Proceedings of the 19th International Workshop on Data Management on New Hardware最新文献

The Difficult Balance Between Modern Hardware and Conventional CPUs 现代硬件和传统cpu之间的艰难平衡

Proceedings of the 19th International Workshop on Data Management on New Hardware Pub Date : 2023-06-18 DOI: 10.1145/3592980.3595314

Fabio Maschi, G. Alonso

{"title":"The Difficult Balance Between Modern Hardware and Conventional CPUs","authors":"Fabio Maschi, G. Alonso","doi":"10.1145/3592980.3595314","DOIUrl":"https://doi.org/10.1145/3592980.3595314","url":null,"abstract":"Research has demonstrated the potential of accelerators in a wide range of use cases. However, there is a growing imbalance between modern hardware and the CPUs that submit the workload. Recent studies of GPUs on real systems have shown that many servers are often needed per accelerator to generate a high enough load so the computing power is leveraged. This fact is often ignored in research, although it often determines the actual feasibility and overall efficiency of a deployment. In this paper, we conduct a detailed study of the possible configurations and overall cost efficiency of deploying an FPGA-based accelerator on a commercial search engine. First, we show that there are many possible configurations balancing the upstream system and the way the accelerator is configured. Of these configurations, not all of them are suitable in practice, even if they provide some of the highest throughput. Second, we analyse the cost of a deployment capable of sustaining the required workload of the commercial search engine. We examine deployments both on-premises and in the cloud with and without FPGAs and with different board models. The results show that, while FPGAs have the potential to significantly improve overall performance, the performance imbalance between their host CPUs and the FPGAs can make the deployments economically unattractive. These findings are intended to inform the development and deployment of accelerators by showing what is needed on the CPU side to make them effective and also to provide important insights into their end-to-end integration within existing systems.","PeriodicalId":400127,"journal":{"name":"Proceedings of the 19th International Workshop on Data Management on New Hardware","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127675144","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Microarchitectural Analysis of Graph BI Queries on RDBMS RDBMS上图BI查询的微架构分析

Proceedings of the 19th International Workshop on Data Management on New Hardware Pub Date : 2023-06-18 DOI: 10.1145/3592980.3595321

Rathijit Sen, Yuanyuan Tian

引用次数: 0

KeRRaS: Sort-Based Database Query Processing on Wide Tables Using FPGAs 使用fpga的基于排序的宽表数据库查询处理

Proceedings of the 19th International Workshop on Data Management on New Hardware Pub Date : 2023-06-18 DOI: 10.1145/3592980.3595300

Mehdi Moghaddamfar, Christian Färber, Wolfgang Lehner, Akash Kumar

引用次数: 0

Processing-in-Memory for Databases: Query Processing and Data Transfer 数据库的内存处理:查询处理和数据传输

Proceedings of the 19th International Workshop on Data Management on New Hardware Pub Date : 2023-06-18 DOI: 10.1145/3592980.3595323

Alexander Baumstark, M. Jibril, K. Sattler

引用次数: 0

Elastic Use of Far Memory for In-Memory Database Management Systems 内存数据库管理系统中远内存的弹性使用

Proceedings of the 19th International Workshop on Data Management on New Hardware Pub Date : 2023-06-18 DOI: 10.1145/3592980.3595311

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

引用次数: 2

Delilah: eBPF-offload on Computational Storage Delilah:计算存储上的ebpf卸载

Proceedings of the 19th International Workshop on Data Management on New Hardware Pub Date : 2023-06-18 DOI: 10.1145/3592980.3595319

Niclas Hedam, Morten Tychsen Clausen, Philippe Bonnet, Sangjin Lee, Ken Friis Larsen

引用次数: 0

Accelerating Main-Memory Table Scans with Partial Virtual Views 用部分虚拟视图加速主存表扫描

Proceedings of the 19th International Workshop on Data Management on New Hardware Pub Date : 2023-06-18 DOI: 10.1145/3592980.3595315

F. Schuhknecht, Justus Henneberg

{"title":"Accelerating Main-Memory Table Scans with Partial Virtual Views","authors":"F. Schuhknecht, Justus Henneberg","doi":"10.1145/3592980.3595315","DOIUrl":"https://doi.org/10.1145/3592980.3595315","url":null,"abstract":"In main-memory column stores, column scans are one of the base operations performed when answering analytical queries. Typically, one or multiple columns must be filtered with respect to the given query predicate, which, by default, involves inspecting all data of the involved columns. To reduce the amount of data to scan, there exist essentially two strategies: (1) Create a coarse-granular index on the column, then use it for early pruning during each scan. While creating such an index is relatively lightweight, unfortunately, accessing the relevant portions of the column through the index causes unpleasant overhead during scanning. (2) Create materialized views that contain semantic portions of the column and filter on these. While this enables fast scans, unfortunately, it requires physical copying and causes significant space overhead. To break this trade-off, in the following, we propose a view-based strategy that avoids any physical copying of column data while providing optimal scan performance. We achieve this by utilizing tools of the virtual memory subsystem provided by the OS: On the lowest level, we materialize all columns within physical main memory. On top of that, we allow the creation of arbitrarily many partial views in virtual memory that map to subsets of the physical columns having certain properties of interest. Creation, maintenance, and usage of these partial virtual views happens fully adaptively as a side-product of scan-based query processing.","PeriodicalId":400127,"journal":{"name":"Proceedings of the 19th International Workshop on Data Management on New Hardware","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115828969","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Zero-sided RDMA: Network-driven Data Shuffling 零侧RDMA:网络驱动的数据变换

Proceedings of the 19th International Workshop on Data Management on New Hardware Pub Date : 2023-06-18 DOI: 10.1145/3592980.3595302

Matthias Jasny, Lasse Thostrup, Carsten Binnig

引用次数: 1

Why Your Experimental Results Might Be Wrong 为什么你的实验结果可能是错误的

Proceedings of the 19th International Workshop on Data Management on New Hardware Pub Date : 2023-06-18 DOI: 10.1145/3592980.3595317

F. Schuhknecht, Justus Henneberg

{"title":"Why Your Experimental Results Might Be Wrong","authors":"F. Schuhknecht, Justus Henneberg","doi":"10.1145/3592980.3595317","DOIUrl":"https://doi.org/10.1145/3592980.3595317","url":null,"abstract":"Research projects in the database community are often evaluated based on experimental results. A typical evaluation setup looks as follows: Multiple methods to compare with each other are embedded in a single shared benchmarking codebase. In this codebase, all methods execute an identical workload to collect the individual execution times. This seems reasonable: Since the only difference between individual test runs are the methods themselves, any observed time difference can be attributed to these methods. Also, such a benchmarking codebase can be used for gradual optimization: If one method runs slowly, its code can be optimized and re-evaluated. If its performance improves, this improvement can be attributed to the particular optimization. Unfortunately, we had to learn the hard way that it is not that simple. The reason for this lies in a component that sits right between our benchmarking codebase and the produced experimental results — the compiler. As we will see in the following case study, this black-box component has the power to completely ruin any meaningful comparison between methods, even if we setup our experiments as equal and fair as possible.","PeriodicalId":400127,"journal":{"name":"Proceedings of the 19th International Workshop on Data Management on New Hardware","volume":"111 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2023-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125547491","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Towards Data-Based Cache Optimization of B+-Trees 基于数据的B+树缓存优化研究

Proceedings of the 19th International Workshop on Data Management on New Hardware Pub Date : 2023-06-18 DOI: 10.1145/3592980.3595316

Roland Kühn, Daniel Biebert, Christian Hakert, Jian-Jia Chen, J. Teubner

引用次数: 0