The Illiac IV was the first large scale array computer. As the fore runner of today's advanced computers, it brought whole classes of scientific computations into the realm of practicality. Conceived initially as a grand experiment in computer science, the revolutionary architecture incorporated both a high level of parallelism and pipe lining. After a difficult gestation, the Illiac IV became operational in November 1975. It has for a decade been a substantial driving force behind the develooment of computer technology. Today the Illiac IV continues to service large-scale scientific aoolication areas includ ing computational fluid dynamics, seismic stress wave propagation model ing, climate simulation, digital image processing, astrophysics, numerical analysis, spectroscopy and other diverse areas. This volume brings together previously published material, adapted in an effort to provide the reader with a perspective on the strengths and weaknesses of the Illiac IV and the impact this unique computa tional resource has had on the development of technology. The history and current status of the Illiac system, the design and architecture of the hardware, the programming languages, and a considerable sampling of applications are all covered at some length. A final section is devoted to commentary.

This volume reviews, in the context of partial differential equations, algorithm development that has been specifically aimed at computers that exhibit some form of parallelism. Emphasis is on the solution of PDEs because these are typically the problems that generate high computational demands. The authors discuss architectural features of these computers insomuch as they influence algorithm performance, and provide insight into algorithm characteristics that allow effective use of hardware.

It has been recognized for a long time that a conventional sequential processor is inefficient for operations on pictorial data where relatively simple operations need to be performed on a large number of data elements (pixels). Though many parallel processing architectures for picture processing have been proposed in the past, very few have actually been implemented due to the costs involved. With LSI technology, it is becoming possible to realize parallel architectures at a modest cost. In the following the authors review some of the proposed architectures for pattern recognition and image processing.

Storage Systems: Organization, Performance, Coding, Reliability and Their Data Processing was motivated by the 1988 Redundant Array of Inexpensive/Independent Disks proposal to replace large form factor mainframe disks with an array of commodity disks. Disk loads are balanced by striping data into strips—with one strip per disk— and storage reliability is enhanced via replication or erasure coding, which at best dedicates k strips per stripe to tolerate k disk failures. Flash memories have resulted in a paradigm shift with Solid State Drives (SSDs) replacing Hard Disk Drives (HDDs) for high performance applications. RAID and Flash have resulted in the emergence of new storage companies, namely EMC, NetApp, SanDisk, and Purestorage, and a multibillion-dollar storage market. Key new conferences and publications are reviewed in this book.The goal of the book is to expose students, researchers, and IT professionals to the more important developments in storage systems, while covering the evolution of storage technologies, traditional and novel databases, and novel sources of data. We describe several prototypes: FAWN at CMU, RAMCloud at Stanford, and Lightstore at MIT; Oracle's Exadata, AWS' Aurora, Alibaba's PolarDB, Fungible Data Center; and author's paper designs for cloud storage, namely heterogeneous disk arrays and hierarchical RAID. - Surveys storage technologies and lists sources of data: measurements, text, audio, images, and video - Familiarizes with paradigms to improve performance: caching, prefetching, log-structured file systems, and merge-trees (LSMs) - Describes RAID organizations and analyzes their performance and reliability - Conserves storage via data compression, deduplication, compaction, and secures data via encryption - Specifies implications of storage technologies on performance and power consumption - Exemplifies database parallelism for big data, analytics, deep learning via multicore CPUs, GPUs, FPGAs, and ASICs, e.g., Google's Tensor Processing Units