We present a new open-source cosmological code, called SWIFT, designed to solve the equations of hydrodynamics using a particlebased approach (Smooth Particle Hydrodynamics) on hybrid shared / distributed-memory architectures. SWIFT was designed from the bottom up to provide excellent strong scaling on both commodity clusters (Tier-2 systems) and Top100-supercomputers (Tier-0 systems), without relying on architecture-specific features or specialized accelerator hardware. This performance is due to three main computational approaches:• Task-based parallelism for shared-memory parallelism, which provides fine-grained load balancing and thus strong scaling on large numbers of cores.• Graph-based domain decomposition, which uses the task graph to decompose the simulation domain such that the work, as opposed to just the data, as is the case with most partitioning schemes, is equally distributed across all nodes.• Fully dynamic and asynchronous communication, in which communication is modelled as just another task in the taskbased scheme, sending data whenever it is ready and deferring on tasks that rely on data from other nodes until it arrives.In order to use these approaches, the code had to be re-written from scratch, and the algorithms therein adapted to the task-based paradigm. As a result, we can show upwards of 60% parallel efficiency for moderate-sized problems when increasing the number of cores 512-fold, on both x86-based and Power8-based architectures.
This paper considers the performance attributes of the molecular simulation code, DL_POLY, as measured and analysed over the past two decades. Following a brief overview of HPC technology, and the performance improvements over that period, we define the benchmark casesfor both DL_POLY Classic and DL_POLY 3 & 4used in generating a broad overview of performance across well over 100 HPC systems, from the Cray T3E/1200 to today's Intel Skylake clusters and accelerator technology offerings from Intel's Xeon Phi co-processor family. Consideration is given to the tools that have proved helpful in analysing the code's performance. With a more rigorous analysis of performance on recent systems, we discuss the optimum choice of processor and interconnect, and present power measurements when running the code, comparing these to measurements for other community codes.
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