We present a shared memory approach to the parallelization of the Ant Colony Optimization (ACO) metaheuristic and a performance comparison with an existing message passing implementation. Our aim is to show that the shared memory approach is a competitive strategy for the parallelization of ACO algorithms. The sequential ACO algorithm on which are based both parallelization schemes is first des...

This paper introduces the parallelization optimization of system-level specification, which explores maximal parallelism among functional blocks of the design. We introduce two tools, spec profiler and spec optimizer, to implement the parallelization optimization automatically.

| This paper presents the parallelization process of a Wave Propagation application using the Pandore environment. Tools are brie y described, the stress is put on the description of the parallelization by data distribution and performance results are shown.

Data mining techniques focus on finding novel and useful patterns or models from large datasets. Because of the volume of the data to be analyzed, the amount of computation involved, and the need for rapid or even interactive analysis, data mining applications require the use of parallel machines. We have been developing compiler and runtime support for developing scalable implementations of da...

The past few years have witnessed growth in the size and computational requirements for training deep convolutional neural networks. Current approaches parallelize the training process onto multiple devices by applying a single parallelization strategy (e.g., data or model parallelism) to all layers in a network. Although easy to reason about, this design results in suboptimal runtime performan...