing existing declarative program analysis. It supports multiple analyses without separate program preprocessing, promotes cooperative Liveness analysis between two compilers, and effectively guides a data placement optimization for Graphic Processing Units (GPU)

Study on the Influence of Ultrasonic Vibration on the Specific Energy of Sawing Ceramic

AbstractThe hard as well as brittle constituents are typically difficult-to-machined materials, and this character upsurges the machining cost. Many non-traditional machining methods were developed to improve its cost-effectiveness. Ultrasonic vibration assisted grinding has been improved the processing performance of a variety of brittle materials, and achieved good results in processing application. In this study, engineering ceramic was precisely sawn using a thin diamond blade with or without ultrasonic vibration conditions. During the sawing process, the specific sawing energy was investigated with the measurement of sawing forces to explore the influence of ultrasonic vibration. The results showed that the ultrasonic vibration made a significant reduction in specific sawing energy. The specific sawing energy decreased with the increase of the maximum undeformed chip thickness in both the sawing conditions; however ultrasonic vibration changed the trend of specific sawing energy in normal cutting mode from exponentially decreasing to a good linear decreasing. Under the ultrasonic vibration assisted sawing condition, the impact of the diamond grain on the engineering ceramic caused to much more material removal in brittle fracture mode. The reducing of the plastic transformation also reduced the energy consumption during the engineering ceramic sawing process