Elastic Use of Far Memory for In-Memory Database Management Systems

Lee, Donghun; Willhalm, Thomas; Ahn, Minseon; Mutalik Desai, Suprasad; Booss, Daniel; Singh, Navneet; Ritter, Daniel; Kim, Jungmin; Rebholz, Oliver

doi:10.1145/3592980.3595311

Cited by 4 publications

(3 citation statements)

References 18 publications

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“…Gao, Yang, Zhao, & Zhao (2021), through their research, address the current status and future prospects of IMC, emphasizing the ongoing need for innovation in this area. These references offer a starting point for those interested in delving deeper into the complexities of IMC and its potential impact on future computing paradigms (Ribeiro, Méhaut, & Carissimi, 2010;Das & Singh, 2014;Lee et al, 2023;Kondo, Fujita, & Nakamura, 2002). Additionally, memory fragmentation can degrade performance over time as the system struggles to allocate large contiguous memory blocks for new data.…”

Section: Limitations and Challenges Of In-memory Computingmentioning

confidence: 99%

Assessing the Dynamics of Data Processing

2024

CEIS

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This study embarks on a comprehensive analysis to evaluate the efficiency and scalability of in-memory computing (IMC) compared to traditional disk-based processing for big data analytics. Utilising the "New York City Taxi Trip Duration" dataset from Kaggle, we designed an experiment focusing on three critical analytical tasks: aggregation, sorting, and filtering. Our objective was to quantify the performance improvements offered by IMC, as facilitated by Apache Spark, against conventional SQL queries executed on a disk-based system. The findings reveal that IMC consistently outperforms disk-based processing in execution time, with significant reductions observed across all tasks. Specifically, the aggregation task highlighted the stark contrast in data retrieval speed, demonstrating IMC's superior efficiency with a completion time of 47.3 seconds, compared to 138.7 seconds for disk-based processing. Similar disparities were noted in sorting and filtering tasks, further accentuating IMC's performance advantage. Resource utilisation analysis, focusing on CPU and RAM consumption, indicated higher demands associated with IMC, underscoring the trade-off between enhanced speed and increased resource usage. The investigation provides a nuanced understanding of the practical implications of adopting IMC for big data analytics, especially considering the resource constraints of home computing environments. By juxtaposing theoretical advantages with empirical data, this paper contributes to the ongoing discourse on optimising data processing methodologies in the era of big data, offering insights into the balance between computational efficiency and resource management.

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Section: Limitations and Challenges Of In-memory Computingmentioning

confidence: 99%

Assessing the Dynamics of Data Processing

2024

CEIS

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“…We execute EMOGI on latency-adjustable CXL memory instead of on the host DRAM. We implement CXL memory based on Intel Agilex®7 FPGA as also used in other existing works [21,41]. Figure 7 shows the block diagram.…”

Section: Evaluation On CXL Memorymentioning

confidence: 99%

“…CXL analysis and evaluation: CXL is an emerging standard that is attracting attention not only from industry but also from research communities. Analysis and evaluation of CXL-enabled systems are being conducted ranging from memory pooling in general [12,23,41,44,46], to more specific applications such as machine learning [19] and in-memory databases [21]. CXL studies involving accelerators such as GPU and FPGA are appearing [3,19].…”

Section: Related Workmentioning

confidence: 99%

GPU Graph Processing on CXL-Based Microsecond-Latency External Memory

Sano,

Bando,

Hiwada

et al. 2023

Proceedings of the SC '23 Workshops of the International Conference on High Performance Computing, Network, Storage, and Analys

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In GPU graph analytics, the use of external memory such as the host DRAM and solid-state drives is a cost-effective approach to processing large graphs beyond the capacity of the GPU onboard memory. This paper studies the use of Compute Express Link (CXL) memory as alternative external memory for GPU graph processing in order to see if this emerging memory expansion technology enables graph processing that is as fast as using the host DRAM. Through analysis and evaluation using FPGA prototypes, we show that representative GPU graph traversal algorithms involving finegrained random access can tolerate an external memory latency of up to a few microseconds introduced by the CXL interface as well as by the underlying memory devices. This insight indicates that microsecond-latency flash memory may be used as CXL memory devices to realize even more cost-effective GPU graph processing while still achieving performance close to using the host DRAM. CCS CONCEPTS• Hardware → Analysis and design of emerging devices and systems; Memory and dense storage.

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