swTVM: Exploring the Automated Compilation for Deep Learning on Sunway Architecture

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the exiting deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific and deep learning applications. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient code for deep learning application on Sunway. The experimental results show the ability of swTVM to automatically generate code for various deep neural network models on Sunway. The performance of automatically generated code for AlexNet and VGG-19 by swTVM achieves 6.71x and 2.45x speedup on average than hand-optimized OpenACC implementations on convolution and fully connected layers respectively. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and high performance architecture particularly with productivity and efficiency in mind. We would like to open source the implementation so that more people can embrace the power of deep learning compiler and Sunway many-core processor

Dynamics of the Geometric Phase in Inhomogeneous Quantum Spin Chains

The dynamics of the geometric phase are studied in inhomogeneous quantum spin chains after a quench. Analytic expressions of the Pancharatnam geometric phase (PGP) $\mathcal{G}(t)$ are derived, for both the period-two quantum Ising chain (QIC) and the disordered QIC. In the period-two QIC, due to the periodic modulation, the PGP changes with time at the boundary of the Brillouin zone, and consequently, the winding number $\nu_{D}(t)=\int_{0}^{\pi}[\partial\phi_{k}^{G}(t)/\partial k]dk/2\pi$ based on the PGP is not quantized and thus not topological anymore. Nevertheless, the PGP and its winding number show non-analytic singularities at the critical times of the dynamical quantum phase transitions (DQPTs). This relation between the PGP and the DQPT is further confirmed in the disordered QIC, where the winding number is not defined. It is found that the critical time of DQPT inherited from the homogeneous system and the additional one induced by the weak disorder are also accompanied by the non-analytic singularity of the PGP, by decomposing the PGP into each quasiparticle mode. The connection between the non-analytic behavior of the PGP at the critical time and the DQPT, regardless of whether the winding number is topological, can be explained by the fact that they both arise when the Loschmidt amplitude vanishes.Comment: 14 pages, 8 figure

MicroRNA-128b mediates lipopolysaccharide-induced apoptosis via reactive oxygen species in human pulmonary microvascular endothelial cells

Objectives: This study aimed to explore the effects of miR-128b in the regulation of Lipopolysaccharide (LPS) induced apoptosis. Methods: Human Pulmonary Microvascular Endothelial Cells (HPMECs) were transfected with an miR-128b inhibitor and stimulated with LPS for 24 h. FCM was performed to detect apoptosis and Reactive Oxygen Species (ROS) production. In addition, miRNA and caspase-3 expression levels were determined using real-time quantitative polymerase chain reaction and western blotting. Results: LPS significantly induced apoptosis and ROS production and upregulated miR-128b and caspase-3 expressions in HPMECs. However, LPS-induced effects were suppressed when an miR-128b inhibitor was used. Preincubation with NAC decreased the LPS-induced apoptosis of HPMECs. Conclusions: These effects were mediated by miR-128b via the caspase-3 pathway

Nonuniform-spaced Critical Behavior of Dynamical Quantum Phase Transitions in Multi-band Bloch Hamiltonian

We investigate the dynamical quantum phase transition (DQPT) in the multi-band Bloch Hamiltonian of the one-dimensional periodic Kitaev model after a quench from a Bloch band. Our study goes beyond the limitations of previous works that primarily focused on two-band models and reveals significant differences in DQPT between the two-band and multi-band systems. Our results show that only the quench from the Bloch states, which causes the band gap to collapse at the critical point, induces the DQPT after crossing the quantum phase transition; otherwise, the DQPT will not occur. Additionally, the critical times of the DQPT are not evenly spaced due to the deviation in the critical momentum caused by the non-analytic singularities of the Pancharatnam geometric phase. Our findings provide a better understanding of the characteristics of non-equilibrium systems surrounding DQPTs.Comment: 9 pages, 10 figure

Dynamical relaxation behavior of extended XY chain with gapless phase following a quantum quench

We investigate the dynamical relaxation behavior of the two-point correlation in extended XY models with a gapless phase after quenches from various initial states. Specifically, we study the XY chain with gapless phase induced by the additional interactions: Dzyaloshinskii-Moriya interaction and XZY-YZX type of three-site interaction. When quenching from the gapped phase, we observe that the additional interactions have no effect on the relaxation behavior. The relaxation behavior is $\delta C_{mn}(t)\sim t^{-3/2}$ and $\sim t^{-1/2}$ for the quench to the commensurate phase and the incommensurate phase, respectively. However, when quenching from the gapless phase, we demonstrate that the scaling behavior of $\delta C_{mn}(t)$ is changed to $\sim t^{-1}$ for the quench to the commensurate phase, and the decay of $\delta C_{mn}(t)$ follows $\sim t^{-1}$ or $\sim t^{-1/2}$ for the quench to the incommensurate phase depending on the parameters of pre-quench Hamiltonian. We also establish the dynamical phase diagrams based on the dynamical relaxation behavior of $\delta C_{mn}(t)$ in the extended XY models.Comment: 12 pages, 10 figure

Efficient Climate Simulation via Machine Learning Method

Hybrid modeling combining data-driven techniques and numerical methods is an emerging and promising research direction for efficient climate simulation. However, previous works lack practical platforms, making developing hybrid modeling a challenging programming problem. Furthermore, the lack of standard data sets and evaluation metrics may hamper researchers from comprehensively comparing various algorithms under a uniform condition. To address these problems, we propose a framework called NeuroClim for hybrid modeling under the real-world scenario, a basic setting to simulate the real climate that we live in. NeuroClim consists of three parts: (1) Platform. We develop a user-friendly platform NeuroGCM for efficiently developing hybrid modeling in climate simulation. (2) Dataset. We provide an open-source dataset for data-driven methods in hybrid modeling. We investigate the characteristics of the data, i.e., heterogeneity and stiffness, which reveals the difficulty of regressing climate simulation data; (3) Metrics. We propose a methodology for quantitatively evaluating hybrid modeling, including the approximation ability of machine learning models and the stability during simulation. We believe that NeuroClim allows researchers to work without high level of climate-related expertise and focus only on machine learning algorithm design, which will accelerate hybrid modeling research in the AI-Climate intersection. The codes and data are released at https://github.com/x-w19/NeuroClim.Comment: Work in progres