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PIMPMe Publications

To Appear

Journal SMASH Memento SMAUG ParPerOS PIMPMe NEON HetCIM
Charting the Benchmarking Landscape – A Survey of Benchmarks for Modern Memory Architectures

David Broneske, Christian Eichler, Hamid Farzaneh, Birte Friesel, Alexander Halbuer, Benedict Herzog, Muhammad Attahir Jibril, Sajad Karim, Sven Köhler, Manuel VögeleBenchCouncil Transactions on Benchmarks, Standards and Evaluations2026to appear.
[BibTex]

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BibTex Entry

@article{Broneske:2026,
author = {Broneske, David and Eichler, Christian and Farzaneh, Hamid and Friesel, Birte and Halbuer, Alexander and Herzog, Benedict and Jibril, Muhammad Attahir and Karim, Sajad and Köhler, Sven and Vögele, Manuel},
date = {2026},
entrysubtype = {Journal},
issn = {2772-4859},
journaltitle = {BenchCouncil Transactions on Benchmarks, Standards and Evaluations},
note = {to appear},
pubstate = {forthcoming},
title = {Charting the Benchmarking Landscape – A Survey of Benchmarks for Modern Memory Architectures},
}

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2026

Workshop PIMPMe C
Characterizing Multi-Host CXL 2.0: Latency, Bandwidth, and Synchronization Costs

Laurin Martins, Alexander Baumstark, Andreas Becher, Kai-Uwe SattlerProceedings of the 22nd International Workshop on Data Management on New HardwareAssociation for Computing Machinery2026.
10.1145/3789237.3809131 [BibTex]

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BibTex Entry

@inproceedings{Martins:2026,
abstract = {Compute Express Link (CXL) is an interconnect that extends the traditional memory hierarchy beyond a single machine. It enables disaggregated memory architecture, where memory can be pooled and shared across multiple hosts effectively. Previous systems and approaches were limited to CXL 1.0 which is restricted to intra-host scaling of memory resources with no out of the box support for sharing across multiple hosts. We evaluate a commercially available CXL 2.0 memory switching platform for multi-host shared memory. While prior work has largely focused on emulation, FPGA-based or non-available prototypes, we investigate the real-world performance and system implications of deploying CXL as shared memory across multiple hosts on a commercially available setup. Our evaluation reveals fundamental trade-offs: Access to shared CXL memory exhibits up to 4x higher latency and significantly lower bandwidth compared to local DRAM, but enables flexible memory pooling with fair sharing across hosts. More importantly, we show that the lack of hardware cache coherence in CXL 2.0 introduces non-trivial challenges, requiring carefully chosen instruction patterns.},
author = {Martins, Laurin and Baumstark, Alexander and Becher, Andreas and Sattler, Kai-Uwe},
booktitle = {Proceedings of the 22nd International Workshop on Data Management on New Hardware},
date = {2026-07-10},
doi = {10.1145/3789237.3809131},
eid = {6},
entrysubtype = {Workshop},
eventdate = {2026-05-31},
isbn = {9798400724558},
keywords = {CXL, Shared Memory},
location = {New York, NY, USA},
publisher = {Association for Computing Machinery},
series = {DaMoN '26},
title = {Characterizing Multi-Host CXL 2.0: Latency, Bandwidth, and Synchronization Costs},
venue = {Bengaluru, India},
}

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Conference PIMPMe A
Does A Fish Need a Bicycle? The Case for On-Chip NPUs in DBMS

Alexander Baumstark, Kai-Uwe SattlerProceedings of the 16th Annual Conference on Innovative Data Systems Research (CIDR '26)2026.
PDF [BibTex]

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BibTex Entry

@inproceedings{Baumstark:2026,
author = {Baumstark, Alexander and Sattler, Kai-Uwe},
booktitle = {Proceedings of the 16th Annual Conference on Innovative Data Systems Research (CIDR '26)},
date = {2026},
entrysubtype = {Conference},
eventdate = {2026-01-18},
series = {CIDR '26},
title = {Does A Fish Need a Bicycle? The Case for On-Chip NPUs in DBMS},
url = {https://vldb.org/cidrdb/papers/2026/p24-baumstark.pdf},
venue = {Chaminade, CA, USA},
}

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Workshop PIMPMe C
Let the Hardware do it: Fast Graph Query processing through FPGA-accelerated Late Materialization and Decompression

Lei Chen, Alexander Baumstark, Andreas Becher, Daniel Ziener, Kai-Uwe SattlerProceedings of the 22nd International Workshop on Data Management on New HardwareAssociation for Computing Machinery2026.
10.1145/3789237.3809133 [BibTex]

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BibTex Entry

@inproceedings{Chen:2026,
abstract = {Property access is a dominant performance bottleneck in modern graph DBMSs, especially for analytical queries involving multi-hop traversals and selective projection. We observe that premature materialization and repeated property decoding cause excessive data movement and irregular memory access, limiting throughput and scalability. While Late Materialization (LM) has proven effective in column stores, its adoption in graph engines is hindered by irregular access patterns and fine-grained decoding costs that are poorly handled by general-purpose CPUs, where cache misses, branch divergence, and synchronization overhead limit effective memory bandwidth utilization. We, therefore, propose an FPGA-accelerated co-processing approach that tightly integrates compression-aware LM into the graph query pipeline. By exploiting the deterministic and streaming-friendly nature of FSST decoding, we offload string decompression to a deeply pipelined hardware dataflow engine, enabling high throughput. Our experimental results demonstrate that the hardware-accelerated approach achieves speedups of up to 24.6 \\texttimes\{\} over a CPU-only baseline.},
author = {Chen, Lei and Baumstark, Alexander and Becher, Andreas and Ziener, Daniel and Sattler, Kai-Uwe},
booktitle = {Proceedings of the 22nd International Workshop on Data Management on New Hardware},
date = {2026-07-10},
doi = {10.1145/3789237.3809133},
eid = {14},
entrysubtype = {Workshop},
eventdate = {2026-05-31},
isbn = {9798400724558},
location = {New York, NY, USA},
publisher = {Association for Computing Machinery},
series = {DaMoN '26},
title = {Let the Hardware do it: Fast Graph Query processing through FPGA-accelerated Late Materialization and Decompression},
venue = {Bengaluru, India},
}

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2025

Journal PIMPMe
Temporal graph processing in modern memory hierarchies

Alexander Baumstark, Muhammad Attahir Jibril, Kai-Uwe SattlerInformation Systems1272025.
10.1016/j.is.2024.102462 [BibTex]

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BibTex Entry

@article{Baumstark:2025,
abstract = {Updates in graph {DBMS} lead to structural changes in the graph over time with different intermediate states. Capturing these changes and their time is one of the main purposes of temporal {DBMS}. Most {DBMSs} built their temporal features based on their non-temporal processing and storage without considering the memory hierarchy of the underlying system. This leads to slower temporal processing and poor storage utilization. In this paper, we propose a storage and processing strategy for (bi-) temporal graphs using temporal materialized views ({TMV}) while exploiting the memory hierarchy of a modern system. Further, we show a solution to the query containment problem for certain types of temporal graph queries. Finally, we evaluate the overhead and performance of the presented approach. The results show that using {TMV} reduces the runtime of temporal graph queries while using less memory.},
author = {Baumstark, Alexander and Jibril, Muhammad Attahir and Sattler, Kai-Uwe},
date = {2025-01-01},
doi = {10.1016/j.is.2024.102462},
eid = {102462},
entrysubtype = {Journal},
issn = {0306-4379},
journaltitle = {Information Systems},
keywords = {Temporal graph, Materialized view, Memory hierarchy},
shortjournal = {Information Systems},
title = {Temporal graph processing in modern memory hierarchies},
url = {https://www.sciencedirect.com/science/article/pii/S0306437924001200},
volume = {127},
}

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2024

Conference PIMPMe A*
Accelerating Aggregation Using a Real Processing-in-Memory System

Muhammad Attahir Jibril, Hani Al-Sayeh, Kai-Uwe SattlerProceedings of the 2024 IEEE 40th International Conference on Data Engineering (ICDE)2024.
10.1109/ICDE60146.2024.00300 [BibTex]

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BibTex Entry

@inproceedings{Jibril:2024,
author = {Muhammad Attahir Jibril and Hani Al-Sayeh and Kai-Uwe Sattler},
booktitle = {Proceedings of the 2024 IEEE 40th International Conference on Data Engineering (ICDE)},
date = {2024-01-01},
doi = {10.1109/ICDE60146.2024.00300},
entrysubtype = {Conference},
keywords = {Energy consumption;Memory management;Bandwidth;Data engineering;Hardware;Optimization;PIM;aggregation;UPMEM;performance optimization;modern hardware},
location = {Utrecht, Netherlands},
pages = {3920-3932},
pubstate = {published},
title = {Accelerating Aggregation Using a Real Processing-in-Memory System},
year = {2024},
}

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Conference PIMPMe A*
Agile-Ant: Self-Managing Distributed Cache Management for Cost Optimization of Big Data Applications

Hani Al-Sayeh, Muhammad Attahir Jibril, Kai-Uwe SattlerProceedings of the VLDB Endowment17.112024.
10.14778/3681954.3681990 [BibTex]

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BibTex Entry

@article{AlSayeh:2024,
abstract = {Distributed in-memory processing frameworks accelerate application runs by caching important datasets in memory. Allocating a suitable cluster configuration for caching these datasets plays a crucial role in achieving minimal cost. We present Agile-ant, a self-managing framework that identifies important datasets and scales out the cluster memory to cache them on the fly without any human interaction, without any prior knowledge of the application, the characteristics of the input data, the specification of the computing resources and their utilization by multiple-tenants. We evaluate Agile-ant on various real-world applications. Compared with our baseline, Agile-ant reduces execution cost by 78.3\% on average and provides better performance than the related work.},
author = {Al-Sayeh, Hani and Jibril, Muhammad Attahir and Sattler, Kai-Uwe},
date = {2024-07-01},
doi = {10.14778/3681954.3681990},
entrysubtype = {Conference},
issn = {2150-8097},
journaltitle = {Proceedings of the {VLDB} Endowment},
number = {11},
pages = {3151–3164},
shorttitle = {Agile-Ant},
title = {Agile-Ant: Self-Managing Distributed Cache Management for Cost Optimization of Big Data Applications},
url = {https://doi.org/10.14778/3681954.3681990},
volume = {17},
}

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Workshop PIMPMe C
So Far and yet so Near - Accelerating Distributed Joins with CXL

Alexander Baumstark, Marcus Paradies, Kai-Uwe Sattler, Steffen Kläbe, Stephan BaumannProceedings of the 20th International Workshop on Data Management on New HardwareAssociation for Computing Machinery2024.
10.1145/3662010.3663449 [BibTex]

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BibTex Entry

@inproceedings{Baumstark:2024,
abstract = {Distributed partitioned joins are one of the most expensive operators in distributed {DBMSs} where a major part of the execution is attributed to network transfer costs. Although high-speed network technologies, such as {RDMA}, can lower this cost, they still come with significantly higher latency than local {DRAM} access. The emerging {CXL} interconnect protocol promises to provide direct and cache-coherent access to remote memory while offering byte-addressable memory access without {CPU} intervention. For short-distance communication in distributed {DBMSs}, {CXL} represents an interesting alternative for low-latency requirements. In this work, we explore how {CXL} can be leveraged for engine-internal communication and data exchange. We discuss and apply communication strategies to distributed joins. We emulate various {CXL} characteristics based on optimistic and pessimistic assumptions on the real performance of upcoming {CXL} devices and evaluate their impact on the execution of distributed joins. Our results show that {CXL} has the potential to improve distributed join performance.},
author = {Baumstark, Alexander and Paradies, Marcus and Sattler, Kai-Uwe and Kläbe, Steffen and Baumann, Stephan},
booktitle = {Proceedings of the 20th International Workshop on Data Management on New Hardware},
date = {2024-06-09},
doi = {10.1145/3662010.3663449},
eid = {7},
entrysubtype = {Workshop},
isbn = {9798400706677},
location = {New York, {NY}, {USA}},
publisher = {Association for Computing Machinery},
series = {{DaMoN} '24},
title = {So Far and yet so Near - Accelerating Distributed Joins with {CXL}},
url = {https://dl.acm.org/doi/10.1145/3662010.3663449},
}

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2023

Journal PIMPMe
Accelerating Large Table Scan Using Processing-In-Memory Technology

Alexander Baumstark, Muhammad Attahir Jibril, Kai-Uwe SattlerDatenbank-Spektrum23.32023.
10.1007/s13222-023-00456-z [BibTex]

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BibTex Entry

@article{Baumstark:2023,
author = {Alexander Baumstark and Muhammad Attahir Jibril and Kai-Uwe Sattler},
date = {2023-10-10},
doi = {10.1007/s13222-023-00456-z},
entrysubtype = {Journal},
issn = {1610-1995},
journaltitle = {Datenbank-Spektrum},
number = {3},
pages = {199–209},
pubstate = {published},
title = {Accelerating Large Table Scan Using Processing-In-Memory Technology},
volume = {23},
year = {2023},
}

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Conference PIMPMe
Accelerating Large Table Scan using Processing-In-Memory Technology

Alexander Baumstark, Muhammad Attahir Jibril, Kai-Uwe SattlerBTW 2023Gesellschaft für Informatik e.V.2023.
10.18420/BTW2023-51 [BibTex]

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BibTex Entry

@incollection{Baumstark:2023c,
author = {Alexander Baumstark and Muhammad Attahir Jibril and Kai-Uwe Sattler},
booktitle = {BTW 2023},
comment = {CORE2023 National:Germany},
date = {2023},
doi = {10.18420/BTW2023-51},
entrysubtype = {Conference},
isbn = {978-3-88579-725-8},
location = {Bonn},
pages = {797–814},
publisher = {Gesellschaft für Informatik e.V.},
pubstate = {published},
title = {Accelerating Large Table Scan using Processing-In-Memory Technology},
}

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Conference PIMPMe
Adaptive Query Compilation with Processing-in-Memory

Alexander Baumstark, Muhammad Attahir Jibril, Kai-Uwe Sattler2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW)2023.
10.1109/ICDEW58674.2023.00035 [BibTex]

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BibTex Entry

@inproceedings{Baumstark:2023b,
author = {Alexander Baumstark and Muhammad Attahir Jibril and Kai-Uwe Sattler},
booktitle = {2023 IEEE 39th International Conference on Data Engineering Workshops (ICDEW)},
date = {2023-04},
doi = {10.1109/ICDEW58674.2023.00035},
entrysubtype = {Conference},
issn = {2473-3490},
pages = {191–197},
pubstate = {published},
title = {Adaptive Query Compilation with Processing-in-Memory},
}

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Journal PIMPMe
Adaptive update handling for graph HTAP

Muhammad Attahir Jibril, Alexander Baumstark, Kai-Uwe SattlerDistributed and Parallel Databases41.32023.
10.1007/s10619-023-07428-y [BibTex]

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BibTex Entry

@article{Jibril:2023,
abstract = {Hybrid transactional/analytical processing ({HTAP}) workloads on graph data can significantly benefit from {GPU} accelerators. However, to exploit the full potential of {GPU} processing, dedicated graph representations are necessary, which mostly make in-place updates difficult. In this paper, we discuss an adaptive update handling approach in a graph database system for {HTAP} workloads. We discuss and evaluate strategies for propagating transactional updates from an update-friendly table storage to a {GPU}-optimized sparse matrix format for analytics.},
author = {Jibril, Muhammad Attahir and Baumstark, Alexander and Sattler, Kai-Uwe},
date = {2023-09-01},
doi = {10.1007/s10619-023-07428-y},
entrysubtype = {Journal},
issn = {1573-7578},
journaltitle = {Distributed and Parallel Databases},
keywords = {Graph data management, {HTAP}, {GPU} accelerator, Delta store, Cost model, {CSR}},
langid = {english},
number = {3},
pages = {331–357},
shortjournal = {Distrib Parallel Databases},
title = {Adaptive update handling for graph {HTAP}},
url = {https://doi.org/10.1007/s10619-023-07428-y},
volume = {41},
}

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Conference PIMPMe A
Fast and Efficient Update Handling for Graph H\textsuperscript{2}TAP

Muhammad Attahir Jibril, Hani Al-Sayeh, Alexander Baumstark, Kai-Uwe SattlerProceedings of the 26th International Conference on Extending Database TechnologyOpenproceedings.org2023.
10.48786/edbt.2023.60 [BibTex]

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BibTex Entry

@inproceedings{Jibril:2023a,
accessdate = {2025-05-06},
author = {Jibril, Muhammad Attahir and Al-Sayeh, Hani and Baumstark, Alexander and Sattler, Kai-Uwe},
booktitle = {Proceedings of the 26th International Conference on Extending Database Technology},
date = {2023},
doi = {10.48786/edbt.2023.60},
entrysubtype = {Conference},
isbn = {978-3-89318-092-9},
pages = {723–736},
publisher = {Openproceedings.org},
series = {{EDBT} 2023},
title = {Fast and Efficient Update Handling for Graph H\\textsuperscript\{2\}TAP},
url = {https://openproceedings.org/2023/conf/edbt/3-paper-113.pdf},
volume = {3},
}

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Workshop PIMPMe C
Processing-in-Memory for Databases: Query Processing and Data Transfer

Alexander Baumstark, Muhammad Attahir Jibril, Kai-Uwe SattlerProceedings of the 19th International Workshop on Data Management on New HardwareAssociation for Computing Machinery2023.
10.1145/3592980.3595323 [BibTex]

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BibTex Entry

@inproceedings{Baumstark:2023a,
author = {Alexander Baumstark and Muhammad Attahir Jibril and Kai-Uwe Sattler},
booktitle = {Proceedings of the 19th International Workshop on Data Management on New Hardware},
date = {2023-06-18},
doi = {10.1145/3592980.3595323},
entrysubtype = {Workshop},
isbn = {9798400701917},
location = {New York, NY, USA},
pages = {107–111},
publisher = {Association for Computing Machinery},
pubstate = {published},
series = {DaMoN '23},
title = {Processing-in-Memory for Databases: Query Processing and Data Transfer},
year = {2023},
}

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Conference PIMPMe C
Temporal Graph Processing in Modern Memory Hierarchies

Alexander Baumstark, Muhammad Attahir Jibril, Kai-Uwe SattlerAdvances in Databases and Information SystemsSpringer Nature Switzerland2023.
10.1007/978-3-031-42914-9_8 [BibTex]

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BibTex Entry

@inproceedings{Baumstark:2023d,
abstract = {Updates in graph {DBMS} lead to structural changes in the graph over time with different intermediate states. These intermediate states in a {DBMS} and the time when the actions to the actual data take place can be processed using temporal {DBMSs}. Most {DBMSs} built their temporal features based on their non-temporal processing and storage without considering the memory hierarchy of the underlying system. This leads to slower temporal processing and poor storage utilization. In this paper, we propose a storage and processing strategy for (bi-) temporal graphs using temporal materialized views ({TMV}) while exploiting the memory hierarchy of a modern system. Further, we show a solution to the query containment problem for certain types of temporal graph queries. Finally, we evaluate the overhead and performance of the presented approach. The results show that using {TMV} reduces the runtime of temporal graph queries while using less memory.},
author = {Baumstark, Alexander and Jibril, Muhammad Attahir and Sattler, Kai-Uwe},
booktitle = {Advances in Databases and Information Systems},
date = {2023-08-28},
doi = {10.1007/978-3-031-42914-9_8},
editor = {Abelló, Alberto and Vassiliadis, Panos and Romero, Oscar and Wrembel, Robert},
entrysubtype = {Conference},
isbn = {9783031429149},
keywords = {Bitemporal Graph Processing, Materialized View, Memory Hierarchy},
langid = {english},
location = {Cham},
pages = {103–116},
publisher = {Springer Nature Switzerland},
title = {Temporal Graph Processing in Modern Memory Hierarchies},
}

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