Download Full Article PARALLEL COMPUTING IN INDIA
Although the performance of single processors has been steadily increasing over the years, the only way to build the next generation teraflop architecture supercomputers seems to be through parallel processing technology. Even with today’s workstation-class high performance processors exceeding 100 megaflops, thousands of processors are required to build a teraflop architecture machine. Further, the fastest special purpose vector processors have a few Gigaflop peak performance, and thus they too need to be utilized in parallel to achieve Teraflop levels of performance.
In 1987, India decided to launch a national initiative in supercomputing in the form of a time-bound mission to design, develop and deliver a supercomputer in the gigaflops range. The major motivation came from delays (political) in getting a CRAY XMP for weather forecasting. A decision was made to support the development of indigenous parallel processing technology. The Center for Development of Advanced Computing (C-DAC) was set up in August 1988 with 3-year budget of Rs. 375 million (approximately US$ 12 million).
C-DAC’s First Mission was directed to deliver 1000 MFlops parallel supercomputer (1GF) by 1991. Simultaneously, several other complementary projects were initiated to develop high-performance parallel computers at the National Aerospace Laboratory of the Council of Scientific and Industrial Research (CSIR), the Center for Development of Telematics (C-DOT), Advanced Numerical Research & Analysis Group (ANURAG) of Defense Research and Development Organization (DRDO) and Bhabha Atomic Research Center (BARC). India’s first generation parallel computers were delivered starting from 1991.
PARALLEL PROCESSING
We all know that the silicon based chips are reaching a physical limit in processing speed, as they are constrained by the speed of electricity, light and certain thermodynamic laws. A viable solution to overcome this limitation is to connect multiple processors working in coordination with each other to solve grand challenge problems. Hence, high performance computing requires the use of Massively Parallel Processing (MPP) systems containing thousands of power full CPUs.
Processing of multiple tasks simultaneously on multiple processors is called Parallel Processing. The parallel program consists of multiple active processes simultaneously solving a given problem. A given task is divided into multiple sub tasks using divide-and-conquer technique and each one of them are processed on different CPUs. Programming on multiprocessor system using divide-and-conquer technique is called Parallel Processing.
The development of parallel processing is being influenced by many factors. The prominent among them include the following:
Ø Computational requirements are ever increasing, both in the area of scientific and business computing. The technical computing problems, which require high-speed computational power, are related to life sciences, aerospace, geographical information systems, mechanical design and analysis, etc.
Ø Sequential architectures reaching physical limitation, as they are constrained by the speed of light and thermodynamics laws. Speed with which sequential CPUs can operate is reaching saturation point ( no more vertical growth ), and hence an alternative way to get high computational speed is to connect multiple CPUs ( opportunity for horizontal growth ).
Ø Hardware improvements in pipelining, super scalar, etc, are non scalable and requires sophisticated compiler technology. Developing such compiler technology is difficult task.
Ø Vector processing works well for certain kind of problems. It is suitable for only scientific problems ( involving lots of matrix operations). It is not useful to other areas such as database.
Ø The technology of parallel processing is mature and can be exploited commercially, there is already significant research and development work on development tools and environment is achieved.
Ø Significant development in networking technology is paving a way for heterogeneous computing.