LAUSR

Unified Memory is a single memory address space that is accessible by any processor (GPUs or CPUs) in a system. NVIDIA’s unified memory creates a pool of managed memory on top of physically separated CPU and GPU memories. NVIDIA’s unified memory automatically migrates page-level data on-demand, so programmers can quickly develop CUDA codes on heterogeneous machines. However, it is extremely difficult for programmers to decide when and how to efficiently use NVIDIA’s unified memory because (1) users are usually unaware of which unified memory hint (e.g., ReadMostly, PreferredLocation, AccessedBy) should be used for a data object in the application, and (2) it is tedious and error-prone to do manual memory management (i.e., manual code modifications) for various applications with difference data objects or inputs. We present XUnified, an advice controller which combines the offline training with the online adaptation to guide the optimal use of unified memory and discrete memory for various applications at runtime. The offline phase uses profiler-generated metrics to train a machine learning model, which is used to predict optimal memory advice choice and it then applies this advice to applications at runtime. We evaluate XUnified on NVIDIA Volta GPUs with a set of heterogeneous computing benchmarks. Results show that it achieves 94.0% prediction accuracy in correctly identifying the optimal memory advice choice with a maximal 34.3% reduction in kernel execution time.