Name
Abstract | ||
|---|---|---|
In-memory databases require careful tuning and many engineering tricks to achieve good performance. Such database performance engineering is hard: a plethora of data and hardware-dependent optimization techniques form a design space that is difficult to navigate for a skilled engineer --- even more so for a query compiler. To facilitate performance-oriented design exploration and query plan compilation, we present Voodoo, a declarative intermediate algebra that abstracts the detailed architectural properties of the hardware, such as multi- or many-core architectures, caches and SIMD registers, without losing the ability to generate highly tuned code. Because it consists of a collection of declarative, vector-oriented operations, Voodoo is easier to reason about and tune than low-level C and related hardware-focused extensions (Intrinsics, OpenCL, CUDA, etc.). This enables our Voodoo compiler to produce (OpenCL) code that rivals and even outperforms the fastest state-of-the-art in memory databases for both GPUs and CPUs. In addition, Voodoo makes it possible to express techniques as diverse as cache-conscious processing, predication and vectorization (again on both GPUs and CPUs) with just a few lines of code. Central to our approach is a novel idea we termed control vectors, which allows a code generating frontend to expose parallelism to the Voodoo compiler in a abstract manner, enabling portable performance across hardware platforms. We used Voodoo to build an alternative backend for MonetDB, a popular open-source in-memory database. Our backend allows MonetDB to perform at the same level as highly tuned in-memory databases, including HyPeR and Ocelot. We also demonstrate Voodoo's usefulness when investigating hardware conscious tuning techniques, assessing their performance on different queries, devices and data. |
Year | DOI | Venue |
|---|---|---|
2016 | 10.14778/3007328.3007336 | PVLDB |
Field | DocType | Volume |
Data mining,Programming language,CUDA,Computer science,Vectorization (mathematics),SIMD,Computer hardware,Intrinsics,Query plan,Source lines of code,Database tuning,Computer architecture,Compiler,Database | Journal | 9 |
Issue | ISSN | Citations |
14 | 2150-8097 | 21 |
PageRank | References | Authors |
0.74 | 23 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Holger Pirk | 1 | 159 | 11.12 |
| Oscar R. Moll Thomae | 2 | 34 | 2.30 |
| Matei Zaharia | 3 | 9101 | 407.89 |
| Samuel Madden | 4 | 16101 | 1176.38 |