DRAGON: breaking GPU memory capacity limits with direct NVM access


Pak Markthub, Mehmet E. Belviranli, Seyong Lee, Jeffrey S. Vetter, and Satoshi Matsuoka, DRAGON: Breaking GPU Memory Capacity Limits with Direct NVM Access, SC 2018: The International Conference for High Performance Computing, Networking, Storage, and Analysis, November 2018


Heterogeneous computing with accelerators is growing in importance in high performance computing (HPC). Recently, application datasets have expanded beyond the memory capacity of these accelerators, and often beyond the capacity of their hosts. Meanwhile, nonvolatile memory (NVM) storage has emerged as a pervasive component in HPC systems because NVM provides massive amounts of memory capacity at affordable cost. Currently, for accelerator applications to use NVM, they must manually orchestrate data movement across multiple memories and this approach only performs well for applications with simple access behaviors. To address this issue, we developed DRAGON, a solution that enables all classes of GP-GPU applications to transparently compute on terabyte datasets residing in NVM. DRAGON leverages the page-faulting mechanism on the recent NVIDIA GPUs by extending capabilities of CUDA Unified Memory (UM). Our experimental results show that DRAGON transparently expands memory capacity and obtain additional speedups via automated I/O and data transfer overlapping.

Read Publication Keywords Accelerated Performance Computing Accelerators Heterogeneous Computing Runtime Systems Emerging Technologies High performance computing Performance Modeling & Analysis Resource management User-driven Software & Systems Resource Management

Last Updated: June 8, 2020 - 11:12 pm