Performance Optimization of Many-core Systems by Exploiting Task Migration and Dark Core Allocation

As an effective scheme often adopted for performance tuning in many-core processors, task migration provides an opportunity for "hot" tasks to be migrated to run on a "cool" core that has a lower temperature. When a task needs to migrate from one processor core to another, the migration can embark on numerous modes defined by the migration paths undertaken and/or the destinations of the migration. Selecting the right migration mode that a task shall follow has always been difficult, and it can be more challenging with the existence of dark cores that can be called back to service (reactivated), which ushers in additional task migration modes. Previous works have demonstrated that dark cores can be placed near the active cores to reduce power density so that the active cores can run at higher voltage/frequency levels for higher performance. However, the existing task migration schemes neither consider the impact of dark cores on each application's performance, nor exploit performance trade-off under different migration modes. Unlike the existing task migration schemes, in this paper, a runtime task migration algorithm that simultaneously takes both migration modes and dark cores into consideration is proposed, and it essentially has two major steps. In the first step, for a specific migration mode that is tied to an application whose tasks need to be migrated, the number of dark cores is determined so that the overall performance is maximized. The second step is to find an appropriate core region and its location for each application to optimize the communication latency and computation performance; during this step, focus is placed on reducing the fragmentation of the free core regions resulting from the task migration. Experimental results have confirmed that our approach achieves over 50% reduction in total response time when compared to recently proposed thermal-aware runtime task migration approachess

Unsupervised learning of ferroic variants from atomically resolved STEM images

An approach for the analysis of atomically resolved scanning transmission electron microscopy data with multiple ferroic variants in the presence of imaging non-idealities and chemical variabilities based on a rotationally invariant variational autoencoder (rVAE) is presented. We show that an optimal local descriptor for the analysis is a sub-image centered at specific atomic units, since materials and microscope distortions preclude the use of an ideal lattice as a reference point. The applicability of unsupervised clustering and dimensionality reduction methods is explored and is shown to produce clusters dominated by chemical and microscope effects, with a large number of classes required to establish the presence of rotational variants. Comparatively, the rVAE allows extraction of the angle corresponding to the orientation of ferroic variants explicitly, enabling straightforward identification of the ferroic variants as regions with constant or smoothly changing latent variables and sharp orientational changes. This approach allows further exploration of the chemical variability by separating the rotational degrees of freedom via rVAE and searching for remaining variability in the system. The code used in this article is available at https://github.com/saimani5/ferroelectric_domains_rVAE

The interrelationship between the carbon market and the green bonds market: Evidence from wavelet quantile-on-quantile method

The 26th edition of the United Nations climate change conference (COP26) underlines the importance of financial products and markets related to “carbon” (e.g., carbon and green bond markets). We, to our knowledge, are the first to construct a framework based on multiple time scales and market conditions to quantify the interrelationship between the carbon futures and green bond markets. Specifically, we estimate it from short-, medium-, and long-term perspectives and different market conditions by combining the maximum overlap discrete wavelet transform (MODWT) and two quantile methods to decompose the sequences into various frequencies and quantiles. We find that the carbon futures price unilaterally Granger causes the green bond index and empirically analyzes the asymmetric impact of the carbon futures with a two-dimensional quantile model constructed by the quantile-on-quantile (QQ) regression approach. We find positive effects of the carbon futures in the medium to long term and erratic performance in the short term. The effects are more pronounced when both markets are in an extreme state. Our findings enrich the research related to eco-economy and carbon finance, providing a more comprehensive and detailed research framework, and helping others optimize investment portfolios and policy arrangements

Analysis of Surface Energy Changes over Different Underlying Surfaces Based on MODIS Land-Use Data and Green Vegetation Fraction over the Tibetan Plateau

To better predict and understand land–atmospheric interactions in the Tibetan Plateau (TP), we used Moderate Resolution Imaging Spectroradiometer (MODIS)-based land-use data and the MODIS-derived green vegetation fraction (GVF) to analyze the variation trend over the TP. The in situ observations from six flux stations (“BJ” (the BJ site of Nagqu Station of Plateau Climate and Environment), “MAWORS” (the Muztagh Ata Westerly Observation and Research Station), “NADORS” (the Ngari Desert Observation and Research Station), “NAMORS” (the Nam Co Monitoring and Research Station for Multisphere Interactions), “QOMS” (the Qomolangma Atmospheric and Environmental Observation and Research Station), and “SETORS” (the Southeast Tibet Observation and Research Station for the Alpine Environment)) at the Chinese TP Scientific Data Center were used to study the surface energy variation characteristics and energy distribution over different underlying surfaces. Finally, we used observation data to verify the applicability of the ERA-5 land reanalysis data to the TP. The results showed that the annual GVF steadily declined from the southeast parts to the northwest parts of the TP, and the vegetation coverage rate was highest from June to September. The sensible heat flux (H), latent heat flux (LE), net surface radiation (Rn), and four-component radiation (solar downward shortwave radiation (Rsd), surface upward shortwave radiation (Rsu), atmospheric downward longwave radiation (Rld), and surface upward longwave radiation (Rlu)) reached their maxima in summer at each station. Rld did not change significantly with time; all other variables increased during the day and decreased at night. The interannual variation in H and LE shows that latent heat exchange was the dominant form of energy transfer in BJ, MAWORS, NAMORS, and SETORS. By contrast, sensible heat exchange was the main form of energy transfer in NADORS and QOMS. The Bowen ratio was generally low in summer, and some sites had a maximum in spring. The surface albedo exhibited a “U” shape, decreasing in spring and summer, and increasing in autumn and winter, and reaching the lowest value at noon. Except for SETORS, ERA-5 Land data and other flux stations had high simulation accuracy and correlation. Regional surface energy changes were mainly observed in the eastern and western parts of the TP, except for the maximum of H in spring; the maximum values of other heat fluxes were concentrated in summer

Fault Slip Model of the 2018 Mw 6.6 Hokkaido Eastern Iburi, Japan, Earthquake Estimated from Satellite Radar and GPS Measurements

In this study, Sentinel-1 and Advanced Land Observation Satellite-2 (ALOS-2) interferometric synthetic aperture radar (InSAR) and global positioning system (GPS) data were used to jointly determine the source parameters and fault slip distribution of the Mw 6.6 Hokkaido eastern Iburi, Japan, earthquake that occurred on 5 September 2018. The coseismic deformation map obtained from the ascending and descending Sentinel-1 and ALOS-2 InSAR data and GPS data is consistent with a thrust faulting event. A comparison between the InSAR-observed and GPS-projected line-of-sight (LOS) deformation suggests that descending Sentinel-1 track T046D, descending ALOS-2 track P018D, and ascending ALOS-2 track P112A and GPS data can be used to invert for the source parameters. The results of a nonlinear inversion show that the seismogenic fault is a blind NNW-trending (strike angle ~347.2°), east-dipping (dip angle ~79.6°) thrust fault. On the basis of the optimal fault geometry model, the fault slip distribution jointly inverted from the three datasets reveals that a significant slip area extends 30 km along the strike and 25 km in the downdip direction, and the peak slip magnitude can approach 0.53 m at a depth of 15.5 km. The estimated geodetic moment magnitude released by the distributed slip model is 6.16   × 10 18   N · m , equivalent to an event magnitude of Mw 6.50, which is slightly smaller than the estimates of focal mechanism solutions. According to the Coulomb stress change at the surrounding faults, more attention should be paid to potential earthquake disasters in this region in the near future. In consideration of the possibility of multi-fault rupture and complexity of regional geologic framework, the refined distributed slip and seismogenic mechanism of this deep reverse faulting should be investigated with multi-disciplinary (e.g., geodetic, seismic, and geological) data in further studies

Analysis of Surface Energy Changes over Different Underlying Surfaces Based on MODIS Land-Use Data and Green Vegetation Fraction over the Tibetan Plateau

To better predict and understand land–atmospheric interactions in the Tibetan Plateau (TP), we used Moderate Resolution Imaging Spectroradiometer (MODIS)-based land-use data and the MODIS-derived green vegetation fraction (GVF) to analyze the variation trend over the TP. The in situ observations from six flux stations (“BJ” (the BJ site of Nagqu Station of Plateau Climate and Environment), “MAWORS” (the Muztagh Ata Westerly Observation and Research Station), “NADORS” (the Ngari Desert Observation and Research Station), “NAMORS” (the Nam Co Monitoring and Research Station for Multisphere Interactions), “QOMS” (the Qomolangma Atmospheric and Environmental Observation and Research Station), and “SETORS” (the Southeast Tibet Observation and Research Station for the Alpine Environment)) at the Chinese TP Scientific Data Center were used to study the surface energy variation characteristics and energy distribution over different underlying surfaces. Finally, we used observation data to verify the applicability of the ERA-5 land reanalysis data to the TP. The results showed that the annual GVF steadily declined from the southeast parts to the northwest parts of the TP, and the vegetation coverage rate was highest from June to September. The sensible heat flux (H), latent heat flux (LE), net surface radiation (Rn), and four-component radiation (solar downward shortwave radiation (Rsd), surface upward shortwave radiation (Rsu), atmospheric downward longwave radiation (Rld), and surface upward longwave radiation (Rlu)) reached their maxima in summer at each station. Rld did not change significantly with time; all other variables increased during the day and decreased at night. The interannual variation in H and LE shows that latent heat exchange was the dominant form of energy transfer in BJ, MAWORS, NAMORS, and SETORS. By contrast, sensible heat exchange was the main form of energy transfer in NADORS and QOMS. The Bowen ratio was generally low in summer, and some sites had a maximum in spring. The surface albedo exhibited a “U” shape, decreasing in spring and summer, and increasing in autumn and winter, and reaching the lowest value at noon. Except for SETORS, ERA-5 Land data and other flux stations had high simulation accuracy and correlation. Regional surface energy changes were mainly observed in the eastern and western parts of the TP, except for the maximum of H in spring; the maximum values of other heat fluxes were concentrated in summer