Inexact Sequence Mapping Study Cases: Hybrid GPU Computing and Memory Demanding Indexes

Due to the NGS data deluge, sequence mapping has become an intensive task that, depending on the experiment, may demand high amounts of computing power or memory capacity. On the one hand, GPGPU architectures have become a cost-effective solution that outperforms common processors in specific tasks. On the other hand, out-of-core implementations allow to directly access data from secondary memory, which may be useful when mapping against big indexes in systems with low memory configurations. In this paper we discuss the implementation of backward search methods for inexact mapping in these two study cases.