Characterizing the efficiency of multicore and manycore processors for the solution of sparse linear systems

Abstract

We analyze the efficiency of servers equipped with state-of-the-art general-purpose multicore processors as well as platforms based on accelerators such as graphics processing units (GPUs) and the Intel Xeon Phi. Following the proposal recently advocated in the High Performance Conjugate Gradient (HPCG) benchmark, we leverage for this purpose efficient implementations of ILUPACK, a preconditioned solver for sparse linear systems that comprises numerical kernels and data access patterns analogous to those of HPCG. Our study analyzes the (computational) performance and energy efficiency, with two different metrics for each: time/floating-point throughput for the former; and energy/floating-point throughput-per-Watt for the latter.Keywords Sparse linear algebra · preconditioned iterative solvers · ILUPACK · energy efficiency · multicore processors · hardware accelerators.

Performance and Energy Optimization of the Iterative Solution of Sparse Linear Systems on Multicore Processors

“…Journal [19] Aliaga, J. I., Barreda, M., Dufrechou, E., Ezzatti, P., Quintana-Ortí, E. S. Characterizing the efficiency of multicore and manycore processors for the solution of sparse linear systems. Computer Science -Research and Development (2015), Vol.31(4), pp.…”

“…Furthermore, we proposed several simple yet accurate power models that captured the variations of average power that result from the introduction of the energy-aware strategies as well as the impact of the P-states into ILUPACK's runtime. Additionally, in [19] we analyzed the performance and energy efficiency of the OmpSs version of ILUPACK in different state-of-the-art general-purpose multicore processors and accelerators such as the Intel Xeon Phi. These analysis allowed us to characterize the efficiency of the platforms.…”