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DaMoN '24: Proceedings of the 20th International Workshop on Data Management on New Hardware
ACM2024 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
SIGMOD/PODS '24: International Conference on Management of Data Santiago AA Chile 10 June 2024
ISBN:
979-8-4007-0667-7
Published:
09 June 2024
Sponsors:
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Abstract

The aim of this one-day workshop is to bring together researchers who are interested in optimizing database performance on modern computing infrastructure by designing new data management techniques and tools. The continued evolution of computing hardware and infrastructure imposes new challenges and bottlenecks to program performance. As a result, traditional database architectures that focus solely on I/O optimization increasingly fail to utilize hardware resources efficiently. Multi-core CPUs, GPUs, FPGAs, new memory and storage technologies (such as flash and non-volatile memory), and low-power hardware impose great challenges to optimizing database performance. Consequently, exploiting the characteristics of modern hardware has become an important topic of database systems research.

The goal is to make database systems adapt automatically to the sophisticated hardware characteristics, thus maximizing performance transparently to applications. To achieve this goal, the data management community needs interdisciplinary collaboration with computer architecture, compiler, operating systems, and storage researchers. This involves rethinking traditional data structures, query processing algorithms, and database software architectures to adapt to the advances in the underlying hardware infrastructure. For the DaMoN Workshop, we seek submissions bridging the area of database systems to computer architecture, compilers, and operating systems.

Acceptance Rates: DaMoN'24: Submitted=25, Accepted=14, Rate=56%

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SESSION: Full Papers
research-article
Open Access
SFVInt: Simple, Fast and Generic Variable-Length Integer Decoding using Bit Manipulation Instructions
Article No.: 1, Pages 1–9https://doi.org/10.1145/3662010.3663439

The ubiquity of variable-length integers in data storage and communication necessitates efficient decoding techniques. In this paper, we present SFVInt, a simple and fast approach to decode the prevalent Little Endian Base-128 (LEB128) varints. Our ...

research-article
Open Access
The Price of Privacy: A Performance Study of Confidential Virtual Machines for Database Systems
Article No.: 2, Pages 1–8https://doi.org/10.1145/3662010.3663440

Confidential virtual machines (CVM) use trusted hardware to encrypt data being processed in memory to prevent unauthorized access. Applications can be migrated to CVM without changes, i.e., lift and shift, to handle sensitive workloads securely in public ...

research-article
Open Access
Heterogeneous Intra-Pipeline Device-Parallel Aggregations
Article No.: 3, Pages 1–10https://doi.org/10.1145/3662010.3663441

The rising hardware heterogeneity in modern systems emphasizes new dimensions of optimizing task execution for data processing frameworks. Specialized hardware is often expected to be the exclusive executor of some particular workload because it was ...

research-article
Open Access
Simple, Efficient, and Robust Hash Tables for Join Processing
Article No.: 4, Pages 1–9https://doi.org/10.1145/3662010.3663442

Hash joins play a critical role in relational data processing and their performance is crucial for the overall performance of a database system. Due to the hard to predict nature of intermediate results, an ideal hash join implementation has to be both ...

research-article
How Does Software Prefetching Work on GPU Query Processing?
Article No.: 5, Pages 1–9https://doi.org/10.1145/3662010.3663445

Improving the performance of GPU query processing is a well-studied problem in database community. However, its performance is still unsatisfactory due to the low utilization of GPU memory bandwidth. In the literature, employing software prefetching ...

research-article
Efficient Data Access Paths for Mixed Vector-Relational Search
Article No.: 6, Pages 1–9https://doi.org/10.1145/3662010.3663448

The rapid growth of machine learning capabilities and the adoption of data processing methods using vector embeddings sparked a great interest in creating systems for vector data management. While the predominant approach of vector data management is to ...

research-article
Open Access
So Far and yet so Near - Accelerating Distributed Joins with CXL
Article No.: 7, Pages 1–9https://doi.org/10.1145/3662010.3663449

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 ...

research-article
Open Access
Accelerating GPU Data Processing using FastLanes Compression
Article No.: 8, Pages 1–11https://doi.org/10.1145/3662010.3663450

We show that compression can be a win-win for GPU data processing: it not only allows to store more data in GPU global memory, but can also accelerate data processing. We show that the complete redesign of compressed columnar storage in FastLanes, with ...

research-article
Open Access
How to Be Fast and Not Furious: Looking Under the Hood of CPU Cache Prefetching
Article No.: 9, Pages 1–10https://doi.org/10.1145/3662010.3663451

Software-based prefetching is a powerful method for tolerating access penalties that are encountered by data processing systems: memory latency. Although the idea appears straightforward---simply informing the CPU about upcoming data accesses---the ...

research-article
Open Access
NULLS!: Revisiting Null Representation in Modern Columnar Formats
Article No.: 10, Pages 1–10https://doi.org/10.1145/3662010.3663452

Nulls are common in real-world data sets, yet recent research on columnar formats and encodings rarely address Null representations. Popular file formats like Parquet and ORC follow the same design as C-Store from nearly 20 years ago that only stores non-...

SESSION: Short Papers
short-paper
Open Access
In situ neighborhood sampling for large-scale GNN training
Article No.: 11, Pages 1–5https://doi.org/10.1145/3662010.3663443

Graph Neural Network (GNN) training algorithms commonly perform neighborhood sampling to construct fixed-size mini-batches for weight aggregation on GPUs. State-of-the-art disk-based GNN frameworks compute sampling on the CPU, transferring edge ...

short-paper
Open Access
Performance or Efficiency? A Tale of Two Cores for DB Workloads
Article No.: 12, Pages 1–5https://doi.org/10.1145/3662010.3663444

We study the performance, power, and thermal profiles for database workloads on hybrid P-core and E-core CPUs. We find that E-cores run cooler than P-cores, use less power, and are more energy-efficient, but need to be provisioned more than 3x the number ...

short-paper
Seamless: Transparent Storage Access Through Smart Switches
Article No.: 13, Pages 1–5https://doi.org/10.1145/3662010.3663446

This paper presents Seamless, a switch-based accelerator for disaggregated SSD-based systems. Seamless comprises two fundamental components: (1) a hardware-accelerated, unified remote storage protocol that ensures efficient data access to Flash and ...

short-paper
Open Access
DuckDB-SGX2: The Good, The Bad and The Ugly within Confidential Analytical Query Processing
Article No.: 14, Pages 1–5https://doi.org/10.1145/3662010.3663447

We provide an evaluation of an analytical workload in a confidential computing environment, combining DuckDB with two technologies: modular columnar encryption in Parquet files (data at rest) and the newest version of the Intel SGX Trusted Execution ...

Contributors
  • Technical University of Darmstadt
  • Massachusetts Institute of Technology

Recommendations

Acceptance Rates

DaMoN '24 Paper Acceptance Rate 14 of 25 submissions, 56%;
Overall Acceptance Rate 94 of 127 submissions, 74%
YearSubmittedAcceptedRate
DaMoN '24251456%
DaMoN '23231774%
DaMoN '22181267%
DAMON '21171588%
DaMoN '20221882%
DaMoN'15161275%
DaMoN '0666100%
Overall1279474%