Having up-to-date information, reducing delays, and using improved speed for competitive advantage, have always been key requirements of any enterprise. With rapidly changing scenarios of the global economy, the requirement of shrinking decision cycles and having shorter time-to-market cycles has been heightened. Be it a bank, a retail chain or a manufacturing company, decision makers would like to be better equipped to make informed decisions, by having access to data that is current and relevant, rather than data that is older and hence less relevant.

Conventional business intelligence solutions primarily deal with data from the past. By using these solutions, enterprises cannot process data ''instantaneously'' or ''on arrival'', due to technical limitations. As per an Aberdeen Group survey, enterprises with real-time visibility have seen noticeably higher performance across several tactical operating metrics such as Growth in Operating Cash Flow, Increase in Inventory Turns and Reduction in Operating Cost.

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E-commerce continues to grow faster on a percentage basis than traditional retail. Even though E-commerce currently contributes only 6-12% of a traditional retailer’s revenue, it is expected to more than double in the next five years. More growth in sales is expected in the near future from online channels than traditional brick-and-mortar channels. New online store brands are creating growth from differentiated business models with higher customer satisfaction thereby generating business from repeat customers.

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The ever growing demand for computing from industry has lead to excessive resource consumption issues which is not only impacting sustainability of compute and utility grids in terms of cost but also from environment perspective as well. Since Grid computing consists of several High Performance Computing centers under different domain, it makes the problem even more difficult. The process of managing resources and scheduling computations over the Grid is complex as they are heterogeneous in nature, distributed, owned by different organizations/individuals with their own policies, have varying load/availability and different cost models. This introduces a huge number of challenges such as heterogeneous substrate, policy extensibility, site autonomy, online control, resource allocation, transparency, and scalability. Some of these issues are currently being addressed by tool-kits such as Globus, gLite and UNICORE. For effective resource utilization, we need to address the various challenges by effectively distributing parallel applications on grid.

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From discovering a cure for a disease to exploring energy to estimating weather, engineering and science today relies heavily on the simulation and modelling performed on high performance computing systems. But to keep ahead of this exploding demand for computing requires innovation, especially with programming issues and power. New and improved Many-core processors are causing changes in the stable computing architecture used in the past 2 decades. To add on top is the increasing importance of cloud computing which is leading to significant changes in computer system architecture.

Enter Many-core systems

A Multi-core chip is a processing chip with 2 or more identical cores in one package, whereas a Manycore chip is a processing chip that has 10 or more heterogeneous cores in one package. In Manycore system, all cores will share resources including main memory and hard drives. Industry analysts anticipate the advent of affordable “manycore” systems in the next couple of years.

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