Abstract:
Three-dimensional (3D) scene rendering is implemented in the form of a pipeline in graphics processing units (GPUs). In different stages of the pipeline, different types of data get accessed. These include, for instance, vertex, depth, stencil, render target (same as pixel color), and texture sampler data. The GPUs traditionally include small caches for vertex, render target, depth, and stencil data as well as multi-level caches for the texture sampler units. Recent introduction of reasonably large last-level caches (LLCs) shared among these data streams in discrete as well as integrated graphics hardware architectures has opened up new opportunities for improving 3D rendering. The GPUs equipped with such large LLCs can enjoy far-flung intraand inter-stream reuses. However, there is no comprehensive study that can help graphics cache architects understand how to effectively manage a large multi-megabyte LLC shared between different 3D graphics streams.

In this paper, we characterize the intra-stream and inter-stream reuses in 52 frames captured from eight DirectX game titles and four DirectX benchmark applications spanning three different frame resolutions. Based on this characterization, we propose graphics stream-aware probabilistic caching (GSPC) that dynamically learns the reuse probabilities and accordingly manages the LLC of the GPU. Our detailed trace-driven simulation of a typical GPU equipped with 768 shader thread contexts, twelve fixed-function texture samplers, and an 8 MB 16-way LLC shows that GSPC saves up to 29.6% and on average 13.1% LLC misses across 52 frames compared to the baseline state-of-the-art two-bit dynamic re-reference interval prediction (DRRIP) policy. These savings in the LLC misses result in a speedup of up to 18.2% and on average 8.0%. On a 16 MB LLC, the average speedup achieved by GSPC further improves to 11.8% compared to DRRIP.