SWIFOLD: Smith-Waterman implementation on FPGA with OpenCL for long DNA sequences.

BACKGROUND: The Smith-Waterman (SW) algorithm is the best choice for searching similar regions between two DNA or protein sequences. However, it may become impracticable in some contexts due to its high computational demands. Consequently, the computer science community has focused on the use of modern parallel architectures such as Graphics Processing Units (GPUs), Xeon Phi accelerators and Field Programmable Gate Arrays (FGPAs) to speed up large-scale workloads. RESULTS: This paper presents and evaluates SWIFOLD: a Smith-Waterman parallel Implementation on FPGA with OpenCL for Long DNA sequences. First, we evaluate its performance and resource usage for different kernel configurations. Next, we carry out a performance comparison between our tool and other state-of-the-art implementations considering three different datasets. SWIFOLD offers the best average performance for small and medium test sets, achieving a performance that is independent of input size and sequence similarity. In addition, SWIFOLD provides competitive performance rates in comparison with GPU-based implementations on the latest GPU generation for the large dataset. CONCLUSIONS: The results suggest that SWIFOLD can be a serious contender for accelerating the SW alignment of DNA sequences of unrestricted size in an affordable way reaching on average 125 GCUPS and almost a peak of 270 GCUPS.

Implementation of a low-cost mobile devices to support medical diagnosis.

Medical imaging has become an absolutely essential diagnostic tool for clinical practices; at present, pathologies can be detected with an earliness never before known. Its use has not only been relegated to the field of radiology but also, increasingly, to computer-based imaging processes prior to surgery. Motion analysis, in particular, plays an important role in analyzing activities or behaviors of live objects in medicine. This short paper presents several low-cost hardware implementation approaches for the new generation of tablets and/or smartphones for estimating motion compensation and segmentation in medical images. These systems have been optimized for breast cancer diagnosis using magnetic resonance imaging technology with several advantages over traditional X-ray mammography, for example, obtaining patient information during a short period. This paper also addresses the challenge of offering a medical tool that runs on widespread portable devices, both on tablets and/or smartphones to aid in patient diagnostics.

A low cost matching motion estimation sensor based on the NIOS II microprocessor.

This work presents the implementation of a matching-based motion estimation sensor on a Field Programmable Gate Array (FPGA) and NIOS II microprocessor applying a C to Hardware (C2H) acceleration paradigm. The design, which involves several matching algorithms, is mapped using Very Large Scale Integration (VLSI) technology. These algorithms, as well as the hardware implementation, are presented here together with an extensive analysis of the resources needed and the throughput obtained. The developed low-cost system is practical for real-time throughput and reduced power consumption and is useful in robotic applications, such as tracking, navigation using an unmanned vehicle, or as part of a more complex system.