Analytical Cost Metrics : Days of Future Past
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As we move towards the exascale era, the new architectures must be capable of running the massive computational problems efficiently. Scientists and researchers are continuously investing in tuning the performance of extreme-scale computational problems. These problems arise in almost all areas of computing, ranging from big data analytics, artificial intelligence, search, machine learning, virtual/augmented reality, computer vision, image/signal processing to computational science and bioinformatics. With Moore's law driving the evolution of hardware platforms towards exascale, the dominant performance metric (time efficiency) has now expanded to also incorporate power/energy efficiency. Therefore, the major challenge that we face in computing systems research is: "how to solve massive-scale computational problems in the most time/power/energy efficient manner?" The architectures are constantly evolving making the current performance optimizing strategies less applicable and new strategies to be invented. The solution is for the new architectures, new programming models, and applications to go forward together. Doing this is, however, extremely hard. There are too many design choices in too many dimensions. We propose the following strategy to solve the problem: (i) Models - Develop accurate analytical models (e.g. execution time, energy, silicon area) to predict the cost of executing a given program, and (ii) Complete System Design - Simultaneously optimize all the cost models for the programs (computational problems) to obtain the most time/area/power/energy efficient solution. Such an optimization problem evokes the notion of codesign.
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