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Smart cities and other cyber-physical systems (CPSs) rely on various scientific, engineering, business, and social applications that provide timely intelligence for their design, operations, and management. Many of these scientific and analytics applications require the solution of sparse linear equation systems, where sparse matrix-vector (SpMV) product is a key computing operation. Several factors determine the performance of parallel SpMV computations, including matrix characteristics, storage formats, and the rising complexity and heterogeneity of computer systems. There is a pressing need for new ways of exploiting parallelism, and mapping data and applications to the computing resources. We propose here ZAKI+ , a data-driven machine-learning approach, allowing users to automatically, effortlessly, and speedily obtain the best configuration (the data distribution, the optimal number of processes, and mapping strategy) and performance for the execution of the parallel SpMV computations on distributed memory machines. We train and test the tool using three machine learning methods-decision trees, random forest, and Xtreme boosting-and nearly 2000 real-world matrices obtained from 45 application domains, including computer vision and robotics. ZAKI+ provides optimal process mapping and outperforms the MPI default mapping policy by a factor of 4.24. This is the first work where the sparsity structure of matrices has been exploited to predict the optimal mapping of processes and data in distributed-memory environments by using different base and ensemble machine learning methods. Various CPSs comprise compute-intensive machine learning applications, such as the SpMV, and hence, the process and data mapping contributions of this paper would be of paramount impact for the CPSs. INDEX TERMS Cyber-physical systems, SpMV, sparse linear algebra, sparse matrices, machine learning, MPI, process affinity, compressed sparse row (CSR), decision trees, random forest, Xtreme boosting, parallel computing, high performance computing (HPC), smart cities analytics, exascale systems, OpenMPI. I. INTRODUCTION Cyber-Physical Systems (CPS) comprises ''interacting digital, analog, physical, and human components engineered for function through integrated physics and logic. These systems will provide the foundation of our critical infrastructure, form the basis of emerging and future smart services, and improve our quality of life in many areas'' [1], including transportation, healthcare, smart grid, disaster management and many other areas. A CPS uses Internet of Things (IoT), The associate editor coordinating the review of this manuscript and approving it for publication was Wei Yu.

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Big data has revolutionised science and technology leading to the transformation of our societies. High Performance Computing (HPC) provides the necessary computational power for big data analysis using artificial intelligence and methods. Traditionally HPC and big data had focused on different problem domains and had grown into two different ecosystems. Efforts have been underway for the last few years on bringing the best of both paradigms into HPC and big converged architectures. Designing HPC and big data converged systems is a hard task requiring careful placement of data, analytics, and other computational tasks such that the desired performance is achieved with the least amount of resources. Energy efficiency has become the biggest hurdle in the realisation of HPC, big data, and converged systems capable of delivering exascale and beyond performance. Data locality is a key parameter of HPDA system design as moving even a byte costs heavily both in time and energy with an increase in the size of the system. Performance in terms of time and energy are the most important factors for users, particularly energy, due to it being the major hurdle in high performance system design and the increasing focus on green energy systems due to environmental sustainability. Data locality is a broad term that encapsulates different aspects including bringing computations to data, minimizing data movement by efficient exploitation of cache hierarchies, reducing intra- and inter-node communications, locality-aware process and thread mapping, and in-situ and in-transit data analysis. This paper provides an extensive review of the cutting-edge on data locality in HPC, big data, and converged systems. We review the literature on data locality in HPC, big data, and converged environments and discuss challenges, opportunities, and future directions. Subsequently, using the knowledge gained from this extensive review, we propose a system architecture for future HPC and big data converged systems. To the best of our knowledge, there is no such review on data locality in converged HPC and big data systems.

The sparse matrix–vector product (SpMV), considered one of the seven dwarfs (numerical methods of significance), is essential in high-performance real-world scientific and analytical applications requiring solution of large sparse linear equation systems, where SpMV is a key computing operation. As the sparsity patterns of sparse matrices are unknown before runtime, we used machine learning-based performance optimization of the SpMV kernel by exploiting the structure of the sparse matrices using the Block Compressed Sparse Row (BCSR) storage format. As the structure of sparse matrices varies across application domains, optimizing the block size is important for reducing the overall execution time. Manual allocation of block sizes is error prone and time consuming. Thus, we propose AAQAL, a data-driven, machine learning-based tool that automates the process of data distribution and selection of near-optimal block sizes based on the structure of the matrix. We trained and tested the tool using different machine learning methods—decision tree, random forest, gradient boosting, ridge regressor, and AdaBoost—and nearly 700 real-world matrices from 43 application domains, including computer vision, robotics, and computational fluid dynamics. AAQAL achieved 93.47% of the maximum attainable performance with a substantial difference compared to in practice manual or random selection of block sizes. This is the first attempt at exploiting matrix structure using BCSR, to select optimal block sizes for the SpMV computations using machine learning techniques.