Emulating Hybrid Memory on NUMA Hardware
July 31, 2018 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Shoaib Akram, Jennifer B. Sartor, Kathryn S. McKinley, Lieven Eeckhout
arXiv ID
1808.00064
Category
cs.PL: Programming Languages
Citations
4
Venue
arXiv.org
Last Checked
3 months ago
Abstract
Non-volatile memory (NVM) has the potential to disrupt the boundary between memory and storage, including the abstractions that manage this boundary. Researchers comparing the speed, durability, and abstractions of hybrid systems with DRAM, NVM, and disk to traditional systems typically use simulation, which makes it easy to evaluate different hardware technologies and parameters. Unfortunately, simulation is extremely slow, limiting the number of applications and dataset sizes in the evaluation. Simulation typically precludes realistic multiprogram workloads and considering runtime and operating system design alternatives. Good methodology embraces a variety of techniques for validation, expanding the experimental scope, and uncovering new insights. This paper introduces an emulation platform for hybrid memory that uses commodity NUMA servers. Emulation complements simulation well, offering speed and accuracy for realistic workloads, and richer software experimentation. We use a thread-local socket to emulate DRAM and the remote socket to emulate NVM. We use standard C library routines to allocate heap memory in the DRAM or NVM socket for use with explicit memory management or garbage collection. We evaluate the emulator using various configurations of write-rationing garbage collectors that improve NVM lifetimes by limiting writes to NVM, and use 15 applications from three benchmark suites with various datasets and workload configurations. We show emulation enhances simulation results. The two systems confirm most trends, such as NVM write and read rates of different software configurations, increasing our confidence for predicting future system effects. Emulation adds novel insights, such as the non-linear effects of multi-program workloads on write rates.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Programming Languages
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Tensor Comprehensions: Framework-Agnostic High-Performance Machine Learning Abstractions
R.I.P.
π»
Ghosted
Glow: Graph Lowering Compiler Techniques for Neural Networks
R.I.P.
π»
Ghosted
Learnable Programming: Blocks and Beyond
R.I.P.
π»
Ghosted
Scenic: A Language for Scenario Specification and Scene Generation
R.I.P.
π»
Ghosted
Vandal: A Scalable Security Analysis Framework for Smart Contracts
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted