Google Research

SWIFT: Using task-based parallelism, fully asynchronous communication, and graph partition-based domain decomposition for strong scaling on more than 100000 cores.

PASC16, EPFL, Lausanne, Switzerland (2016)


We present a new open-source cosmological code, called \swift, designed to solve the equations of hydrodynamics using a particle-based approach (Smooth Particle Hydrodynamics) on hybrid shared / distributed-memory architectures. \swift was designed from the bottom up to provide excellent {\em 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:


\item \textbf{Task-based parallelism} for shared-memory parallelism, which provides fine-grained load balancing and thus strong scaling on large numbers of cores.

\item \textbf{Graph-based domain decomposition}, which uses the task graph to decompose the simulation domain such that the {\em work}, as opposed to just the {\em data}, as is the case with most partitioning schemes, is equally distributed across all nodes.

\item \textbf{Fully dynamic and asynchronous communication}, in which communication is modelled as just another task in the task-based scheme, sending data whenever it is ready and deferrin 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.

Learn more about how we do research

We maintain a portfolio of research projects, providing individuals and teams the freedom to emphasize specific types of work