Using Criticality of GPU Accesses in Memory Management for CPU-GPU Heterogeneous Multi-Core Processors

Abstract

Heterogeneous chip-multiprocessors with CPU and GPU integrated on the same die allow sharing of critical memory system resources among the CPU and GPU applications. Such architectures give rise to challenging resource scheduling problems. In this paper, we explore memory access scheduling algorithms driven by criticality of GPU accesses in such systems. Different GPU access streams originate from different parts of the GPU rendering pipeline, which behaves very differently from the typical CPU pipeline requiring new techniques for GPU access criticality estimation. We propose a novel queuing network model to estimate the performance-criticality of the GPU access streams. If a GPU application performs below the quality of service requirement (e.g., frame rate in 3D scene rendering), the memory access scheduler uses the estimated criticality information to accelerate the critical GPU accesses. Detailed simulations done on a heterogeneous chip-multiprocessor model with one GPU and four CPU cores running heterogeneous mixes of DirectX, OpenGL, and CPU applications show that our proposal improves the GPU performance by 15% on average without degrading the CPU performance much. Extensions proposed for the mixes containing GPGPU applications, which do not have any quality of service requirement, improve the performance by 7% on average for these mixes.