People | Locations | Statistics |
---|---|---|
Naji, M. |
| |
Motta, Antonella |
| |
Aletan, Dirar |
| |
Mohamed, Tarek |
| |
Ertürk, Emre |
| |
Taccardi, Nicola |
| |
Kononenko, Denys |
| |
Petrov, R. H. | Madrid |
|
Alshaaer, Mazen | Brussels |
|
Bih, L. |
| |
Casati, R. |
| |
Muller, Hermance |
| |
Kočí, Jan | Prague |
|
Šuljagić, Marija |
| |
Kalteremidou, Kalliopi-Artemi | Brussels |
|
Azam, Siraj |
| |
Ospanova, Alyiya |
| |
Blanpain, Bart |
| |
Ali, M. A. |
| |
Popa, V. |
| |
Rančić, M. |
| |
Ollier, Nadège |
| |
Azevedo, Nuno Monteiro |
| |
Landes, Michael |
| |
Rignanese, Gian-Marco |
|
Pietrzyk, Johannes
in Cooperation with on an Cooperation-Score of 37%
Topics
Publications (1/1 displayed)
Places of action
Organizations | Location | People |
---|
article
Partition-based SIMD Processing and its Application to Columnar Database Systems
Abstract
<jats:title>Abstract</jats:title><jats:p>The Single Instruction Multiple Data (SIMD) paradigm became a core principle for optimizing query processing in columnar database systems. Until now, only theinstructions are considered to be efficient enough to achieve the expected speedups, while avoidingis considered almost imperative. However, theinstruction offers a very flexible way to populate SIMD registers with data elements coming from non-consecutive memory locations. As we will discuss within this article, theinstruction can achieve the same performance as theinstruction, if applied properly. To enable the proper usage, we outline a novel access pattern allowing fine-grained, partition-based SIMD implementations. Then, we apply this partition-based SIMD processing to two representative examples from columnar database systems to experimentally demonstrate the applicability and efficiency of our new access pattern.</jats:p>