A theoretical foundation for program transformations to reduce cache thrashing due to true data sharing

AbstractCache thrashing due to true data sharing can degrade the performance of parallel programs significantly. Our previous work showed that parallel task alignment via program transformations can be quite effective for the reduction of such cache thrashing. In this paper, we present a theoretical foundation for such program transformations. Based on linear algebra and the theory of numbers, our work analyzes the data dependences among the tasks created by a fork-join parallel program and determines at compile time how these tasks should be assigned to processors in order to reduce cache thrashing due to true data sharing. Our analysis and program transformations can be easily performed by compilers for parallel computers

Anisotropically Shaped Magnetic/Plasmonic Nanocomposites for Information Encryption and Magnetic-Field-Direction Sensing.

Instantaneous control over the orientation of anisotropically shaped plasmonic nanostructures allows for selective excitation of plasmon modes and enables dynamic tuning of the plasmonic properties. Herein we report the synthesis of rod-shaped magnetic/plasmonic core-shell nanocomposite particles and demonstrate the active tuning of their optical property by manipulating their orientation using an external magnetic field. We further design and construct an IR-photoelectric coupling system, which generates an output voltage depending on the extinction property of the measured nanocomposite sample. We employ the device to demonstrate that the nanocomposite particles can serve as units for information encryption when immobilized in a polymer film and additionally when dispersed in solution can be employed as a new type of magnetic-field-direction sensor

Accelerating Backward Aggregation in GCN Training with Execution Path Preparing on GPUs

The emerging Graph Convolutional Network (GCN) has now been widely used in many domains, and it is challenging to improve the efficiencies of applications by accelerating the GCN trainings. For the sparsity nature and exploding scales of input real-world graphs, state-of-the-art GCN training systems (e.g., GNNAdvisor) employ graph processing techniques to accelerate the message exchanging (i.e. aggregations) among the graph vertices. Nevertheless, these systems treat both the aggregation stages of forward and backward propagation phases as all-active graph processing procedures that indiscriminately conduct computation on all vertices of an input graph. In this paper, we first point out that in a GCN training problem with a given training set, the aggregation stages of its backward propagation phase (called as backward aggregations in this paper) can be converted to partially-active graph processing procedures, which conduct computation on only partial vertices of the input graph. By leveraging such a finding, we propose an execution path preparing method that collects and coalesces the data used during backward propagations of GCN training conducted on GPUs. The experimental results show that compared with GNNAdvisor, our approach improves the performance of the backward aggregation of GCN trainings on typical real-world graphs by 1.48x~5.65x. Moreover, the execution path preparing can be conducted either before the training (during preprocessing) or on-the-fly with the training. When used during preprocessing, our approach improves the overall GCN training by 1.05x~1.37x. And when used on-the-fly, our approach improves the overall GCN training by 1.03x~1.35x

Research on Trust Propagation Models in Reputation Management Systems

Feedback based reputation systems continue to gain popularity in eCommerce and social media systems today and reputation management in large social networks needs to manage cold start and sparseness in terms of feedback. Trust propagation has been widely recognized as an effective mechanism to handle these problems. In this paper we study the characterization of trust propagation models in the context of attack resilience. We characterize trust propagation models along three dimensions: (i) uniform propagation and conditional propagation, (ii) jump strategies for breaking unwanted cliques, and (iii) decay factors for differentiating recent trust history from remote past history. We formally and experimentally show that feedback similarity is a critical measure for countering colluding attacks in reputation systems. Without feedback similarity guided control, trust propagations are vulnerable to different types of colluding attacks

Modeling dust sources, transport, and radiative effects at different altitudes over the Tibetan Plateau

Mineral dust plays an important role in the climate of the Tibetan Plateau (TP) by modifying the radiation budget, cloud macro- and microphysics, precipitation, and snow albedo. Meanwhile, the TP, with the highest topography in the world, can affect intercontinental transport of dust plumes and induce typical distribution characteristics of dust at different altitudes. In this study, we conduct a quasi-global simulation to investigate the characteristics of dust source contribution and transport over the TP at different altitudes by using a fully coupled meteorology–chemistry model, the Weather Research and Forecasting model with chemistry (WRF-Chem), with a tracer-tagging technique. Generally, the simulation reasonably captures the spatial distribution of satellite-retrieved dust aerosol optical depth (AOD) at different altitudes. Model results show that dust particles are emitted into atmosphere through updrafts over major desert regions and then transported to the TP. The East Asian dust (mainly from the Gobi and Taklamakan deserts) is transported southward and is lifted up to the TP, contributing a mass loading of 50 mg m−2 at a height of 3 km and 5 mg m−2 at a height of 12 km over the northern slope of the TP. Dust from North Africa and the Middle East are concentrated over both of the northern and southern slopes below 6 km, where mass loadings range from 10 to 100 and 1 to 10 mg m−2 below 3 km and above 9 km, respectively. As the dust is transported to the north and over the TP, mass loadings are 5–10 mg m−2 above a height of 6 km. The dust mass flux carried from East Asia to the TP is 7.9 Tg yr−1, mostly occurring at heights of 3–6 km. The dust particles from North Africa and the Middle East are transported eastward following the westerly jet and then are carried into the TP at the west side with dust mass fluxes of 7.8 and 26.6 Tg yr−1, respectively. The maximum mass flux of the North African dust mainly occurs at 0–3 km (3.9 Tg yr−1), while the Middle Eastern dust occurs at 6–9 km (12.3 Tg yr−1). The dust outflow occurs on the east side (−17.89 Tg yr−1) and south side (−11.22 Tg yr−1) of the TP, with a peak value (8.7 Tg yr−1) at 6–9 km. Moreover, the dust (by mass) is concentrated within the size range of 1.25–5.0 µm and the dust (by particle number) is concentrated in the size range of 0.156–1.25 µm. Compared with other aerosols, the dust contributes to more than 50 % of the total AOD over the TP. The direct radiative forcing induced by the dust is −1.28 W m−2 at the top of the atmosphere (cooling), 0.41 W m−2 in the atmosphere (warming), and −1.68 W m−2 at the surface (cooling). Our quantitative analyses of the dust contributions from different source regions and the associated radiative forcing can help us to better understand the role of dust on the climate over the TP and surrounding regions