Multi-dimensional intra-tile parallelization for memory-starved stencil computations

TL;DR

This paper introduces a multi-dimensional intra-tile parallelization technique for stencil computations on shared-cache multicore CPUs, significantly improving cache efficiency, performance, and energy savings especially for low-arithmetic-intensity stencils.

Contribution

It proposes a novel intra-tile parallelization method and an auto-tuner framework, Girih, to optimize stencil computations on shared-cache multicore architectures, outperforming existing frameworks.

Findings

Girih achieves superior performance across various stencil schemes.

The method reduces cache space requirements without hardware prefetching issues.

Energy consumption is decreased due to lower DRAM bandwidth usage.

Abstract

Optimizing the performance of stencil algorithms has been the subject of intense research over the last two decades. Since many stencil schemes have low arithmetic intensity, most optimizations focus on increasing the temporal data access locality, thus reducing the data traffic through the main memory interface with the ultimate goal of decoupling from this bottleneck. There are, however, only few approaches that explicitly leverage the shared cache feature of modern multicore chips. If every thread works on its private, separate cache block, the available cache space can become too small, and sufficient temporal locality may not be achieved. We propose a flexible multi-dimensional intra-tile parallelization method for stencil algorithms on multicore CPUs with a shared outer-level cache. This method leads to a significant reduction in the required cache space without adverse effects from hardware prefetching or TLB shortage. Our \emph{Girih} framework includes an auto-tuner to select optimal parameter configurations on the target hardware. We conduct performance experiments on two contemporary Intel processors and compare with the state-of-the-art stencil frameworks PLUTO and Pochoir, using four corner-case stencil schemes and a wide range of problem sizes. \emph{Girih} shows substantial performance advantages and best arithmetic intensity at almost all problem sizes, especially on low-intensity stencils with variable coefficients. We study in detail the performance behavior at varying grid size using phenomenological performance modeling. Our analysis of energy consumption reveals that our method can save energy by reduced DRAM bandwidth usage even at marginal performance gain. It is thus well suited for future architectures that will be strongly challenged by the cost of data movement, be it in terms of performance or energy consumption.