We introduce in this paper, HeteroSTA, the first CPU-GPU heterogeneous timing analysis engine that efficiently supports: (1) a set of delay calculation models providing versatile accuracy-speed choices without relying on an external golden tool, (2) robust support for industry formats, including especially the.sdc constraints containing all common timing exceptions, clock domains, and case analysis modes, and (3) end-to-end GPU-acceleration for both graph-based and path-based timing queries, all exposed as a zero-overhead flattened heterogeneous application programming interface (API). HeteroSTA is publicly available with both a standalone binary executable and an embeddable shared library targeting ubiquitous academic and industry applications. Example use cases as a standalone tool, a timing-driven DREAMPlace 4.0 integration, and a timing-driven global routing integration have all demonstrated remarkable runtime speed-up and comparable quality.

Static timing analysis (STA) plays an essential role in VLSI design optimization. While CPU-based incremental STA methods reduce computational overhead by selectively updating affected circuit regions, and GPU-accelerated engines improve full-circuit analysis throughput, effectively combining these approaches has remained challenging. Existing solutions offer only partial incrementality, either switching to CPU processing for small modifications or handling solely delay value changes without supporting structural updates. We introduce IncreGPUSTA, a novel GPU-accelerated incremental STA algorithm with dual-CSR data structures and incremental levelization that efficiently processes timing updates for both localized and structural modifications. Experimental results on industrial benchmarks demonstrate speedups of up to 3.06× over GPU full Timer and up to 72.50× over CPU incremental Timer for million-scale designs.

Along with the continuous development of capital markets and intelligent finance technologies, quantitative investment is entering into the most critical and challenging area – fundamental quantitative investment. So far, quantitative investment has been focused on automation of technical analysis and trading, while fundamental investment has been large discretionary. This paper provides an overview of quantitative investment and fundamental investment towards a fundamental quantitative investment theory and technical system based on multi-factor models. We start with reviewing relevant literature on modern financial quantitative investment and fundamental investment. Then we cover the theoretical basis and development of multi-factor models and their applications for stock selection, involving linear and non-linear relationships, machine learning, deep learning with neural networks, random forests, and Support Vector Machines (SVMs). We explore the frontiers of fundamental quantitative investment and shed light on the future research prospects.