.png){kind=icon}
.png){kind=icon}
Return
LPM: A Systematic Methodology for Concurrent Data Access Pattern Optimization from a Matching Perspective
.png){kind=icon}
PRE
AI
DOI：10.1109/TPDS.2019.2912573.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
As applications become increasingly data intensive, conventional computing systems become increasingly inefficient due to data access performance bottlenecks. While intensive efforts have been made in developing new memory technologies and in designing special purpose machines, there is a lack of solutions for evaluating and utilizing recent hardware advancements to address the memory-wall problem in a systematic way. In this study, we present the memory Layered Performance Matching (LPM) methodology to provide a systematic approach for data access performance optimization. LPM uniquely presents and utilizes the data access concurrency, in addition to data access locality, in a memory hierarchical system. The LPM methodology consists of models and algorithms, and is supported with a series of analytic results for its correctness. The rationale of LPM is to reduce the overall data access delay through the matching of data request rate and data supply rate at each layer of a memory hierarchy, with a balanced consideration of data locality, data concurrency, and latency hiding of data flow. Extensive experimentations on both physical platforms and software simulators confirm our theoretical findings, and they show that the LPM approach can be applied in diverse computing platforms and can effectively guide performance optimization of memory systems.
Keywords:
Concurrent computing
Optimization
Delays
Program processors
Hardware
Systematics
Analytical models
Memory wall
memory stall time
efficiency
performance optimization
layered performance matching (LPM)
memory concurrency
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
6
Papers:
5.2K
Citations:
1.1W