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Subject Domain (in DDC) Computer science, information & general works ♦ Data processing & computer science
Subject Keyword Hpc Application Performance ♦ Extreme Scale Computer ♦ Small Amount ♦ Application Improvement ♦ Significant Drop ♦ Hpc Application Programmer ♦ Application Performance ♦ Concurrent Device ♦ Level Improvement ♦ Transistor Count ♦ Component Failure ♦ General Auto-parallelization Technique ♦ Biological Cortical System ♦ Predictive Associative Memory ♦ Today High Performance Computer ♦ Parallel System ♦ Hpc System ♦ Sheer Number ♦ Biological Inspiration ♦ Pattern Recognition ♦ Multicore Programming ♦ Clock Frequency ♦ Significant Fraction ♦ Traditional Processor Core ♦ Basic Premise
Abstract We propose a radically new, biologically inspired, model of extreme scale computer on which application performance automatically scales with the transistor count even in the face of component failures. Today high performance computers are massively parallel systems composed of potentially hundreds of thousands of traditional processor cores, formed from trillions of transistors, consuming megawatts of power. Unfortunately, increasing the number of cores in a system, unlike increasing clock frequencies, does not automatically translate to application level improvements. No general auto-parallelization techniques or tools exist for HPC systems. To obtain application improvements, HPC application programmers must manually cope with the challenge of multicore programming and the significant drop in reliability associated with the sheer number of transistors. Drawing on biological inspiration, the basic premise behind this work is that computation can be dramatically accelerated by integrating a very large-scale, system-wide, predictive associative memory into the operation of the computer. The memory effectively turns computation into a form of pattern recognition and prediction whose result can be used to avoid significant fractions of computation. To be effective the expectation is that the memory will require billions of concurrent devices akin to biological cortical systems, where each device implements a small amount of storage,
Educational Role Student ♦ Teacher
Age Range above 22 year
Educational Use Research
Education Level UG and PG ♦ Career/Technical Study