# Big Data Analysis and Visualization: Challenges and Solutions
## 2 Challenges and Solutions for Big Data Analysis and Visualization
### Big Data Analysis  
Nowadays, _massively parallel_ tasks--which make use of a large number of compute processors to simultaneously compute complicated mathematical operations in parallel--are needed for numerous big data applications. To perform these tasks, platforms such as Compute Unified Device Architecture (CUDA) and Open Computing Language (OpenCL) are widely used because they enhance the throughput of massively parallel tasks. Observing the demand in high-level abstractions and platform independence across these massively parallel computing platforms, a new cross-platform abstraction layer for single-source heterogeneous computing with C++ template-level abstractions for OpenCL, calledSYCL, was introduced. As there are several SYCL implementations from various vendors, [PERSON] et al. [1] compared the large-scale data computing performance of these SYCL heterogeneous parallel processing layer implementations, especially when handling well-known massively parallel tasks. They analyzed their characteristics, strengths and weaknesses when computing various types of mathematical operations and/or data sizes. These comparison results help users to select a suitable SYCL implementation to maximize kernel performance in large-scale big data parallel computing and visualization applications. Moreover, as demands for computing power grow, data processing for efficient resource management in traditional workstations has been under pressure. Consequently, a tremendous volume of big data has been processed using parallel computing. As an example, HTCondor--which is an open-source high-throughput computing (HTC) software framework for coarse-grained distributed parallelization of computationally intensive tasks--provides researchers with computing power to conduct data analysis. Although such a framework works effectively in a traditional computing cluster environment, an efficient methodology is required to meet the growing demand for computing with limited resources. [PERSON] et al. [2] consolidated job schedulers in traditional independent scientific workflows. Their approach integrates those clusters that can share their computing power based on a priority policy. While sharing worker nodes, it maintains the resources allocated to each group. It also utilizes the historical data of user usage for analyzing problems that may have occurred during job execution caused by resource sharing and/or the actual operating results. Experimental results show the benefits of integrating resources and sharing limited computing powers by multiple scientific groups. Note that high-performance computing (HPC) reduces overall job execution time and increases the capacity to solve large-scale complex problems using many distributed computing resources to solve problems through parallel computation. As a flagship tool in HPC, the job scheduler in a supercomputer is mainly responsible for distributing and managing the resources of large systems. [PERSON] et al. [3] conducted a case study on analyzing the execution log of the job scheduler over a period. It aims to improve execution time and resources in HPC, and thus reduces the idle time of jobs. The simulation results of the Tachyon2 system reveal that the causes of delayed jobs were strongly related to resource waiting. To elaborate, an increase in idle resources, which must be ready when applying for large-scale job, affected and significantly delayed the execution of the whole job. Consequently, [PERSON] et al. presented a backfilling algorithm to use available resources and reduce execution time for backfilled tasks, thus improving the performance of the overall scheduler. Furthermore, graph neural networks (GNNs) can successfully process graph-based data. Their iterative information aggregation from neighbors can be considered as a special form of Laplacian smoothing. However, many GNNs fall into an over-smoothing problem--i.e., the learned representations become indistinguishable when the model goes deeper.