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Sparse matrix-vector multiplication (SpMV) is a crucial computing kernel with widespread applications in iterative algorithms. Over the past decades, research on SpMV optimization has made remarkable strides, giving rise to various optimization contributions. However, the comprehensive and systematic literature survey that introduces, analyzes, discusses, and summarizes the advancements of SpMV in recent years is currently lacking. Aiming to fill this gap, this paper compares existing techniques and analyzes their strengths and weaknesses. We begin by highlighting two representative applications of SpMV, then conduct an in-depth overview of the important techniques that optimize SpMV on modern architectures, which we specifically classify as classic, auto-tuning, machine learning, and mixed-precision-based optimization. We also elaborate on the hardware-based architectures, including CPU, GPU, FPGA, processing in Memory, heterogeneous, and distributed platforms. We present a comprehensive experimental evaluation that compares the performance of state-of-the-art SpMV implementations. Based on our findings, we identify several challenges and point out future research directions. This survey is intended to provide researchers with a comprehensive understanding of SpMV optimization on modern architectures and provide guidance for future work., Comment: 34 pages, 18 figures, 16 tables

Community smells appear in sub-optimal software development community structures, causing unforeseen additional project costs, e.g., lower productivity and more technical debt. Previous studies analyzed and predicted community smells in the granularity of community sub-groups using socio-technical factors. However, refactoring such smells requires the effort of developers individually. To eliminate them, supportive measures for every developer should be constructed according to their motifs and working states. Recent work revealed developers' personalities could influence community smells' variation, and their sentiments could impact productivity. Thus, sentiments could be evaluated to predict community smells' occurrence on them. To this aim, this paper builds a developer-oriented and sentiment-aware community smell prediction model considering 3 smells such as Organizational Silo, Lone Wolf, and Bottleneck. Furthermore, it also predicts if a developer quitted the community after being affected by any smell. The proposed model achieves cross- and within-project prediction F-Measure ranging from 76% to 93%. Research also reveals 6 sentimental features having stronger predictive power compared with activeness metrics. Imperative and indicative expressions, politeness, and several emotions are the most powerful predictors. Finally, we test statistically the mean and distribution of sentimental features. Based on our findings, we suggest developers should communicate in a straightforward and polite way., Comment: Research paper accepted at ICPC 2021 (29th IEEE/ACM International Conference on Program Comprehension)

General Sparse Matrix-Matrix Multiplication (SpGEMM) has attracted much attention from researchers in graph analyzing, scientific computing, and deep learning. Many optimization techniques have been developed for different applications and computing architectures over the past decades. The objective of this paper is to provide a structured and comprehensive overview of the researches on SpGEMM. Existing researches have been grouped into different categories based on target architectures and design choices. Covered topics include typical applications, compression formats, general formulations, key problems and techniques, architecture-oriented optimizations, and programming models. The rationales of different algorithms are analyzed and summarized. This survey sufficiently reveals the latest progress of SpGEMM research to 2021. Moreover, a thorough performance comparison of existing implementations is presented. Based on our findings, we highlight future research directions, which encourage better design and implementations in later studies., Comment: 37 pages, 20 figures, 11 tables, 1 algorithm

The competition, coexistence and cooperation of various orders in low-dimensional materials like spin, charge, topological orders and charge-density-wave has been one of the most intriguing issues in condensed matter physics. In particular, layered transition metal dichalcogenides provide an ideal platform for studying such an interplay with a notable case of 1${T}$-TaS$_{2}$ featuring Mott-insulating ground state, charge-density-wave, spin frustration and emerging superconductivity together. We investigated local electronic states of Se-substituted 1${T}$-TaS$_{2}$ by scanning tunneling microscopy/spectroscopy (STM/STS), where superconductivity emerges from the unique Mott-CDW state. Spatially resolved STS measurements reveal that an apparent V-shape pseudogap forms at the Fermi Level (E$_{F}$), with the origin of the electronic states splitting and transformation from the Mott states, and the CDW gaps are largely preserved. The formation of the pseudogap has little correlation to the variation of local Se concentration, but appears to be a global characteristics. Furthermore, the correlation length of local density of states (LDOS) diverges at the Fermi energy and decays rapidly at high energies. The spatial correlation shows a power-law decay close to the Fermi energy. Our statistics analysis of the LDOS indicates that our system exhibits weak multifractal behavior of the wave functions. These findings strongly support a correlated metallic state induced by disorder in our system, which provides an new insight into the novel mechanism of emerging superconductivity in the two-dimensional correlated electronic systems.