User transparent data and task parallel multimedia computing with Pyxis-DT

• We present Pyxis-DT, a programming model for multimedia applications on clusters.
• Pyxis-DT can exploit data and task parallelism both in isolation and concurrently.
• Hybrid parallelism can outperform strict data or task parallelism.
• GPU extensions to Pyxis-DT can improve performance several orders of magnitude.

The research area of Multimedia Content Analysis (MMCA) considers all aspects of the automated extraction of knowledge from multimedia archives and data streams. To satisfy the increasing computational demands of emerging MMCA problems, there is an urgent need to apply High Performance Computing (HPC) techniques. As most MMCA researchers are not also HPC experts, however, there is a demand for programming models and tools that are both efficient and easy to use. Existing user transparent parallelization tools generally use a data parallel approach in which data structures (e.g. video frames) are scattered among the available nodes in a compute cluster. For certain MMCA applications a data parallel approach induces intensive communication, however, which significantly decreases performance. In these situations, we can benefit from applying alternative approaches.We present Pyxis-DT, a user transparent parallel programming model for MMCA applications that employs both data and task parallelism. Hybrid parallel execution is obtained by run-time construction and execution of a task graph consisting of strictly defined building block operations. Results show that for realistic MMCA applications the concurrent use of data and task parallelism can significantly improve performance compared to using either approach in isolation. Extensions for GPU clusters are also presented.