Frequent Pattern-growth Algorithm on Multi-core CPU and GPU Processors

Frequent Pattern-growth Algorithm on Multi-core CPU and GPU Processors

Discovering association rules that identify relationships among sets of items is an important problem in data mining. It’s a two steps process, the first step finds all frequent itemsets and the second one constructs association rules from these frequent sets. Finding frequent itemsets is computationally the most expensive step in association rules discovery algorithms. Utilizing parallel archi...

pyPaSWAS: Python-based multi-core CPU and GPU sequence alignment

BACKGROUND Our previously published CUDA-only application PaSWAS for Smith-Waterman (SW) sequence alignment of any type of sequence on NVIDIA-based GPUs is platform-specific and therefore adopted less than could be. The OpenCL language is supported more widely and allows use on a variety of hardware platforms. Moreover, there is a need to promote the adoption of parallel computing in bioinforma...

Cache optimization for CPU - GPU heterogeneous processors ∗

Microprocessors combining CPU and GPU cores using a common last-level cache pose new challenges to cache management algorithms. Since GPU cores feature much higher data access rates than CPU cores, the majority of the available cache space will be used by GPU applications, leaving only very limited cache capacity for CPU applications, which may be disadvantageous for overall system performance....

Algorithm libraries for multi-core processors

Game loop model properties and characteristics on multi-core CPU and GPU games

From a software architecture point of view, digital games are interactive systems that create virtual environments with the illusion that everything is happening at the same time (i.e. in realtime). This illusion is carried out through “game loop models” (or architectures). Although this illusion is a key feature in these applications, the patterns and requirements that lead to its implementati...