Two current Paths to Exascale Hardware
There are a number of constraints of the architecture for exascale. One constraint is cost. Everybody says a machine can cost no more than $200 million. You’re going to spend half your money on memory, so you have take that into consideration.
There are also other constraints that come into play. For example, the machine can consume no more than 20 MW. That’s thought to be the upper limit for a reasonable machine from the standpoint of power, cooling, etc.
Currently there are two ways to exascale hardware. One is going to be lightweight processors. By lightweight, we mean things like the Blue Gene [PowerPC] processor. One general way to characterize this architecture is 1GHz in processor speed, one thousand cores per node, and one million nodes per system. A second path to exascale is commodity processors together with accelerators, such as GPUs. The software would support both those models, although there would be differences we’d have to deal with.
Both of the models generate 10^18 FLOPS and both have on the order of a billion threads of execution. We realize that represents a lot of parallel processing and we need to support that in some manner. That’s today’s view of the hardware, although clearly, that could change.
The goal of the IESP is to come up with an international plan for developing the next generation of open source software for high performance scientific computing. So our goal is to develop a roadmap, and that roadmap would lay out issues, priorities, and describe the software stack that’s necessary for exascale.
This software stack has things from the system side, like operating systems, I/O, the external environment and system management. It also deals with the development environment, which looks at programming models, frameworks for developing applications, compilers, numerical libraries and debugging tools. There’s another element that tries to integrate applications and use them as a vehicle for testing the ideas.
And finally there’s an avenue that I’ll call cross-cutting issues — issues that really impact all of the software that we’re talking about. That has to do with resilience, power management, performance optimization, and overall programmability.
Today we don’t really have this global evaluation of missing components within the stack itself. We want to make sure that we understand what the needs are and that the research would cover those needs. So we’re trying to define and develop the priorities to help with this planning process.
Brian Wang is a Futurist Thought Leader and a popular Science blogger with 1 million readers per month. His blog Nextbigfuture.com is ranked #1 Science News Blog. It covers many disruptive technology and trends including Space, Robotics, Artificial Intelligence, Medicine, Anti-aging Biotechnology, and Nanotechnology.
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