The presentation, however, is based on experiments done as part of the DEEP Project, which investigates highly parallel computing models that help speed up supercomputers. In addition to investigating software development and programming tools, the project involves building high-performance systems called JUROPA (Jülich Research on Petaflop Architectures).
"My team was building the very effective JUROPA system together with Bull, Partec and Intel. This machine is ideal for highly complex problems that exhibit a lower concurrency, in general. Most codes live somewhere in between. I want to find out, if we can bring the concepts together. The different architectures can assign the ... different code parts according to the concurrency," Lippert said.
Performance in supercomputers has scaled thanks to new programming models and hardware such as accelerators and graphics cards. Code needs to be structured according to concurrency levels, such as in programming languages like the one provided by Barcelona Supercomputing Center's OmpSS, Lippert said.
Despite the title of the presentation, the aim is not to challenge Amdahl's law, Lippert said.
"On the contrary, I think, we are not taking [Amdahl's law] serious enough. It is simply obvious that we should adapt the right piece of hardware to the corresponding concurrency," Lippert said. "Only this approach has the potential to be most energy efficient and performance oriented at the same time."
While Lippert's blog entry did not provide much detail on how Amdahl's law is being challenged, academics said it is always interesting to see the law being revisited.
Unlike Moore's Law, which is an observation, Amdahl's law cannot be "broken" in any mathematical sense, and is still relevant, said Paul Lu, associate professor at University of Alberta's Department of Computing Science.
As with any mathematical theorem, if the assumptions are no longer true, the law is not relevant. "That is not to say that the law has been 'broken'; it just means that the law does not apply to that situation," Lu said.
While the size of a problem can be meaningfully increased, there are cases in which the size of a problem is fixed, and Amdahl's law is relevant.
"For fixed-sized problems, Amdahl's law is a sobering reminder of reality," Lu said.
Faster computers are designed to reduce execution time for fixed-size problems, but there are other metrics that need to be taken into account, said Xian-He Sun, chair and professor of the Department of Computer Science at the Illinois Institute of Technology.
"Amdahl's law is a law, which shows even when you have reduced your communication and other overhead — such as memory access delay, software and hardware delay — to zero, with the sequential portion of your program, your parallel processing gain is still very limited," Xian-He said.
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