The Algorithmic Development of a Fully Asynchronous Conjugate Gradient Method

Abstract: Decentralized computing environments (DCE), or environments, such as edge computing, which utilize spatially distributed, non-hierarchical, and potentially heterogeneous computational units, present additional challenges such as communication delays and data locality which require asynchronous and resilient iterative methods to be practical. Such algorithms must be capable of progressing toward a solution even if one or more computational units is experiencing slowdowns, either due to poor communication connections or under-performing hardware. Previous work has shown that these challenges can be overcome for stationary iterative methods, such as asynchronous Jacobi (ASJ). In high-performance computing (HPC) settings, Krylov subspace iterative solvers, such as the conjugate gradient method (CG), are preferred for solving linear systems with symmetric positive-definite matrices due to their strong convergence guarantees. Despite requiring synchronous communication across all processors at every iteration, CG performs exceptionally well in shared memory and HPC environments. In DCE, the communication costs of synchronization may be considerably higher, as delays from even one computational unit prevent any other units from continuing. Further, the connection speeds between devices in edge computing will be considerable slower than the high-speed interconnects found in the HPC setting. We have developed an asynchronous CG (ACG) algorithm which removes these synchronization points by utilizing the partial direction vectors received at each iteration to form the next, mutually orthogonal search direction. Initial numerical results demonstrate the number of iterations required for convergence scales with the square root of the condition number of the matrix, as in the traditional CG algorithm. Further, we will present numerical results which compare ACG to CG and ASJ in both reliable computing environments and with injected delays and corruption to demonstrate the algorithmic performance of each for different computing environments.

See slides linked above for more info!