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Sparsified Preconditioned Conjugate Gradient Solver on GPUs
DescriptionPreconditioned iterative sparse linear solvers are memory-efficient for large scientific simulations, but the dependences between iterations introduced by preconditioners limit parallelization. This issue is exacerbated on GPUs, which feature many parallel cores. We propose a sparsified preconditioned conjugate gradient (SPCG) solver that increases parallelism by reducing dependences through sparsification, while preserving convergence behavior. We evaluate the proposed SPCG using both ILU(0) and ILU(K) preconditioners on a wide range of symmetric positive definite (SPD) matrices. The proposed SPCG improves the performance of the iterative phase of SPCG by a geometric mean speedup of 1.23$\times$ and 1.65$\times$ over the non-sparsified PCG using ILU(0) and ILU(K), respectively, on an NVIDIA A100 GPU. SPCG also yields geometric mean end-to-end speedups of 1.68$\times$ and 3.73$\times$ over the non-sparsified versions with ILU(0) and ILU(K), respectively, on the same platform.
Event Type
Paper
TimeTuesday, 18 November 20253:30pm - 3:52pm CST
Location263-264
Performance Measurement, Modeling, & Tools