Query Optimization to Improve Performance of the Code Execution

Object-Oriented Programming (OOP) is one of the most successful techniques for abstraction. Bundling together objects into collections of objects, and then operating on these collections, is a fundamental part of main stream object-oriented programming languages. Object querying is an abstraction of operations over collections, whereas manual implementations are performed at low level which forces the developers to specify how a task must be done. Some object-oriented languages allow the programmers to express queries explicitly in the code, which are optimized using the query optimization techniques from the database domain. In this regard, we have developed a technique that performs query optimization at compile-time to reduce the burden of optimization at run-time to improve the performance of the code execution. Keywords- Querying; joins; compile time; run-time; histograms; query optimizatio

Optimization Coaching for JavaScript

The performance of dynamic object-oriented programming languages such as JavaScript depends heavily on highly optimizing just-in-time compilers. Such compilers, like all compilers, can silently fall back to generating conservative, low-performance code during optimization. As a result, programmers may inadvertently cause performance issues on users\u27 systems by making seemingly inoffensive changes to programs. This paper shows how to solve the problem of silent optimization failures. It specifically explains how to create a so-called optimization coach for an object-oriented just-in-time-compiled programming language. The development and evaluation build on the SpiderMonkey JavaScript engine, but the results should generalize to a variety of similar platforms

3D Video Object Detection with Learnable Object-Centric Global Optimization

We explore long-term temporal visual correspondence-based optimization for 3D video object detection in this work. Visual correspondence refers to one-to-one mappings for pixels across multiple images. Correspondence-based optimization is the cornerstone for 3D scene reconstruction but is less studied in 3D video object detection, because moving objects violate multi-view geometry constraints and are treated as outliers during scene reconstruction. We address this issue by treating objects as first-class citizens during correspondence-based optimization. In this work, we propose BA-Det, an end-to-end optimizable object detector with object-centric temporal correspondence learning and featuremetric object bundle adjustment. Empirically, we verify the effectiveness and efficiency of BA-Det for multiple baseline 3D detectors under various setups. Our BA-Det achieves SOTA performance on the large-scale Waymo Open Dataset (WOD) with only marginal computation cost. Our code is available at https://github.com/jiaweihe1996/BA-Det.Comment: CVPR202