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.
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A comparison of the use of virtual versus physical snapshots for supporting update-intensive workloads
Deployments of networked sensors fuel online applications that feed on real-time sensor data. This scenario calls for techniques that support the management of workloads that contain queries as well as very frequent updates. This paper compares two well-...
Reducing OLTP instruction misses with thread migration
During an instruction miss a processor is unable to fetch instructions. The more frequent instruction misses are the less able a modern processor is to find useful work to do and thus performance suffers. Online transaction processing (OLTP) suffers ...
KISS-Tree: smart latch-free in-memory indexing on modern architectures
Growing main memory capacities and an increasing number of hardware threads in modern server systems led to fundamental changes in database architectures. Most importantly, query processing is nowadays performed on data that is often completely stored ...
Making cost-based query optimization asymmetry-aware
The architecture and algorithms of database systems have been built around the properties of existing hardware technologies. Many such elementary design assumptions are 20--30 years old. Over the last five years we witness multiple new I/O technologies (...
Hathi: durable transactions for memory using flash
Recent architectural trends---cheap, fast solid-state storage, inexpensive DRAM, and multi-core CPUs---provide an opportunity to rethink the interface between applications and persistent storage. To leverage these advances, we propose a new system ...
Ameliorating memory contention of OLAP operators on GPU processors
Implementations of database operators on GPU processors have shown dramatic performance improvement compared to multicore-CPU implementations. GPU threads can cooperate using shared memory, which is organized in interleaved banks and is fast only when ...
X-device query processing by bitwise distribution
The diversity of hardware components within a single system calls for strategies for efficient cross-device data processing. For example, existing approaches to CPU/GPU co-processing distribute individual relational operators to the "most appropriate" ...
GPU join processing revisited
Until recently, the use of graphics processing units (GPUs) for query processing was limited by the amount of memory on the graphics card, a few gigabytes at best. Moreover, input tables had to be copied to GPU memory before they could be processed, and ...
GiST scan acceleration using coprocessors
Efficient lookups in huge, possibly multi-dimensional datasets are crucial for the performance of numerous use cases that generate multiple search operations at the same time, like point queries in ray tracing or spatial joins in collision detection of ...