5 ms·

10 years ago the fastest supercomputer was BlueGene/L which was rated at 136.8 TFlop/s. The current fastest supercomputer is rated at 33,862.7 TFlop/s, or 247 t

10 years ago the fastest supercomputer was BlueGene/L which was rated at 136.8 TFlop/s. The current fastest supercomputer is rated at 33,862.7 TFlop/s, or 247 times faster. It seems to me that the aim of taking 10 years to build a supercomputer that is only 20 times faster than the current one might fall a little short if it's aiming to take the top spot.

edpichler 11y ago
I think this do not advanced more just because of economic reasons. I have have read last 10 years that Processing is a lot more cheaper did by network of computers and clusters, instead of a expensive supercomputer that also demands an appropriate building and infrastructure.
- TallGuyShort 11y ago
  Modern supercomputers are essentially clusters, but with much more advanced network topologies and technologies, shared storage, etc. It's not just one monolothic machine. However, the types of computation performed by the top supercomputers are rarely the "embarassingly parallel" programs you can easily distribute via an @Home-style program, or something like Hadoop. They do depend heavily on very reliable, very low latency, high bandwidth networks.
  - deleted 11y ago
    [deleted]
murbard2 11y ago
Doubling time: 10 years * log(2)/log(247) ~ 15 months x20 ought to take about five years and a half.
bjacobel 11y ago
It could very well be that there's diminishing returns involved but I agree - they should be aiming to surpass the current tech by at last 100x in the next ten years.
- bbgm 11y ago
  The current trend [1] would suggest that exascale is not on that kind of trajectory. The dominance of non US countries (especially China) in the Top500 rankings is as much a driver here. 1. http://www.theplatform.net/2015/07/13/top-500-supercomputer-list-reflects-shifting-state-of-global-hpc-trends/ http://www.theplatform.net/2015/07/13/top-500-supercomputer-...
  - semi-extrinsic 11y ago
    1. The top 500 list is broken, as people who are serious about real world applications give essentially zero shits about Linpack. 2. The dominance of China is achieved through the use of Xeon Phi accelerators, which may be great for Linpack, but have not made much of a splash for applications yet. GPUs are solidly beating Intel's accelerator offering both on adaptation and performance.
phreeza 11y ago
The extrapolation on the top500 supercomputer list [1] estimates the first EFlop computer in 2019. The math in the article is weird. They say 20x faster, but 20x33 PFlops is quite a bit less than 1EFlop. [1] http://www.top500.org/statistics/perfdevel/ http://www.top500.org/statistics/perfdevel/
- bpodgursky 11y ago
  The extrapolation in the linked graph pretty clearly doesn't track increases after 2012 correctly. Tracking the past 3-4 years puts 2019 at around 600PF.
- nickpsecurity 11y ago
  See my comment above. Has all the numbers you might need.
vmarsy 11y ago
This isn't only about the FLOPS, the big trend of these countries* ordering new supercomputers by 2020/2025 is very focused on power. Current supercomputers consume a lot. Also the FLOPS measurement is a bit broken: It focuses on dense linear algebra problem, for which GPU or other accelerators boost the results easily. If all you plan to do is running simulations that are easily parallelized on GPU it is fine, for other types of programs it is hard to tell which is the fastest supercomputer. * France is also ordering a would -be top 10 supercomputer : http://www.hpcwire.com/off-the-wire/the-cea-agency-and-atos-team-to-deliver-exaflop-supercomputer-by-2020/ http://www.hpcwire.com/off-the-wire/the-cea-agency-and-atos-...
- amelius 11y ago
  > Also the FLOPS measurement is a bit broken Does anybody have a better measurement? Perhaps it could be the size of the matrix that can be inverted on it in an hour of time, with IEEE double precision floats, using some standard algorithm.
  - vmarsy 11y ago
    It depends, I meant broken if you plan to compute other types of problems. For instance some big graph problem instead of involving linear algebra. Then you would favor the benchmarks from http://www.graph500.org/ http://www.graph500.org/. But what if you want to optimize for programs that are communication intensive, or memory intensive? Should the FLOPS of a very specific linear algebra suite be used as the metric of best computers?
  - ajdecon 11y ago
    The "High Performance Conjugate Gradients" benchmark was proposed a couple years ago as an alternative metric for ranking supercomputers. Its proponents claim its behavior is more similar to real applications (irregular access patterns, lower ratio of computation to memory access, etc), compared to linear algebra problems like the "High Performance Linpack" benchmark currently used by the Top500. The different performance numbers for top systems on HPCG vs HPL are pretty striking: http://www.hpcg-benchmark.org/custom/index.html?lid=155&slid=279 http://www.hpcg-benchmark.org/custom/index.html?lid=155&slid... Original proposal to use HPCG as an alternative to HPL for supercomputer rankings: http://www.sandia.gov/~maherou/docs/HPCG-Benchmark.pdf http://www.sandia.gov/~maherou/docs/HPCG-Benchmark.pdf
nerdcity 11y ago
Then they just can just "order" another one. We're talking government here. Declaring faster computers by government fiat is already a dumb idea.
robgibbons 11y ago
Computer hardware innovation is a textbook example of diminishing returns. With each improvement in processor performance, size, energy usage, and heat management, it becomes more expensive to push the tech further. We're currently witnessing this effect in action with the recent stagnation in consumer processor speeds. They are still getting better in size, energy and heat management, but average speeds have hovered around 2.5Ghz for years now.

edpichler 11y ago

I think this do not advanced more just because of economic reasons. I have have read last 10 years that Processing is a lot more cheaper did by network of computers and clusters, instead of a expensive supercomputer that also demands an appropriate building and infrastructure.

TallGuyShort 11y ago
Modern supercomputers are essentially clusters, but with much more advanced network topologies and technologies, shared storage, etc. It's not just one monolothic machine. However, the types of computation performed by the top supercomputers are rarely the "embarassingly parallel" programs you can easily distribute via an @Home-style program, or something like Hadoop. They do depend heavily on very reliable, very low latency, high bandwidth networks.
- deleted 11y ago
  [deleted]

murbard2 11y ago

Doubling time: 10 years * log(2)/log(247) ~ 15 months x20 ought to take about five years and a half.

bjacobel 11y ago

It could very well be that there's diminishing returns involved but I agree - they should be aiming to surpass the current tech by at last 100x in the next ten years.

bbgm 11y ago
The current trend [1] would suggest that exascale is not on that kind of trajectory. The dominance of non US countries (especially China) in the Top500 rankings is as much a driver here. 1. http://www.theplatform.net/2015/07/13/top-500-supercomputer-list-reflects-shifting-state-of-global-hpc-trends/ http://www.theplatform.net/2015/07/13/top-500-supercomputer-...
- semi-extrinsic 11y ago
  1. The top 500 list is broken, as people who are serious about real world applications give essentially zero shits about Linpack. 2. The dominance of China is achieved through the use of Xeon Phi accelerators, which may be great for Linpack, but have not made much of a splash for applications yet. GPUs are solidly beating Intel's accelerator offering both on adaptation and performance.

phreeza 11y ago

The extrapolation on the top500 supercomputer list [1] estimates the first EFlop computer in 2019. The math in the article is weird. They say 20x faster, but 20x33 PFlops is quite a bit less than 1EFlop. [1] http://www.top500.org/statistics/perfdevel/ http://www.top500.org/statistics/perfdevel/

bpodgursky 11y ago
The extrapolation in the linked graph pretty clearly doesn't track increases after 2012 correctly. Tracking the past 3-4 years puts 2019 at around 600PF.
nickpsecurity 11y ago
See my comment above. Has all the numbers you might need.

vmarsy 11y ago

This isn't only about the FLOPS, the big trend of these countries* ordering new supercomputers by 2020/2025 is very focused on power. Current supercomputers consume a lot. Also the FLOPS measurement is a bit broken: It focuses on dense linear algebra problem, for which GPU or other accelerators boost the results easily. If all you plan to do is running simulations that are easily parallelized on GPU it is fine, for other types of programs it is hard to tell which is the fastest supercomputer. * France is also ordering a would -be top 10 supercomputer : http://www.hpcwire.com/off-the-wire/the-cea-agency-and-atos-team-to-deliver-exaflop-supercomputer-by-2020/ http://www.hpcwire.com/off-the-wire/the-cea-agency-and-atos-...

amelius 11y ago
> Also the FLOPS measurement is a bit broken Does anybody have a better measurement? Perhaps it could be the size of the matrix that can be inverted on it in an hour of time, with IEEE double precision floats, using some standard algorithm.
- vmarsy 11y ago
  It depends, I meant broken if you plan to compute other types of problems. For instance some big graph problem instead of involving linear algebra. Then you would favor the benchmarks from http://www.graph500.org/ http://www.graph500.org/. But what if you want to optimize for programs that are communication intensive, or memory intensive? Should the FLOPS of a very specific linear algebra suite be used as the metric of best computers?
- ajdecon 11y ago
  The "High Performance Conjugate Gradients" benchmark was proposed a couple years ago as an alternative metric for ranking supercomputers. Its proponents claim its behavior is more similar to real applications (irregular access patterns, lower ratio of computation to memory access, etc), compared to linear algebra problems like the "High Performance Linpack" benchmark currently used by the Top500. The different performance numbers for top systems on HPCG vs HPL are pretty striking: http://www.hpcg-benchmark.org/custom/index.html?lid=155&slid=279 http://www.hpcg-benchmark.org/custom/index.html?lid=155&slid... Original proposal to use HPCG as an alternative to HPL for supercomputer rankings: http://www.sandia.gov/~maherou/docs/HPCG-Benchmark.pdf http://www.sandia.gov/~maherou/docs/HPCG-Benchmark.pdf

nerdcity 11y ago

Then they just can just "order" another one. We're talking government here. Declaring faster computers by government fiat is already a dumb idea.

robgibbons 11y ago

Computer hardware innovation is a textbook example of diminishing returns. With each improvement in processor performance, size, energy usage, and heat management, it becomes more expensive to push the tech further. We're currently witnessing this effect in action with the recent stagnation in consumer processor speeds. They are still getting better in size, energy and heat management, but average speeds have hovered around 2.5Ghz for years now.