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DaMoN '23: Proceedings of the 19th International Workshop on Data Management on New Hardware
ACM2023 Proceeding
Publisher:
  • Association for Computing Machinery
  • New York
  • NY
  • United States
Conference:
SIGMOD/PODS '23: International Conference on Management of Data Seattle WA USA June 18 - 23, 2023
ISBN:
979-8-4007-0191-7
Published:
18 June 2023
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 mod[1]ern 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.

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SESSION: Full Papers
research-article
KeRRaS: Sort-Based Database Query Processing on Wide Tables Using FPGAs

Sorting is an important operation in database query processing. Complex pipeline-breaking operators (e.g., aggregation and equi-join) become single-pass algorithms on sorted tables. Therefore, sort-based query processing is a popular method for FPGA-...

research-article
Exploiting Access Pattern Characteristics for Join Reordering

With increasing main memory sizes, data processing has significantly shifted from secondary storage to main memory. However, choosing a good join order is still very important for efficient query execution in modern DBMS. This choice bases mainly on ...

research-article
Accelerating User-Defined Aggregate Functions (UDAF) with Block-wide Execution and JIT Compilation on GPUs

The GPU-accelerated DataFrame library cuDF has become increasingly popular for data analytics applications due to its superior performance against CPU-based DataFrame libraries such as Pandas. One of the frequently-used operations in dataframe ...

research-article
Open Access
Micro Partitioning: Friendly to the Hardware and the Developer

Modern hardware’s complexity has made studying hardware-conscious algorithms a relevant topic for many years. Partitioning algorithms, for instance, break data into bits that fit into fast CPU caches. Unfortunately, they are often challenging to design, ...

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

The separation and independent scalability of compute and memory is one of the crucial aspects for modern in-memory database systems (IMDBMSs) in the cloud. The new, cache-coherent memory interconnect Compute Express Link (CXL) promises elastic memory ...

research-article
pimDB: From Main-Memory DBMS to Processing-In-Memory DBMS-Engines on Intelligent Memories

The performance and scalability of modern data-intensive systems are limited by massive data movement of growing datasets across the whole memory hierarchy to the CPUs. Such traditional processor-centric DBMS architectures are bandwidth- and latency-...

research-article
The Difficult Balance Between Modern Hardware and Conventional CPUs

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 ...

research-article
Open Access
Towards Data-Based Cache Optimization of B+-Trees

The rise of in-memory databases and systems with considerably large memories and cache sizes requires the rethinking of the proper implementation of index structures like B+-trees in such systems. While disk block-sized nodes and binary search were ...

research-article
Open Access
Delilah: eBPF-offload on Computational Storage

The idea of pushing computation to storage devices has been explored for decades, without widespread adoption so far. The definition of Computational Programs namespaces in NVMe (TP 4091) might be a breakthrough. The proposal defines device-specific ...

SESSION: Short Papers
research-article
Public Access
AMULET: Adaptive Matrix-Multiplication-Like Tasks

Many useful tasks in data science and machine learning applications can be written as simple variations of matrix multiplication. However, users have difficulty performing such tasks as existing matrix/vector libraries support only a limited class of ...

research-article
Zero-sided RDMA: Network-driven Data Shuffling

In this paper, we present a novel communication scheme called zero-sided RDMA, enabling data exchange as a native network service using a programmable switch. In contrast to one- or two-sided RDMA, in zero-sided RDMA, neither the sender nor the receiver ...

research-article
Accelerating Main-Memory Table Scans with Partial Virtual Views

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 ...

research-article
Why Your Experimental Results Might Be Wrong

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 ...

research-article
Random Forests over normalized data in CPU-GPU DBMSes

This short paper studies query execution based on message passing on CPU-GPU systems, using random forests training as the workload. We investigate different data placement and query execution strategies and find that the unique properties of training ...

research-article
Microarchitectural Analysis of Graph BI Queries on RDBMS

We present results of microarchitectural analysis for LDBC SNB BI queries on a relational database engine. We find underutilization of multicore CPUs, inefficient instruction execution, data access overheads at the on-chip cache hierarchy, data TLB ...

research-article
Open Access
Processing-in-Memory for Databases: Query Processing and Data Transfer

The Processing-in-Memory (PIM) paradigm promises to accelerate data processing by pushing down computation to memory, reducing the amount of data transfer between memory and CPU, and – in this way – relieving the CPU from processing. Particularly, in in-...

Contributors
  • SAP SE
  • Massachusetts Institute of Technology

Recommendations

Acceptance Rates

DaMoN '23 Paper Acceptance Rate 17 of 23 submissions, 74%;
Overall Acceptance Rate 94 of 127 submissions, 74%
YearSubmittedAcceptedRate
DaMoN '24251456%
DaMoN '23231774%
DaMoN '22181267%
DAMON '21171588%
DaMoN '20221882%
DaMoN'15161275%
DaMoN '0666100%
Overall1279474%