high-performance computing
such as movement of data, and new applications, for example to tackle simulations of the human brain. ‘We started seeing the end of Moore’s law
about ten years ago. It’s a little like the statement: ‘Te world will run out of fossil fuels by 2025’, said John Gustafson, an accomplished expert on supercomputer systems and creator of Gustafson’s law (also known as Gustafson-Barsis’ law) in computer engineering. ‘Tat’s not what happens. It just gets more
and more expensive to keep going the way we’re going, and the economics will lead us to alternatives.’ If you take Moore’s law, which applies to
transistors, it offers no improvements to the old-fashioned wiring connections between components, argues Gustafson. Today, the bottleneck within the architecture is that memory transfer takes longer to complete than a floating- point arithmetic operation — in some cases memory is 2,300 times slower, while transistor logic has improved by a factor of trillions. ‘Imagine a power cable as thick as a horse’s leg
next to a wire just a millimetre in diameter,’ said Gustafson. ‘It is easy to guess that the power cable
Deputy Director of the US Lawrence Berkeley National Laboratory. ‘Exascale will provide the computational
power needed to address the important science challenges, but that capability will come at an expense of a dramatic change in architectures, algorithms, and soſtware,’ said Jack Dongarra — the man who introduced the original fixed-size Linpack benchmark and one of the first contributors to the Top500 list. Dongarra is currently based at the Innovative Computing Laboratory at the University of Tennessee.
Energy and data movement Energy efficiency is one of the crucial factors that Dongarra cites. On current trends, the power needed to get to Exascale would be unaffordable. Other factors come into play too,
consumes maybe a thousand times as much energy as the wire. Te ratio is similar between “on-chip” transistor arithmetic connections and the connections that go “off-chip” to memory. I sometimes tell people combining today’s ultra-fast arithmetic units with a typical memory system is like mounting a large V8 gasoline engine on a tricycle and expecting it to be a high-performance vehicle.’ Running costs for systems associated with this bottleneck inevitably increase.
High bandwidth and low latency In addition to this problem of data-transfer latency, applications that require more complex computations have become more common. Tese calculations require high bandwidth, low latency, and data access using irregular patterns – something Linpack cannot test. In 2012, the Cray Blue Waters system at the National Center for Supercomputing
Applications, University of Illinois in Urbana- Champaign, US, refused to submit an entry to the TOP500 list. Blue Waters Project Director Bill Kramer said that the benchmark did not give an indication of real sustained performance and value, and was perhaps doing detriment to the community.
New benchmarks In a sense the issue is that no single computational task can ever reflect the overall complexity of applications that run within a supercomputer architecture. Jack Dongarra is well aware of the imbalance that is being created and the need to address today’s data-intensive applications. An alternative benchmark he has proposed could better compare computation
REAL APPLICATIONS
FALL FAR SHORT OF PEAK PERFORMANCE OR LINPACK PERFORMANCE
to data-access patterns. In 2013, Dongarra talked about a new benchmark called the ‘High Performance Conjugate Gradient’ (HPCG), which synchronises the benchmark to applications that use differential equations. ‘HPCG is getting people to see that
“real applications” in general fall far short of the peak performance or the Linpack performance numbers,’ said Dongarra. Tis was well known before this test, with
many papers available on the subject. HPCG is trying to catch up to what is known, said Kramer. ‘But, since HPCG is not a real application it
cannot speak for them; it is just a test of other architectural features — it is not clear whether it is proportional to application performance overall,’ said Kramer. Dongarra hopes there will be an effort to
optimise both hardware and algorithms to work better together. Kramer said that HPCG is an important step
forward to improve the benchmarking situation, but it is insufficient as a single measure of
➤
Blue Waters at the US National Center for Supercomputing Applications. Project Director Bill Kramer refused to submit an entry to the TOP500
www.scientific-computing.com l @scwmagazine JUNE/JULY 2015 23
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