high-performance computing

A New Computational Model for Real Gains in Big Data Processing Power

Big data and high performance computing are seen by many as important tools that will be used to advance science. However, the computational power needed for this promise to materialize far exceeds what is currently available. This paper argues that the von Neumann computational model, the only model in everyday use, has inherent weaknesses that will prevent computers from achieving the envisaged performance levels. First, these weaknesses are explored and the properties of a computational model are identified that would be required to overcome these weaknesses.

Tasks Scaling with Chameleon© C2HDL Design Tool in Self-Configurable Computer Systems Based on Partially Reconfigurable FPGAs

The FPGA-based accelerators and reconfigurable computer systems based on them require designing the application-specific processor soft-cores and are effective for certain classes of problems only, for which application-specific processor soft-cores were previously developed. In Self-Configurable FPGA-based Computer Systems the problem of designing the application-specific processor soft-cores is solved with use of the C2HDL tools, allowing them to be generated automatically.