Open Access Open Access  Restricted Access Subscription or Fee Access

Coarse Grain to Mixed Grain Parallel Computing

Tirumale Ramesh


Parallel computing interests are growing again as a result of multicore processors. History of parallel computing dates back to early 1970’s when supercomputers supported high performance computing demand for scientific applications. Today, highly dense Very Large Scale Integration (VLSI) chips have opened up new opportunities and challenges for parallel computing. In this article, we provide a short synopsis of an evolutionary path of parallel computing from both hardware granularity and programming point of view. The article also discusses the challenges of parallel computing to emerging big data analytics area.

Keywords: Parallel Computing, Coarse Grain, Fine Grain, Mixed-grain, Multiprocessor, Multicore, Instruction Level Parallelism, SIMD, MIMD, Data Flow, VLSI, Field Programmable Gate Arrays (FPGA), Multi-FPGA, Graphic Processor Units (GPU), Big Data Analytics

Full Text:



William Stallings, Computer Organization & Architecture- Designing for Performance, Prentice-Hall India Pvt Ltd; 2008.

John Paul Shen, Mikko Lipasti, Modern Processor Design-Fundamentals of Superscalar Processors, Tata McGraw Hill, 3rd Edition, 2011.

Ananth Grama, Anshul Gupta, George Karypsis, et al. Introduction to Parallel Computing, Pearson Publications, Second Edition, 2011.

The Cray-1 Supercomputer - CHM Revolution [Online], Available at:

Selling the Computer Revolution [Online], Available at:

Flynn's Taxonomy [Online], Available at:

Tirumale Ramesh, Reconfigurable and Flexibly Coupled Multiprocessor for Parallel Computing, [PhD dissertation], Dept. Computer Science and Engineering, Oakland University, Rochester, MI, 1993.

Chip Makers Turn To Multicore Processors [Online], Available at:

Wen-Chung Tsai, 1 Ying-Cherng Lan, 1 Yu-Hen Hu, et al. Networks on Chips: Structure and Design Methodologies, J.

Electr. Comput. Eng. 2012; 2012: Article ID 509465, 15p.

Khan O., Lis M., Sinangil Y., et al. DCC: A Dependable Cache Coherence Multicore Architecture, Comput. Archit. Lett. 2011; 10(1): 16–19p.

Evolution of the NVIDIA GPU Architecture [Online], Available at:

Stephen W. Keckler, William J. Dally, Brucek Khailany, et al. GPUs and the Future of Parallel Computing, IEEE Micro, 2011.

Bill Dally, Steve Lacy, VLSI Architecture: Past, Present, and Future, Citeseer Publication, 1999.

Tehranipoor, Mohammad, Wang, Cliff (Eds.), Introduction to Hardware Security and Trust, VIII, 427, Springer Publication, 2012.

Security Basics for Computer Architects. Available at:

Jack Dongara and Alexey L. Lastovetsky, High Performance Heterogenous Computing, John Wiley & Sons, 2009.

Ziegler H., Byoungro So, Hall M., et al. Coarse-grain Pipelining on Multiple FPGA Architectures, FCCM 2002 Proceedings of the 10 th Annual IEEE Symposium on Field-Programmable Computing Machines, 2002; 77p.

Tirumale Ramesh, John Meier, A Multi-FPGA High Performance Computing Platform for Network Centric Applications, Poster Paper, International High Performance Computing, 2008.

Coarse Grain Reconfigurable Architectures. Available at:

Von Neumann Architecture of Computer Systems. Available at:

Huabin Ruan, Xiaomeng Huang, Haohuan Fu, et al. An FPGA-Based Data Flow Engine for Gaussian Copula Model, IEEE 21st Annual International Symposium on Field-Programmable Custom Computing Machines (FFCM), 2013; 218–225p.

Chiu M., Ravindran R., Mahlke S., Data Access Partitioning for Fine-grain Parallelism on Multicore Architectures, 40th Annual IEEE/ACM International Symposium on Microarchitecture, 2007.

Lars Baunegaard, With Jensen, Anders Kjær-Nielsen, et al. A Hybrid FPGA/Coarse Parallel Processing Architecture for Multi-modal Visual Feature Descriptors, International Conference on RC and FPGAs, 2008.

AL-Marakeby A, FPGA on FPGA: Implementation of Fine-grained Parallel Genetic Algorithm on Field Programmable Gate Array, Int. J. Comput. Appl. 2013; 80(6): 29–32p.

From SMPs to FPGAs: Multi-Target Data-Parallel Programming [Online], Available at:

Manuel Alejandro Salda˜na De Fuentes, A Parallel Programming Model for a Multi-FPGA Multiprocessor Machine, [A thesis] submitted in conformity with the requirements for the degree of Master of Applied Sciences Graduate Department of Electrical and Computer Engineering, University of Toronto, 2006.

The Future Fabric of Data Analysis [Online]. Available at:

How Quantum Computers and Machine Learning Will Revolutionize Big Data. Available at:

Ron Bekkerman, Mikhail Bilenko and John Langford (Eds), Uniformly Fine-Grained Data-Parallel Computing for Machine Learning Algorithms, Cambridge Press, 89–106p, 2011.

Fabric Computing [Online]. Available at:


  • There are currently no refbacks.

This site has been shifted to