Search

With the semi-conductor technology gradually approaching its physical and heat limits, recent supercomputers have adopted major architectural changes to continue increasing the performance through more power-efficient heterogeneous many-core systems. Examples include Sunway TaihuLight that has four Management Processing Element (MPE) and 256 Computing Processing Element (CPE) inside one processor and Summit that has two central processing units (CPUs) and 6 graphics processing units (GPUs) inside one node. Meanwhile, current high-resolution Earth system models that desperately require more computing power, generally consist of millions of lines of legacy codes developed for traditional homogeneous multi-core processors and cannot automatically benefit from the advancement of supercomputer hardware. As a result, refactoring and optimizing the legacy models for new architectures become a key challenge along the road of taking advantage of greener and faster supercomputers, providing better support for the global climate research community and contributing to the long-lasting society task of addressing long-term climate change. This article reports the efforts of a large group in the International Laboratory for High-Resolution Earth System Prediction (iHESP) established by the cooperation of Qingdao Pilot National Laboratory for Marine Science and Technology (QNLM), Texas A & M University and the National Center for Atmospheric Research (NCAR), with the goal of enabling highly efficient simulations of the high-resolution (25-km atmosphere and 10-km ocean) Community Earth System Model (CESM-HR) on Sunway TaihuLight. The refactoring and optimizing efforts have improved the simulation speed of CESM-HR from 1 SYPD (simulation years per day) to 3.4 SYPD (with output disabled), and supported several hundred years of pre-industrial control simulations. With further strategies on deeper refactoring and optimizing for a few remaining computing hot spots, we expect an equivalent or even better efficiency than homogeneous CPU platforms. The refactoring and optimizing processes detailed in this paper on the Sunway system should have implications to similar efforts on other heterogeneous many-core systems such as GPU-based high-performance computing (HPC) systems. [ABSTRACT FROM AUTHOR]

Physical and heat limits of the semiconductor technology require the adaptation of heterogeneous architectures in supercomputers to maintain a continuous increase of computing performance. The coexistence of general-purpose cores and accelerator cores, which usually employ different hardware architectures, can lead to bit-level differences, especially when we try to maximize the performance on both kinds of cores. Such differences further lead to unavoidable computational perturbations through temporal integration, which can blend with software or human errors. Software correctness verification in the form of quality assurance is a critically important step in the development and optimization of Earth system models (ESMs) on heterogeneous many-core systems with mixed perturbations of software changes and hardware updates. We have developed a deep learning-based consistency test approach for Earth System Models referred to as ESM-DCT. The ESM-DCT is based on the unsupervised bidirectional gate recurrent unit-autoencoder (BGRU-AE) model, which can still detect the existence of software or human errors when taking hardware-related perturbations into account. We use the Community Earth System Model (CESM) on the new Sunway system as an example of large-scale ESMs to evaluate the ESM-DCT. The results show that facing with the mixed perturbations caused by hardware designs and software changes in heterogeneous computing, the ESM-DCT can detect software or human errors when determining whether or not the model simulation is consistent with the original results in homogeneous computing. Our ESM-DCT tool provides an efficient and objective approach for verifying the reliability of the development and optimization of scientific computing models on the heterogeneous many-core systems. [ABSTRACT FROM AUTHOR]

Oil palm trees are important economic crops in Malaysia and other tropical areas. The number of oil palm trees in a plantation area is important information for predicting the yield of palm oil, monitoring the growing situation of palm trees and maximizing their productivity, etc. In this paper, we propose a deep learning based framework for oil palm tree detection and counting using high-resolution remote sensing images for Malaysia. Unlike previous palm tree detection studies, the trees in our study area are more crowded and their crowns often overlap. We use a number of manually interpreted samples to train and optimize the convolutional neural network (CNN), and predict labels for all the samples in an image dataset collected through the sliding window technique. Then, we merge the predicted palm coordinates corresponding to the same palm tree into one palm coordinate and obtain the final palm tree detection results. Based on our proposed method, more than 96% of the oil palm trees in our study area can be detected correctly when compared with the manually interpreted ground truth, and this is higher than the accuracies of the other three tree detection methods used in this study. [ABSTRACT FROM AUTHOR]

The Single Column Atmospheric Model (SCAM) is an essential tool for analyzing and improving the physics schemes of CAM. Although it already largely reduces the compute cost from a complete CAM, the exponentially-growing parameter space makes a combined analysis or tuning of multiple parameters difficult. In this paper, we propose a hybrid framework that combines parallel execution and a learning-based surrogate model, to support large-scale sensitivity analysis (SA) and tuning 5 of combinations of multiple parameters. We start with a workflow (with modifications to the original SCAM) to support the execution and assembly of a large number of sampling, sensitivity analysis, and tuning tasks. By reusing the 3,840 instances with the variation of 11 parameters, we train a neural network (NN) based surrogate model that achieves both accuracy and efficiency (with the computational cost reduced by several orders of magnitude). The improved balance between cost and accuracy enables us to integrate NN-based grid search into the traditional optimization methods to achieve better optimization results with fewer compute cycles. Using such a hybrid framework, we explore the joint sensitivity of multi-parameter combinations to multiple cases using a set of three parameters, identify the most sensitive three-parameter combination out of eleven, and perform a tuning process that reduces the error of precipitation by 5% to 15% in different cases. [ABSTRACT FROM AUTHOR]

This paper presents a novel analytical model and an efficient admission control (AC) algorithm for IEEE 802.11 distributed coordinated function access mechanism. In contrast to the previous approaches, both saturated and unsaturated states of network are analyzed and the impacts of error-frame rate and retransmission limit are also taken into account based on an improved Markov chain model. Thus, the network resources can be efficiently utilized. Taking the throughput difference between saturated and unsaturated states as the residual bandwidth, an efficient AC algorithm is designed to utilize the network resources effectively. Extensive simulation data match our analytical results and demonstrate that the AC algorithm is efficient and can make the effective utilization of network resources. Copyright © 2008 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

Physical and heat limits of the semiconductor technology require the adaptation of heterogeneous architectures in supercomputers, such as graphics processing units (GPUs) with many-core accelerators and many-core processors with management and computing cores, to maintain a continuous increase of computing performance. The transition from homogeneous multi-core architectures to heterogeneous many-core architectures can produce "potential differences" that lead to numerical perturbations and uncertainties in simulation results, which could blend with errors due to coding bugs. The development of a methodology to identify the computational perturbations and secure the model correctness is a critically important step in model development on the computer system with new architectures. We have developed a methodology to characterize the uncertainties in the heterogeneous many-core computing environment, which contains a simple multiple-column atmospheric model consisting of typical discontinuous physical parameterizations defined by on-off switches, an efficient ensemble-based test approach, and a software tool applied to the GPU-based high-performance computing (HPC) and Sunway systems. Statistical distributions from ensembles of the heterogeneous systems show quantitative analyses of computational perturbations and acceptable error tolerances. The methodology explores fully understanding to distinguish between perturbations caused by platforms and discrepancies caused by software bugs, and provides encouraging references for verifying the reliability of supercomputing platforms and discussing the sensibility of Earth system modeling to the adaptation of new heterogeneous many-core architectures. [ABSTRACT FROM AUTHOR]

Phenology response to climatic variables is a vital indicator for understanding changes in biosphere processes as related to possible climate change. We investigated global phenology relationships to precipitation and land surface temperature (LST) at high spatial and temporal resolution for calendar years 2008-2011. We used cross-correlation between MODIS Enhanced Vegetation Index (EVI), MODIS LST and Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks (PERSIANN) gridded rainfall to map phenology relationships at 1-km spatial resolution and weekly temporal resolution. We show these data to be rich in spatiotemporal information, illustrating distinct phenology patterns as a result of complex overlapping gradients of climate, ecosystem and land use/land cover. The data are consistent with broad-scale, coarse-resolution modeled ecosystem limitations to moisture, temperature and irradiance. We suggest that high-resolution phenology data are useful as both an input and complement to land use/land cover classifiers and for understanding climate change vulnerability in natural and anthropogenic landscapes. [ABSTRACT FROM AUTHOR]

In the Community Earth System Model (CESM), the ocean model is computationally expensive for high-resolution grids and is often the least scalable component for high-resolution production experiments. The major bottleneck is that the barotropic solver scales poorly at high core counts. We design a new barotropic solver to accelerate the high-resolution ocean simulation. The novel solver adopts a Chebyshev-type iterative method to reduce the global communication cost in conjunction with an effective block preconditioner to further reduce the iterations. The algorithm and its computational complexity are theoretically analyzed and compared with other existing methods. We confirm the significant reduction of the global communication time with a competitive convergence rate using a series of idealized tests. Experimental results obtained with the CESM 0.1° global ocean model show that the proposed approach results in a factor of 1.7 speed-up over the original method with no loss of accuracy, achieving 10.5 simulated years per wall-clock day on 16,875 cores. [ABSTRACT FROM AUTHOR]

The oil and gas industry is a major user of high-performance computing, and geoscience computational cycles are dominated by kernels that are relatively few and well defined. This project explores accelerating geoscience applications using FPGA-based hardware, optimizing the algorithm and the hardware to achieve maximum performance. This approach can deliver speedup of 20 to 70 times compared with a conventional HPC node. [ABSTRACT FROM PUBLISHER]