Sisir Koppaka | Searcharxiv

arXiv2010

Fast Histograms using Adaptive CUDA Streams

Histograms are widely used in medical imaging, network intrusion detection, packet analysis and other stream-based high throughput applications. However, while porting such software stacks to the GPU, the computation of the histogram is a typical bottleneck primarily due to the large impact on kernel speed by atomic operations. In this work, we propose a stream-based model implemented in CUDA, using a new adaptive kernel that can be optimized based on latency hidden CPU compute. We also explore the tradeoffs of using the new kernel vis-à-vis the stock NVIDIA SDK kernel, and discuss an intelligent kernel switching method for the stream based on a degeneracy criterion that is adaptively computed from the input stream.

cs.DC↗

arXiv2010

Superior Exploration-Exploitation Balance with Quantum-Inspired Hadamard Walks

This paper extends the analogies employed in the development of quantum-inspired evolutionary algorithms by proposing quantum-inspired Hadamard walks, called QHW. A novel quantum-inspired evolutionary algorithm, called HQEA, for solving combinatorial optimization problems, is also proposed. The novelty of HQEA lies in it's incorporation of QHW Remote Search and QHW Local Search - the quantum equivalents of classical mutation and local search, that this paper defines. The intuitive reasoning behind this approach, and the exploration-exploitation balance thus occurring is explained. From the results of the experiments carried out on the 0,1-knapsack problem, HQEA performs significantly better than a conventional genetic algorithm, CGA, and two quantum-inspired evolutionary algorithms - QEA and NQEA, in terms of convergence speed and accuracy.

cs.NE↗