Program optimization

Summary

In computer science, program optimization, code optimization, or software optimization, is the process of modifying a software system to make some aspect of it work more efficiently or use fewer resources. In general, a computer program may be optimized so that it executes more rapidly, or to make it capable of operating with less memory storage or other resources, or draw less power. General Although the word "optimization" shares the same root as "optimal", it is rare for the process of optimization to produce a truly optimal system. A system can generally be made optimal not in absolute terms, but only with respect to a given quality metric, which may be in contrast with other possible metrics. As a result, the optimized system will typically only be optimal in one application or for one audience. One might reduce the amount of time that a program takes to perform some task at the price of making it consume more memory. In an application where memory space is at a premiu

Official source

https://en.wikipedia.org/wiki/Program_optimization

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Visualisation of high-resolution 3D images on a web application

Matthieu François Edgard Rudelle

The web is a complicated world where users are demanding and where small optimizations can make a big difference. Users want the information fast, but they also want the best quality. In terms of images, there is a well documented solution to that; Virtual Texturing. But when it comes to browsers and web application very few documentation exist and they lack technical details. This leads to my project which consists in the implementation of a Virtual Texturing engine for a web application. And this paper documents the details of my journey.

2015

This is my master thesis done at PPL in Stanford under the supervision of Prof. Kunle Olukotun. It improved LMS, a framework for embedding DSLs (domain-specific languages) into Scala which features many general optimizations that can be used by any DSLs for free. I implemented a more powerful and cleaner version of the loop fusion optimization from the compiler world. Loop fusion is an important performance optimization for all languages that feature list comprehensions and translate their high-level operations into loop-based representations. It can decrease runtime, memory footprint and code size through two different fusion cases: The simpler one is called horizontal or side-by-side fusion and fuses adjacent loops iterating over the same range, enabling further optimizations. The second one is vertical or pipeline fusion, where a producer and a consumer of data are fused, removing the need for the intermediate data structure.

2014

The potential energy savings resulting from the cooperative management of community districts, also known as energy hubs, has been widely demonstrated throughout the literature. Various developed predictive control strategies have proven to generate remarkable gains by exploiting the shared energy generation, conversion and storage devices of building communities. However, one difficulty amongst these methods lie in the integration of information regarding the long term perturbations brought to the system. While most of the existing work considers prediction horizons ranging from day ahead to weekly time frames, recent studies have explored the control of inter-seasonal storage units on a yearly basis through available historical data sets. This study focuses on testing and improving the existing methods while integrating combined and stand alone wind generation to the system. In particular, the contribution of the seasonal storage state bounds for Value function approximation and control process has been investigated for several well-known methods, including scenario approach, stochastic dual dynamic programing and adaptive dynamic programing. Solving the resulting multistage stochastic optimization problem reveals very good results and demonstrate the contributions of the bounds to almost all Value function approximation methods. In fine, the integration of wind production to the system underlines the importance of seasonal trends for efficient optimization of long term storage and suggests using tolerance margins around the bounds for systems particularly sensitive to perturbations.

2018