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.
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-...
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.
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...
Original proposal to use HPCG as an alternative to HPL for supercomputer rankings: http://www.sandia.gov/~maherou/docs/HPCG-Benchmark.pdf
The reality is that there are many dimensions to supercomputing performance and it's impossible for one number to capture the utility of the machine. Our HPGMG benchmark (https://hpgmg.org) attempts to strike a balance and give useful supplementary information. I do think it's better than any other single benchmark for evaluating today's machines and will also prove to be more durable over time.
With respect to deep learning and other applications using CG or related algorithms, the bottlenecks depend on the scale, and ability to expose locality, and operator/preconditioner representation. If there is no locality, then matrix-vector products require all-to-all communication which tend to dwarf the cost of the reductions in CG. Even with locality in the matrix-vector product, preconditioners often need to communicate globally in a scalable way similar to HPGMG. Operators need not be represented as a table of numbers or a sparse matrix format, but could use a tensor product, fast transform, or other information to compute the action using less storage. If they are represented explicitly (sparse or dense), then matrix-vector product performance (thus CG as a whole) is dominated by memory bandwidth for problem sizes that do not fit in cache. HPGMG tries to strike a balance between memory bandwidth demands and compute using a matrix-free representation. HPGMG also reports dynamic range expressed as Performance versus Time-to-solution as the problem size is varied, which allows applications to see performance barriers that might be relevant to them (e.g., see how Titan cannot do a solve in less than 200 ms while Edison can do 50 ms, and how that relates to climate simulation performance targets; see slide 7 of https://jedbrown.org/files/20150624-Versatility.pdf).
One of the biggest reasons for use of HPL is that many sizing considerations can be based off of the theoretical calculations.
But anyway this is very interesting. I definitely need to check this out.
HPGMG is representative of most structure-exploiting algorithms in that it does not have this abundance of flops, thus theoretical performance is actively constrained by both memory bandwidth and flop/s. We see many active constraints in practice; e.g., improving any of peak flop/s, memory bandwidth, network latency, or network bandwidth produces a tangible improvement in HPGMG performance. Depending on the fidelity of the performance model, these dimensions can be a fairly accurate predictor of performance, but ILP, compiler quality, on-node synchronization latency, cache sizes, and similar factors also matter (more for HPGMG-FE than HPGMG-FV).
I think it is actually quite undesirable for benchmark performance to be trivially computed from one parameter in machine provisioning. No computing center has a mission statement asking for a place on a benchmark ranking list (like Top500). Instead, they have a scientific or engineering mandate. Press releases tend to overemphasize the ranking and I think it is harmful to the science any time the benchmark takes precedence over the expected scientific workload. HPGMG is intended to be representative in the sense that if you build an "HPGMG Machine", you'll get a balanced, versatile machine that scientists and engineers in most disciplines will be happy with. I'd still rather the centers focus on their workload instead of HPGMG.
What I meant by asynchronous is that not all terms in a gradient are required to be summed in the same step.
The transpose step in Sibyl is implemented in the Shuffle and Reduce phases. The filesystem is used to hold the temporary data. Nevertheless even for large systems, very few steps are required, and step times are reasonable, even compared to modern supercomputers. This is a tribute primarily to the design of sibyl and the implementation of MapReduce at Google.
This is all explained in online versions of the Sibyl presentation. I really wish more people from DOE who write modern solvers would pay attention to this stuff.
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?
1. http://www.theplatform.net/2015/07/13/top-500-supercomputer-...
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.
x20 ought to take about five years and a half.
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.
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.