Day 2 Registration

Numerical codes require visualization for effective debugging and analysis due to the complexity of the problems involved. The coding errors cannot be easily revealed without visualization. We'll demonstrate how to integrate the system for calculation and analysis using Python, C++ and Qt.

Profiling is widely used to analyze a program’s runtime performance characteristics. It collects performance metric such as CPU usage, function call frequencies and generates reports, graphs, or visualizations that highlight hotspots. There are two kinds of profiling tools, non-intrusive profiling and intrusive profiling. The former gathers information about the program’s behavior by periodically observe the program’s state without modifying its code, the later involves modifying the program’s code or behavior to gather performance data, including source code instrumentation or binary rewriting. However, most of the existing profiling tools are sampling based such as linux perf, meaning that it can’t capture precise hit count of each function being executed. In this proposal, I would like to talk about how linux perf tool gathers performance metric and how can one benefit from it to build a source code level profiling tool for a C++ application.

形形色色的星體有各自的故事。他們如何開始旅程？又經歷了什麼因而轉變？星空舞台上演的故事與天文觀測資料息息相關，但目前的天文基礎教育及科普推廣，很少有機會讓學生及大眾認識探索這些資料。在此演講中，我將分享我如何透過開發基於Streamlit的天文資料互動教材、在高中舉辦天文黑客松，以及撰寫「天聞的資料科學」專欄文章，來引導大眾探索埋在天文資料裡的故事。

Day 2 Lunch

We present fundamental theories of image stitching, using MATLAB based on feature detection and projection. Our technique, enhanced through mismatch detection, smooth blending, and reshaping, creates flawless panoramas. Impressively, it processes 0.23 times faster than Photoshop.

Break

In this talk, we will introduce tensor network and our tensor network library Cytnx. We first introduce the graphical tensor notation and provide simple examples showing how to use Cytnx to implement a tensor contraction. We then explain in detail the basic elements in Cytnx, and how to perform tensor contractions and tensor decompositions. We then show benchmark the performance of Cytnx with another tensor library ITensor. In the end we will give a summary and discuss future goals of the library.

Closing Remark