Sometimes, the processing requirements of organizational applications may be more than what a regular computer configuration may offer. This can be addressed to an extent by increasing the processor capacity and other resource allocation. While this can improve the performance for a while, it restricts any future computational requirements, such as adding more powerful computational processors; it also involves an extra cost for producing such powerful systems. Also, there is a need to produce efficient algorithms and practices to produce the best results. A practical and economic substitute for these single high-power computers lies in establishing multiple low-power capacity processors that can work collectively and organize their processing capabilities. This means we'll set up parallel computers that would permit processing activities to be distributed among multiple low-capacity computers and obtain the best results. This would result in a powerful system.

In this chapter, we will cover the following topics:

• Era of computing
• Commanding parallel system architectures
• Massively Parallel Processors (MPP)
• Symmetric Multiprocessors (SMP)
• Cache Coherent Nonuniform Memory Access (CC-NUMA)
• Distributed systems
• Clusters
• Network of workstations
• Cluster computing architecture
• Cluster system software and tools
• High-Performance Computing Cluster (HPCC)
• Java support for high-performance computing
• Java support for parallel programming models
• Multithreading
• The Spliterator interface
• Java 9 updates for processing an API