Use of massively parallel computing to improve modelling accuracy within the nuclear sector

2016 International Journal of Multiphysics  
Use of massively parallel computing to improve modelling accuracy within the nuclear sector Engineering design for the nuclear sector brings together a particular set of demands not observed elsewhere [1] . Nuclear power plants must be built in such a way that they must not suffer catastrophic failure under any given set of perceivable circumstances from earthquakes [2] to terrorist attacks [3] . The inevitable wear and tear of components over the plant's lifespan must be well understood and
more » ... dictable. At the plant's end of life it must be possible to be decommissioned, safely depositing activated parts of the machine ensuring long-term safety and sustainability of the surrounding area [4] . During operation, the constituent materials used ___________________________________ The last nuclear power station to be built in the UK, Sizewell-B, started construction in 1987 and therefore was designed years earlier using technology of that era. To put things into perspective the world's fastest computer in 1988 was the Cray Y-MP system capable of 2.6 GFlop/s (floating point operations per second) which is roughly comparable to an iPhone 4 (released in 2010) or the Intel Atom N2600 (released in 2011) both used as low power consuming mobile processors. Suffice to say that computing hardware has developed drastically since then [14] ; currently the world's most powerful computer is Tianhe-2, China, which is capable of 33.9 PFlop/s. That is to say, high performance computing power has increased by over 13 million fold in less than 30 years. The power of Tianhe-2 alone is equivalent to the entire global population solving 4.7 million calculations per second; this hardware enables us to approach problems that were previously impractical to solve but only if software makes efficient use of this technology. As with all current HPC systems, Tianhe-2 achieves faster computing times by utilising a greater number of computing cores rather than increased speed on a single processor [15] . This has been the standard practice since the mid-90s when vector computing fell out of favour and frequency scaling was abandoned [16] . Advances since 2010 have mostly been achieved through the introduction of heterogeneous supercomputers that use a mixed processing approach [17] . Typically, this consists of standard CPU processors coupled with GPUs but may also include field programmable gate arrays (FPGA) or bespoke coprocessors. This allows offloading of certain tasks to a different processing architecture better suited to the task, e.g. GPUs are particularly well suited to high-throughput tasks. Although this hardware offers additional computing capability, this can only be used if software is written such that it can make use of what is available [17] . As with parallel computing, where problems need to be sub-divided for distribution over processors, heterogeneous computing requires determining which parts can be offloaded to the coprocessor. Although efforts have been made to automate this process, little headway has been made. Therefore, just as parallel coding involves an additional layer of complexity so does coding for coprocessors, which increases development time. The Intel Xeon Phi processors, used in Tianhe-2, improve efficiency by including more computing hardware (i.e. computational cores) on a single processing board. In doing so it can process vast amount of data very quickly, the current limitation to speed-up is how quickly it can access this data. The architecture is made up of several tiers of memory, i.e. cache, RAM, HDD, each with increasing amount of space but 'further' away from the processor. Very large simulations can often have datasets that are terabytes in size, causing the data input/output (I/O) section of code to be the bottleneck. As computational power increases so too will the desire to handle larger datasets. The SAGE project [18], led by Seagate, aims to address this issue of hierarchical memory by using 'percipient storage methods' to allow computations that could be performed on any tier of data via advanced object based storage. This will be achieved by embedding the computational capabilities directly onto the storage thus drastically reducing data movement between compute and storage clusters, shown schematically in Figure 1 . As computing systems move towards exascale capability (a thousand petaflops), if processor power usage continues on its current trend, the demand for electricity will be prohibitively large. A single exascale system would require over a gigawatt of power, equal
doi:10.21152/1750-9548.10.2.215 fatcat:4ivn3dexend4jivbh4svy563xi