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Super-computing or HPC clusters are built to provide services to execute computationally complex applications. Generally, these HPC applications involve large scale IO (input/output) processing over the networked parallel file system disks. They are commonly developed on top of the C/C++ based MPI standard library. The HPC clusters MPI–IO performance significantly depends on the particular parameter value configurations, not generally considered when writing the algorithms or programs. Therefore, this leads to poor IO and overall program performance degradation. The IO is mostly left to individual practitioners to be optimised at code level. This usually leads to unexpected consequences due to IO bandwidth degradation which becomes inevitable as the file data scales in size to petabytes. To overcome the poor IO performance, this research paper presents an approach for auto-tuning of the configuration parameters by forecasting the MPI–IO bandwidth via artificial neural networks (ANNs), a machine learning (ML) technique. These parameters are related to MPI–IO library and lustre (parallel) file system. In addition to this, we have identified a number of common configurations out of numerous possibilities, selected in the auto-tuning process of READ/WRITE operations. These configurations caused an overall READ bandwidth improvement of 65.7% with almost 83% test cases improved. In addition, the overall WRITE bandwidth improved by 83% with number of test cases improved by almost 93%. This paper demonstrates that by using auto-tuning parameters via ANNs predictions, this can significantly impact overall IO bandwidth performance.

ExSeisDat is designed using standard message passing interface (MPI) library for seismic data processing on high-performance super-computing clusters. These clusters are generally designed for efficient execution of complex tasks including large size IO. The IO performance degradation issues arise when multiple processes try accessing data from parallel networked storage. These complications are caused by restrictive protocols running by a parallel file system (PFS) controlling the disks and due to less advancement in storage hardware itself as well. This requires and leads to the tuning of specific configuration parameters to optimize the IO performance, commonly not considered by users focused on writing parallel application. Despite its consideration, the changes in configuration parameters are required from case to case. It adds up to further degradation in IO performance for a large SEG-Y format seismic data file scaling to petabytes. The SEG-Y IO and file sorting operations are the two of the main features of ExSeisDat. This research paper proposes technique to optimize these SEG-Y operations based on artificial neural networks (ANNs). The optimization involves auto-tuning of the related configuration parameters, using IO bandwidth prediction by the trained ANN models through machine learning (ML) process. Furthermore, we discuss the impact on prediction accuracy and statistical analysis of auto-tuning bandwidth results, by the variation in hidden layers nodes configuration of the ANNs. The results have shown the overall improvement in bandwidth performance up to 108.8% and 237.4% in the combined SEG-Y IO and file sorting operations test cases, respectively. Therefore, this paper has demonstrated the significant gain in SEG-Y seismic data bandwidth performance by auto-tuning the parameters settings on runtime by using an ML approach.

HPC or super-computing clusters are designed for executing computationally intensive operations that typically involve large scale I/O operations. This most commonly involves using a standard MPI library implemented in C/C++. The MPI-I/O performance in HPC clusters tends to vary significantly over a range of configuration parameters that are generally not taken into account by the algorithm. It is commonly left to individual practitioners to optimise I/O on a case by case basis at code level. This can often lead to a range of unforeseen outcomes. The ExSeisDat utility is built on top of the native MPI-I/O library comprising of Parallel I/O and Workflow Libraries to process seismic data encapsulated in SEG-Y file format. The SEG-Y File data structure is complex in nature, due to the alternative arrangement of trace header and trace data. Its size scales to petabytes and the chances of I/O performance degradation are further increased by ExSeisDat. This research paper presents a novel study of the changing I/O performance in terms of bandwidth, with the use of parallel plots against various MPI-I/O, Lustre (Parallel) File System and SEG-Y File parameters. Another novel aspect of this research is the predictive modelling of MPI-I/O behaviour over SEG-Y File benchmarks using Artificial Neural Networks (ANNs). The accuracy ranges from 62.5% to 96.5% over the set of trained ANN models. The computed Mean Square Error (MSE), Mean Absolute Error (MAE) and Mean Absolute Percentage Error (MAPE) values further support the generalisation of the prediction models. This paper demonstrates that by using our ANNs prediction technique, the configurations can be tuned beforehand to avoid poor I/O performance.

Matrix Exponential based algorithm (MEXP) is a recently developed method for solving a positive definite system of linear equations. MEXP already outperforms other state of the art algorithms, such as the Preconditioned Conjugate Gradient method (PCG), in most cases, on customizable hardware platforms such as FPGAs or ASICs. In this paper we have analyzed the performance of MEXP on multicore hardware using a shared-memory model called Cilk and compare it with the Conjugate Gradient method (CG) and PCG. Our multithreaded MEXP outperforms the Cilk based PCG and CG methods in terms of parallelism and execution time as we increase numbers of cores. The comparison of the performance for the tested benchmark problems shows that parallel MEXP relatively gives almost 2 to 3 times more speedup than parallel PCG and 5 to 8 times more speedup than parallel CG. Thus, MEXP is more parallelizable and scalable than both PCG and CG.

Effectiveness of refuge crop is on risk due to shifting of pollen mediated gene flow by honey bee pollinators. Pesticides and pollinators both are essential for the high and quality production of many crops. Present studies were therefore carried out to study the pesticide risks on pollinators and the ultimate effect of pollinators on cotton refuge crop. Pollinator population was badly affected in insecticide treated plots with 92 percent reduction over control. Honey bee were the efficient pollinator in cotton and pollen mediated gene flow was recorded as higher as up to 26 percent in absence of any chemical or toxic sprays. Among cultivars VH-290 showed the highest attraction for honeybees and consequently greater gene flow (21.2%) than other varieties because of its unique floral morphology. Regression analysis of pollinator population per plant and gene flow demonstrated that one-unit increase in pollinator population would increase geneflow @ 6.86% in sprayed field and 7.09% in un sprayed field. It is concluded that we should cultivate refuge non Bt cotton that have minimum chance of gene flow in order to minimize pollen mixing, maintain the quality of the refuge crop and to counter the field evolved resistance of insect pests against pesticides.