Parallel and Distributed Architectures

Our group focuses on the design, implementation and evaluation of scalable tools for genomic sequence analysis (Bioinformatics) and computational science applications. Our approach is based on using modern high performance computing (HPC) technologies, such as

• Manycore architectures (such as CUDA-enabled GPUs)
• Multicore architectures
• Heterogeneous clusters
• FPGAs

Using these platforms we design efficient parallel algorithms that can serve as a foundation for a wide variety of tools, such as

• short/long-read aligners (e.g. CUSHAW)
• de-novo short/long-read genome assemblers (e.g. PASHA, Taipan)

• short/long-read clustering (e.g. CRiSPy-CUDA, DySC)
• multiple sequence aligners (e.g. MSA-Probs, MSA-CUDA)
• sequence database searching (e.g. CUDA-BLASTP, CUDASW++ (Smith-Waterman))
• short/long-read error correction (e.g. SHREC, DecGPU)
• motif finding (e.g. CUDA-MEME)
• solving linear equations by iterative sparse matrix-vector multiplication (e.g. Integer factorization for RSA cryptography with the Block-Wiedemann algorithm)

Our methods and tools are often developed in collaboration with interdisciplinary partners at JGU Mainz, such as the Department of Biology, Medical School, and the Institute of Molecular Biology.

For the pioneering work in the area of CUDA-enabled bioinformatics, we have been awarded the status of a CUDA Research Center and a CUDA Teaching Center.