A fast bit-vector algorithm for approximate string matching based on dynamic programming
Journal of the ACM (JACM)
A guided tour to approximate string matching
ACM Computing Surveys (CSUR)
Queue - GPU Computing
Traffic Aggregation for Malware Detection
DIMVA '08 Proceedings of the 5th international conference on Detection of Intrusions and Malware, and Vulnerability Assessment
Designing efficient sorting algorithms for manycore GPUs
IPDPS '09 Proceedings of the 2009 IEEE International Symposium on Parallel&Distributed Processing
Understanding throughput-oriented architectures
Communications of the ACM
Architecture-Aware Mapping and Optimization on a 1600-Core GPU
ICPADS '11 Proceedings of the 2011 IEEE 17th International Conference on Parallel and Distributed Systems
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General-purpose computation on graphics processing units (GPGPU) has great potential to accelerate many scientific models and algorithms. However, some problems are considerably more difficult to accelerate than others, and it may be difficult for those new to GPGPU to ascertain the difficulty of accelerating a particular problem. Additionally, problems of different levels of difficulty require varying complexities of optimisations to achieve satisfactory results, and currently there is no clear separation between the different levels of known optimisations, which would be helpful to new users of GPGPU. Through what was learned in the acceleration of three problems, problem attributes have been identified to assist in evaluating the difficulty of accelerating a problem on a GPU. We envisage that with further development, these attributes could form the foundation of a difficulty classification system that could be used to determine whether GPU acceleration is practical for a candidate GPU acceleration problem, aid in identifying appropriate techniques and optimisations, and outline the required GPGPU knowledge.