GPU Memory Management Solution Supporting Incomplete Pages

Despite the increasing investment in integrated GPUs and next-generation interconnect research, discrete GPUs connected by PCI Express still account for the dominant position of the market, the management of data communication between CPU and GPU continues to evolve. This paper analyze the address translation overhead and migration latency introduced by this paged memory management solution in CPU-GPU heterogeneous systems. Based on the analysis, a new memory management scheme is proposed: paged memory management solution supporting incomplete pages, which can limit both address translation overhead and migration delay. “Incomplete” refers to a page that has only been partially migrated. This new memory management solution modifies the address translation and data migration process with only minor changes in hardware.

[1]  Margaret Martonosi,et al.  Reducing GPU offload latency via fine-grained CPU-GPU synchronization , 2013, 2013 IEEE 19th International Symposium on High Performance Computer Architecture (HPCA).

[2]  Ján Veselý,et al.  Observations and opportunities in architecting shared virtual memory for heterogeneous systems , 2016, 2016 IEEE International Symposium on Performance Analysis of Systems and Software (ISPASS).

[3]  Raphael Landaverde,et al.  An investigation of Unified Memory Access performance in CUDA , 2014, 2014 IEEE High Performance Extreme Computing Conference (HPEC).

[4]  Jasmin Ajanovic.  PCI express 3.0 overview , 2009, 2009 IEEE Hot Chips 21 Symposium (HCS).

[5]  David W. Nellans,et al.  Towards high performance paged memory for GPUs , 2016, 2016 IEEE International Symposium on High Performance Computer Architecture (HPCA).

[6]  Henry Wong,et al.  Analyzing CUDA workloads using a detailed GPU simulator , 2009, 2009 IEEE International Symposium on Performance Analysis of Systems and Software.

[7]  Stephen W. Keckler,et al.  Page Placement Strategies for GPUs within Heterogeneous Memory Systems , 2015, ASPLOS.

[8]  Erik Lindholm,et al.  NVIDIA Tesla: A Unified Graphics and Computing Architecture , 2008, IEEE Micro.

[9]  Rachata Ausavarungnirun,et al.  Mosaic: A GPU Memory Manager with Application-Transparent Support for Multiple Page Sizes , 2017, 2017 50th Annual IEEE/ACM International Symposium on Microarchitecture (MICRO).