MOTIVATION: Reference-based phasing and genotype imputation algorithms have been developed with sublinear theoretical runtime behaviour, but runtimes are still high in practice when large genome-wide reference datasets are used. RESULTS: We developed EagleImp, a software based on the methods used in the existing tools Eagle2 and PBWT, which allows accurate and accelerated phasing and imputation in a single tool by algorithmic and technical improvements and new features. We compared accuracy and runtime of EagleImp with Eagle2, PBWT and prominent imputation servers using whole-genome sequencing data from the 1000 Genomes Project, the Haplotype Reference Consortium and simulated data with 1 million reference genomes. EagleImp was 2-30 times faster (depending on the single or multiprocessor configuration selected and the size of the reference panel) than Eagle2 combined with PBWT, with the same or better phasing and imputation quality in all tested scenarios. For common variants investigated in typical genome-wide association studies, EagleImp provided same or higher imputation accuracy than the Sanger Imputation Service, Michigan Imputation Server and the newly developed TOPMed Imputation Server, despite larger (not publicly available) reference panels. Additional features include automated chromosome splitting and memory management at runtime to avoid job aborts, fast reading and writing of large files and various user-configurable algorithm and output options. Due to the technical optimizations, EagleImp can perform fast and accurate reference-based phasing and imputation and is ready for future large reference panels in the order of 1 million genomes. AVAILABILITY AND IMPLEMENTATION: EagleImp is implemented in C++ and freely available for download at https://github.com/ikmb/eagleimp. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Interaction between genes has become a major topic in quantitative genetics. It is believed that these interactions play a significant role in genetic variations causing complex diseases. Due to the number of tests required for an exhaustive search in genome-wide association studies (GWAS), a large amount of computational power is required. In this paper, we present a hybrid architecture consisting of tightly interconnected CPUs, GPUs and FPGAs and a fine-tuned software suite to outperform other implementations in pairwise interaction analysis while consuming less than 300Watts and fitting into a standard desktop computer case.

High-throughput genotyping technologies (such as SNP-arrays) allow the rapid collection of up to a few million genetic markers of an individual. Detecting epistasis (based on 2-SNP interactions) in Genome-Wide Association Studies is an important but time consuming operation since statistical computations have to be performed for each pair of measured markers. Computational methods to detect epistasis therefore suffer from prohibitively long runtimes; e.g., processing a moderately-sized dataset consisting of about 500,000 SNPs and 5,000 samples requires several days using state-of-the-art tools on a standard 3 GHz CPU. In this paper, we demonstrate how this task can be accelerated using a combination of fine-grained and coarse-grained parallelism on two different computing systems. The first architecture is based on reconfigurable hardware (FPGAs) while the second architecture uses multiple GPUs connected to the same host. We show that both systems can achieve speedups of around four orders-of-magnitude compared to the sequential implementation. This significantly reduces the runtimes for detecting epistasis to only a few minutes for moderately-sized datasets and to a few hours for large-scale datasets.

Modern genotyping technologies are able to obtain up to a few million genetic markers (such as SNPs) of an individual within a few minutes of time. Detecting epistasis, such as SNP-SNP interactions, in Genome-Wide Association Studies is an important but time-consuming operation since statistical computations have to be performed for each pair of measured markers. Therefore, a variety of HPC architectures have been used to accelerate these studies. In this work we present a parallel approach for multi-core clusters, which is implemented with UPC++ and takes advantage of the features available in the Partitioned Global Address Space and Object Oriented Programming models. Our solution is based on a well-known regression model (used by the popular BOOST tool) to test SNP-pairs interactions. Experimental results show that UPC++ is suitable for parallelizing data-intensive bioinformatics applications on clusters. For instance, it reduces the time to analyze a real-world dataset with more than 500,000 SNPs and 5,000 individuals from several days when using a single core to less than one minute using 512 nodes (12,288 cores) of a Cray XC30 supercomputer.

A weakness of many security systems is the strength of the chosen password or key derivation function. We show how FPGA technology can be used to effectively attack cryptographic applications with a password dictionary. We have implemented two independent PBKDF2 cores each using four HMAC cores with pipelines calculating a RIPEMD-160 hash to derive encryption keys together with one resource optimized AES-256 XTS core for direct decryption on a Xilinx Spartan6-LX150 FPGA. Our design targets TRUECRYPT containers, but may be applied to similar encryption tools with little adaption. In order to save resources and maximize speed, we have further optimized the RIPEMD-160 hash function for this purpose. Our design executed on the multi-FPGA system RIVYERA S6-LX150 containing 128 S6-LX150 FPGAs, finally reaches a peak performance of about 245,000 passwords per second.

The popular free encryption software True Crypt uses whole device or partition encryption as well as encrypted container files to protect sensible data from unauthorized access. Several combinations of encryption algorithms and hash functions used for the key derivation can be chosen by the user. This paper regards the combination with SERPENT as encryption algorithm and WHIRLPOOL as hash function for the key derivation. A dictionary attack has been implemented for this combination using the FPGA-based high-performance computer RIVYERA S3-5000. The achieved performance reaches more than 200,000 passwords per second. Compared to 820 passwords per second, achieved by a fully threaded Intel Core i7-970 system at 3.2GHz using the Crypto++ library, this leads to a speedup of more than 247 with energy savings of about 99%.