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.
Proceeding Downloads
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-...
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 ...
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 ...
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, ...
Elastic Use of Far Memory for In-Memory Database Management Systems
- Donghun Lee,
- Thomas Willhalm,
- Minseon Ahn,
- Suprasad Mutalik Desai,
- Daniel Booss,
- Navneet Singh,
- Daniel Ritter,
- Jungmin Kim,
- Oliver Rebholz
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 ...
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-...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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 ...
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-...