I’m only summarizing here some sections from the Roadmap document of the International Extascale Software Project. This project has on board well-known scientists and professionals shaping the next software stack for machines with millions of cores with exascale computing power. This document is aggregating thoughts and ideas about how HPC and multi-core community should remodel the software stack. Machines with millions of cores will be a reality in few years if Moore’s law continues to work (e.g. doubling the number of cores inside microprocessors every 18 months and selling them 50% cheaper). HPC community should make serious efforts to be ready for such massively parallel machines. Authors believe that this is best achieved if we focus our efforts on understanding expected architectural developments and possible applications that will run on these platforms. The roadmap document is defining four main paths to the exascale software stack:

  1. Systems software, which includes: operating systems, I/O systems, systems management, and external environments.
  2. Development environments, which includes: programming models, frameworks, compilers, numerical libraries, and debugging tools.
  3. Applications, which includes: algorithms, data analysis and visualization, and scientific data management
  4. Cross Cutting Dimensions, which include important components, such as power management, performance optimization, and programmability.

Suggestions of changes in these paths depend on some important technology trends in HPC platforms:

  1. Technology Trends:
    1. Concurrency: processors’ concurrency will be very large and finer-grained
    2. Reliability: increased number of transistors and functional units will impose a reliability challenge
    3. Power consumption: it will increase from 10 Mega Watts these days to be close to 100 Mega Watts around 2020.
  2. Science Trends. Many research and governmental agencies specified upcoming scientific challenges that HPC can help as we cross into the exascale era, major research areas: climate, high-energy physics, nuclear physics, fusion energy sciences, nuclear energy, biology, materials science and chemistry, and national nuclear security
  3. Politico-economic trends: HPC is growing 11% every year. Main demands come from governments and large research institutions. It will continue this way since a lot of commercial entities depend on capacity rather than computing power to expand their infrastructures.

           
     

I’m interested in quickly sharing with you some key points from each of these paths. I think this document is more visionary compared to Berkeley’s former document: The Landscape of Parallel Computing Research. My next posting will be summarizing the first path of systems software.

This posting is part of a series summarizing the roadmap document of the Exascale Software Project:

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