Paper Info
Title
On the Applicability of PEBS based Online Memory Access Tracking for Heterogeneous Memory Management at Scale.
Abstract
Operating systems have historically had to manage only a single type of memory device. The imminent availability of heterogeneous memory devices based on emerging memory technologies confronts the classic single memory model and opens a new spectrum of possibilities for memory management. Transparent data movement between different memory devices based on access patterns of applications is a desired feature to make optimal use of such devices and to hide the complexity of memory management to the end user. However, capturing memory access patterns of an application at runtime comes at a cost, which is particularly challenging for large-scale parallel applications that may be sensitive to system noise. In this work, we focus on the access pattern profiling phase prior to the actual memory relocation. We study the feasibility of using Intel's Processor Event-Based Sampling (PEBS) feature to record memory accesses by sampling at runtime and study the overhead at scale. We have implemented a custom PEBS driver in the IHK/-McKernel lightweight multi-kernel operating system, one of whose advantages is minimal system interference due to the lightweight kernel's simple design compared to other OS kernels such as Linux. We present the PEBS overhead of a set of scientific applications and show the access patterns identified in noise sensitive HPC applications. Our results show that clear access patterns can be captured with a 10% overhead in the worst-case and 1% in the best case when running on up to 128k CPU cores (2,048 Intel Xeon Phi Knights Landing nodes). We conclude that online memory access profiling using PEBS at large-scale is promising for memory management in heterogeneous memory environments.
Year
DOI
Venue
2018
10.1145/3286475.3286477
MCHPC'18: Workshop on Memory Centric High Performance Computing Dallas TX USA November, 2018
Keywords
Field
DocType
high-performance computing, operating systems, heterogeneous memory
Kernel (linear algebra),End user,Supercomputer,Xeon Phi,Computer science,Profiling (computer programming),Memory model,Memory management,Multi-core processor,Embedded system,Distributed computing
Conference
ISSN
ISBN
Citations 
Proceedings of the Workshop on Memory Centric High Performance Computing (2018) 50-57
978-1-4503-6113-2
0
PageRank 
References 
Authors
0.34
0
6
Name
Order
Citations
PageRank
Aleix Roca Nonell100.68
Balazs Gerofi210716.24
Leonardo Bautista-Gomez314811.33
Dominique Martinet410.69
Vicenç Beltran Querol500.34
Yutaka Ishikawa61449188.06