PSqueue: an Enhanced Memory Scheduling Queue Architecture For Multi-Threaded Streaming Memory Access

The surge in core counts of modern processors has led to a notable reduction in available memory bandwidth per core. This paper introduces PSqueue, an enhanced memory scheduling queue architecture designed to optimize multithreaded streaming memory access on Loongson processors. The proposed architecture addresses two significant challenges identified in pre-silicon performance evaluations: the stalling of compression-based memory scheduling queue during rank-level refreshes and Network-on-Chip(NoC) congestion during highly intensive memory accesses. To mitigate these issues, PSqueue, the combination of a preprocessing queue and a selection-based memory scheduling queue, is introduced. The selection-based scheduling queue allows ready-to-issue requests to bypass blocked ones, enhancing out-of-order scheduling capabilities. The preprocessing queue alleviates congestion by providing an escape channel for memory requests, thereby increasing the overall throughput of the memory controller. Experimental results indicate that the PSqueue architecture achieves an average memory bandwidth gain of 4.04%, with peak gains of 6.48% in multi-threaded streaming memory access scenarios. These innovations are in preparation for the design of the next-generation Loongson processors.

Authors

Institutions

Publication Details

Published
2024-12-13
DOI
https://doi.org/10.1109/hpcc64274.2024.00044
Primary Topic
Interconnection Networks and Systems
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

PSqueue: an Enhanced Memory Scheduling Queue Architecture For Multi-Threaded Streaming Memory Access

Huandong Wang, Weitong Wang, Han Xu, Wang Yuyangheng
Interconnection Networks and Systems
article

PSqueue: an Enhanced Memory Scheduling Queue Architecture For Multi-Threaded Streaming Memory Access

Huandong Wang, Weitong Wang, Han Xu, Wang Yuyangheng
article en

Abstract

The surge in core counts of modern processors has led to a notable reduction in available memory bandwidth per core. This paper introduces PSqueue, an enhanced memory scheduling queue architecture designed to optimize multithreaded streaming memory access on Loongson processors. The proposed architecture addresses two significant challenges identified in pre-silicon performance evaluations: the stalling of compression-based memory scheduling queue during rank-level refreshes and Network-on-Chip(NoC) congestion during highly intensive memory accesses. To mitigate these issues, PSqueue, the combination of a preprocessing queue and a selection-based memory scheduling queue, is introduced. The selection-based scheduling queue allows ready-to-issue requests to bypass blocked ones, enhancing out-of-order scheduling capabilities. The preprocessing queue alleviates congestion by providing an escape channel for memory requests, thereby increasing the overall throughput of the memory controller. Experimental results indicate that the PSqueue architecture achieves an average memory bandwidth gain of 4.04%, with peak gains of 6.48% in multi-threaded streaming memory access scenarios. These innovations are in preparation for the design of the next-generation Loongson processors.

Institute of Computing Technology (CN)
Research and Development
Affordable and clean energy
Openalex Percentile: Top 64%
Interconnection Networks and Systems
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

PSqueue: an Enhanced Memory Scheduling Queue Architecture For Multi-Threaded Streaming Memory Access — Huandong Wang, Weitong Wang, et al. · (2024) | TGRS Research Map | TGRS