BLPP: Improving the Performance of GPGPUs with Heterogeneous Memory through Bandwidth- and Latency-Aware Page Placement

Abstract

GPGPUs with heterogeneous memory have surfaced as a promising solution to improve the programmability and flexibility of GPGPU computing. Despite the extensive prior works, relatively little work has been done to investigate holistic system software support for heterogeneity-aware memory management. To bridge this gap, we propose bandwidth- and latencyaware page placement (BLPP) for GPGPUs with heterogeneous memory. BLPP dynamically places pages across the heterogeneous memory nodes by preserving the optimal allocation ratio computed based on their performance characteristics. Our experimental results show that BLPP considerably outperforms the state-of-the-art technique and performs similarly to the staticbest version, which requires extensive offline profiling.