Darwin: A DRAM-based Multi-level Processing-in-Memory Architecture for Data Analytics

Fuente: arXiv
Saved in:
Bibliographic Details
Main Authors: Kim, Donghyuk, Kim, Jae-Young, Han, Wontak, Won, Jongsoon, Choi, Haerang, Kwon, Yongkee, Kim, Joo-Young
Format: Preprint
Published: 2023
Subjects:
Online Access:
Tags: Add Tag
No Tags, Be the first to tag this record!
_version_ 1866915504977870848
author Kim, Donghyuk
Kim, Jae-Young
Han, Wontak
Won, Jongsoon
Choi, Haerang
Kwon, Yongkee
Kim, Joo-Young
author_facet Kim, Donghyuk
Kim, Jae-Young
Han, Wontak
Won, Jongsoon
Choi, Haerang
Kwon, Yongkee
Kim, Joo-Young
contents Processing-in-memory (PIM) architecture is an inherent match for data analytics application, but we observe major challenges to address when accelerating it using PIM. In this paper, we propose Darwin, a practical LRDIMM-based multi-level PIM architecture for data analytics, which fully exploits the internal bandwidth of DRAM using the bank-, bank group-, chip-, and rank-level parallelisms. Considering the properties of data analytics operators and DRAM's area constraints, Darwin maximizes the internal data bandwidth by placing the PIM processing units, buffers, and control circuits across the hierarchy of DRAM. More specifically, it introduces the bank processing unit for each bank in which a single instruction multiple data (SIMD) unit handles regular data analytics operators and bank group processing unit for each bank group to handle workload imbalance in the condition-oriented data analytics operators. Furthermore, Darwin supports a novel PIM instruction architecture that concatenates instructions for multiple thread executions on bank group processing entities, addressing the command bottleneck by enabling separate control of up to 512 different in-memory processing units simultaneously. We build a cycle-accurate simulation framework to evaluate Darwin with various DRAM configurations, optimization schemes and workloads. Darwin achieves up to 14.7x speedup over the non-optimized version. Finally, the proposed Darwin architecture achieves 4.0x-43.9x higher throughput and reduces energy consumption by 85.7% than the baseline CPU system (Intel Xeon Gold 6226 + 4 channels of DDR4-2933). Compared to the state-of-the-art PIM, Darwin achieves up to 7.5x and 7.1x in the basic query operators and TPC-H queries, respectively. Darwin is based on the latest GDDR6 and requires only 5.6% area overhead, suggesting a promising PIM solution for the future main memory system.
format Preprint
id arxiv_https___arxiv_org_abs_2305_13970
institution arXiv
publishDate 2023
record_format arxiv
spellingShingle Darwin: A DRAM-based Multi-level Processing-in-Memory Architecture for Data Analytics
Kim, Donghyuk
Kim, Jae-Young
Han, Wontak
Won, Jongsoon
Choi, Haerang
Kwon, Yongkee
Kim, Joo-Young
Systems and Control
Processing-in-memory (PIM) architecture is an inherent match for data analytics application, but we observe major challenges to address when accelerating it using PIM. In this paper, we propose Darwin, a practical LRDIMM-based multi-level PIM architecture for data analytics, which fully exploits the internal bandwidth of DRAM using the bank-, bank group-, chip-, and rank-level parallelisms. Considering the properties of data analytics operators and DRAM's area constraints, Darwin maximizes the internal data bandwidth by placing the PIM processing units, buffers, and control circuits across the hierarchy of DRAM. More specifically, it introduces the bank processing unit for each bank in which a single instruction multiple data (SIMD) unit handles regular data analytics operators and bank group processing unit for each bank group to handle workload imbalance in the condition-oriented data analytics operators. Furthermore, Darwin supports a novel PIM instruction architecture that concatenates instructions for multiple thread executions on bank group processing entities, addressing the command bottleneck by enabling separate control of up to 512 different in-memory processing units simultaneously. We build a cycle-accurate simulation framework to evaluate Darwin with various DRAM configurations, optimization schemes and workloads. Darwin achieves up to 14.7x speedup over the non-optimized version. Finally, the proposed Darwin architecture achieves 4.0x-43.9x higher throughput and reduces energy consumption by 85.7% than the baseline CPU system (Intel Xeon Gold 6226 + 4 channels of DDR4-2933). Compared to the state-of-the-art PIM, Darwin achieves up to 7.5x and 7.1x in the basic query operators and TPC-H queries, respectively. Darwin is based on the latest GDDR6 and requires only 5.6% area overhead, suggesting a promising PIM solution for the future main memory system.
title Darwin: A DRAM-based Multi-level Processing-in-Memory Architecture for Data Analytics
topic Systems and Control
url https://arxiv.org/abs/2305.13970