XScan: An Integrated Tool for Understanding Open Source Community-Based Scientific Code

Many scientific communities have adopted community-based models that integrate multiple components to simulate whole system dynamics. The community software projects’ complexity, stems from the integration of multiple individual software components that were developed under different application requirements and various machine architectures, has become a challenge for effective software system understanding and continuous software development. The paper presents an integrated software toolkit called X-ray Software Scanner (in abbreviation, XScan) for a better understanding of large-scale community-based scientific codes. Our software tool provides support to quickly summarize the overall information of scientific codes, including the number of lines of code, programming languages, external library dependencies, as well as architecture-dependent parallel software features. The XScan toolkit also realizes a static software analysis component to collect detailed structural information and provides an interactive visualization and analysis of the functions. We use a large-scale community-based Earth System Model to demonstrate the workflow, functions and visualization of the toolkit. We also discuss the application of advanced graph analytics techniques to assist software modular design and component refactoring

On the Strong Correlation Between Model Invariance and Generalization

Generalization and invariance are two essential properties of any machine learning model. Generalization captures a model's ability to classify unseen data while invariance measures consistency of model predictions on transformations of the data. Existing research suggests a positive relationship: a model generalizing well should be invariant to certain visual factors. Building on this qualitative implication we make two contributions. First, we introduce effective invariance (EI), a simple and reasonable measure of model invariance which does not rely on image labels. Given predictions on a test image and its transformed version, EI measures how well the predictions agree and with what level of confidence. Second, using invariance scores computed by EI, we perform large-scale quantitative correlation studies between generalization and invariance, focusing on rotation and grayscale transformations. From a model-centric view, we observe generalization and invariance of different models exhibit a strong linear relationship, on both in-distribution and out-of-distribution datasets. From a dataset-centric view, we find a certain model's accuracy and invariance linearly correlated on different test sets. Apart from these major findings, other minor but interesting insights are also discussed.Comment: 18 pages, 11 figures; this version is not fully edited and will be updated soo

TeV Scale Phenomenology of e+e−→μ+μ−e^+e^- \to\mu^+ \mu^- Scattering in the Noncommutative Standard Model with Hybrid Gauge Transformation

The hybrid gauge transformation and its nontrivial phenomenological implications are investigated using the noncommutative gauge theory with the Seiberg-Witten map expanded scenario. Particularly, the $e^+e^- \to\mu^+ \mu^-$ process is studied with a generalized noncommutative standard model (NCSM) including massive neutrinos and neutrino-photon interaction. In this model, the hybrid gauge transformation in the lepton sector is naturally introduced through the requirement of gauge invariance of the seesaw neutrino mass term. It is shown that in the NCSM without hybrid gauge transformation the noncommutative correction to the scattering amplitude of the $e^+e^- \to\mu^+ \mu^-$ process appears only as a phase factor, predicting no new physical deviation in the cross section. However, when the hybrid feature is considered, the noncommutative effect appears in the single channel process. The cross section and angular distribution are analyzed in the laboratory frame including Earth's rotation. It is proposed that pair production of muons in the upcoming TeV International Linear Collider (ILC) can provide an ideal opportunity for exploring not only the NC space-time, but also the mathematical structure of the corresponding gauge theory.Comment: 22 pages, 8 figure

suCAQR: A Simplified Communication-Avoiding QR Factorization Solver Using the TBLAS Framework

The scope of this paper is to design and implement a scalable QR factorization solver that can deliver the fastest performance for tall and skinny matrices and square matrices on modern supercomputers. The new solver, named scalable universal communication-avoiding QR factorization (suCAQR), introduces a simplified and tuning-less way to realize the communication-avoiding QR factorization algorithm to support matrices of any shapes. The software design includes a mixed usage of physical and logical data layouts, a simplified method of dynamic-root binary-tree reduction, and a dynamic dataflow implementation. Compared with the existing communication avoiding QR factorization implementations, suCAQR has the benefits of being simpler, more general, and more efficient. By balancing the degree of parallelism and the proportion of faster computational kernels, it is able to achieve scalable performance on clusters of multicore nodes. The software essentially combines the strengths of both synchronization-reducing approach and communication-avoiding approach to achieve high performance. Based on the experimental results using 1,024 CPU cores, suCAQR is faster than DPLASMA by up to 30%, and faster than ScaLAPACK by up to 30 times