K. Forster

Paper “Efficient SIMD Vectorization for Hashing in OpenCL” accepted for publishing at EDBT 2018

The paper shows an experimental study about OpenCL-based vectorization, that is competitive to intrinsics on CPUs and XEON Phi coprocessors.

Efficient SIMD Vectorization for Hashing in OpenCL , Tobias Behrens, Viktor Rosenfeld, Jonas Traub, Sebastian Breß, Volker Markl. 21st International Conference on Extending Database Technology (EDBT), Vienna, March 26-29, 2018.

Abstract :
Hashing is at the core of many efficient database operators such as hash-based joins and aggregations. Vectorization is a technique that uses Single Instruction Multiple Data (SIMD) instructions to process multiple data elements at once. Applying vectorization to hash tables results in promising speedups for build and probe operations. However, vectorization typically requires intrinsics – low-level APIs in which functions map to processor-specific SIMD instructions. Intrinsics are specific to a processor architecture and result in complex and difficult to maintain code.

Link to publication preprint