Sub-matrix updates for the Continuous-Time Auxiliary Field algorithm

We present a sub-matrix update algorithm for the continuous-time auxiliary field method that allows the simulation of large lattice and impurity problems. The algorithm takes optimal advantage of modern CPU architectures by consistently using matrix instead of vector operations, resulting in a speedup of a factor of $\approx 8$ and thereby allowing access to larger systems and lower temperature. We illustrate the power of our algorithm at the example of a cluster dynamical mean field simulation of the N\'{e}el transition in the three-dimensional Hubbard model, where we show momentum dependent self-energies for clusters with up to 100 sites

Analysing Astronomy Algorithms for GPUs and Beyond

Astronomy depends on ever increasing computing power. Processor clock-rates have plateaued, and increased performance is now appearing in the form of additional processor cores on a single chip. This poses significant challenges to the astronomy software community. Graphics Processing Units (GPUs), now capable of general-purpose computation, exemplify both the difficult learning-curve and the significant speedups exhibited by massively-parallel hardware architectures. We present a generalised approach to tackling this paradigm shift, based on the analysis of algorithms. We describe a small collection of foundation algorithms relevant to astronomy and explain how they may be used to ease the transition to massively-parallel computing architectures. We demonstrate the effectiveness of our approach by applying it to four well-known astronomy problems: Hogbom CLEAN, inverse ray-shooting for gravitational lensing, pulsar dedispersion and volume rendering. Algorithms with well-defined memory access patterns and high arithmetic intensity stand to receive the greatest performance boost from massively-parallel architectures, while those that involve a significant amount of decision-making may struggle to take advantage of the available processing power.Comment: 10 pages, 3 figures, accepted for publication in MNRA

Non-smooth optimization methods for computation of the conditional value-at-risk and portfolio optimization

We examine numerical performance of various methods of calculation of the Conditional Value-at-risk (CVaR), and portfolio optimization with respect to this risk measure. We concentrate on the method proposed by Rockafellar and Uryasev in (Rockafellar, R.T. and Uryasev, S., 2000, Optimization of conditional value-at-risk. Journal of Risk, 2, 21-41), which converts this problem to that of convex optimization. We compare the use of linear programming techniques against a non-smooth optimization method of the discrete gradient, and establish the supremacy of the latter. We show that non-smooth optimization can be used efficiently for large portfolio optimization, and also examine parallel execution of this method on computer clusters.<br /

Staphylococcal protein Ecb impairs complement receptor-1 mediated recognition of opsonized bacteria

Staphyloccus aureus is a major human pathogen leading frequently to sepsis and soft tissue infections with abscesses. Multiple virulence factors including several immune modulating molecules contribute to its survival in the host. When S. aureus invades the human body, one of the first line defenses is the complement system, which opsonizes the bacteria with C3b and attract neutrophils by release of chemotactic peptides. Neutrophils express Complement receptor-1 [CR1, CD35) that interacts with the C3b-opsonized particles and thereby plays an important role in pathogen recognition by phagocytic cells. In this study we observed that a fraction of S. aureus culture supernatant prevented binding of C3b to neutrophils. This fraction consisted of S. aureus leukocidins and Efb. The C-terminus of Efb is known to bind C3b and shares significant sequence homology to the extracellular complement binding protein [Ecb). Here we show that S. aureus Ecb displays various mechanisms to block bacterial recognition by neutrophils. The presence of Ecb blocked direct interaction between soluble CR1 and C3b and reduced the cofactor activity of CR1 in proteolytic inactivation of C3b. Furthermore, Ecb could dose-dependently prevent recognition of C3b by cell-bound CR1 that lead to impaired phagocytosis of NHS-opsonized S. aureus. Phagocytosis was furthermore reduced in the presence of soluble CR1 [sCR1). These data indicate that the staphylococcal protein Ecb prevents recognition of C3b opsonized bacteria by neutrophil CR1 leading to impaired killing by phagocytosis and thereby contribute to immune evasion of S. aureus.Peer reviewe

Microbes Bind Complement Inhibitor Factor H via a Common Site

To cause infections microbes need to evade host defense systems, one of these being the evolutionarily old and important arm of innate immunity, the alternative pathway of complement. It can attack all kinds of targets and is tightly controlled in plasma and on host cells by plasma complement regulator factor H (FH). FH binds simultaneously to host cell surface structures such as heparin or glycosaminoglycans via domain 20 and to the main complement opsonin C3b via domain 19. Many pathogenic microbes protect themselves from complement by recruiting host FH. We analyzed how and why different microbes bind FH via domains 19–20 (FH19-20). We used a selection of FH19-20 point mutants to reveal the binding sites of several microbial proteins and whole microbes (Haemophilus influenzae, Bordetella pertussis, Pseudomonas aeruginosa, Streptococcus pneumonia, Candida albicans, Borrelia burgdorferi, and Borrelia hermsii). We show that all studied microbes use the same binding region located on one side of domain 20. Binding of FH to the microbial proteins was inhibited with heparin showing that the common microbial binding site overlaps with the heparin site needed for efficient binding of FH to host cells. Surprisingly, the microbial proteins enhanced binding of FH19-20 to C3b and down-regulation of complement activation. We show that this is caused by formation of a tripartite complex between the microbial protein, FH, and C3b. In this study we reveal that seven microbes representing different phyla utilize a common binding site on the domain 20 of FH for complement evasion. Binding via this site not only mimics the glycosaminoglycans of the host cells, but also enhances function of FH on the microbial surfaces via the novel mechanism of tripartite complex formation. This is a unique example of convergent evolution resulting in enhanced immune evasion of important pathogens viautilization of a “superevasion site.

BioPhysConnectoR: Connecting Sequence Information and Biophysical Models

<p>Abstract</p> <p>Background</p> <p>One of the most challenging aspects of biomolecular systems is the understanding of the coevolution in and among the molecule(s).</p> <p>A complete, theoretical picture of the selective advantage, and thus a functional annotation, of (co-)mutations is still lacking. Using sequence-based and information theoretical inspired methods we can identify coevolving residues in proteins without understanding the underlying biophysical properties giving rise to such coevolutionary dynamics. Detailed (atomistic) simulations are prohibitively expensive. At the same time reduced molecular models are an efficient way to determine the reduced dynamics around the native state. The combination of sequence based approaches with such reduced models is therefore a promising approach to annotate evolutionary sequence changes.</p> <p>Results</p> <p>With the <monospace>R</monospace> package <monospace>BioPhysConnectoR</monospace> we provide a framework to connect the information theoretical domain of biomolecular sequences to biophysical properties of the encoded molecules - derived from reduced molecular models. To this end we have integrated several fragmented ideas into one single package ready to be used in connection with additional statistical routines in <monospace>R</monospace>. Additionally, the package leverages the power of modern multi-core architectures to reduce turn-around times in evolutionary and biomolecular design studies. Our package is a first step to achieve the above mentioned annotation of coevolution by reduced dynamics around the native state of proteins.</p> <p>Conclusions</p> <p><monospace>BioPhysConnectoR</monospace> is implemented as an <monospace>R</monospace> package and distributed under GPL 2 license. It allows for efficient and perfectly parallelized functional annotation of coevolution found at the sequence level.</p

Accelerated large-scale multiple sequence alignment

<p>Abstract</p> <p>Background</p> <p>Multiple sequence alignment (MSA) is a fundamental analysis method used in bioinformatics and many comparative genomic applications. Prior MSA acceleration attempts with reconfigurable computing have only addressed the first stage of progressive alignment and consequently exhibit performance limitations according to Amdahl's Law. This work is the first known to accelerate the third stage of progressive alignment on reconfigurable hardware.</p> <p>Results</p> <p>We reduce subgroups of aligned sequences into discrete profiles before they are pairwise aligned on the accelerator. Using an FPGA accelerator, an overall speedup of up to 150 has been demonstrated on a large data set when compared to a 2.4 GHz Core2 processor.</p> <p>Conclusions</p> <p>Our parallel algorithm and architecture accelerates large-scale MSA with reconfigurable computing and allows researchers to solve the larger problems that confront biologists today. Program source is available from <url>http://dna.cs.byu.edu/msa/</url>.</p

Model-driven approach for supporting the mapping of parallel algorithms to parallel computing platforms

The trend from single processor to parallel computer architectures has increased the importance of parallel computing. To support parallel computing it is important to map parallel algorithms to a computing platform that consists of multiple parallel processing nodes. In general different alternative mappings can be defined that perform differently with respect to the quality requirements for power consumption, efficiency and memory usage. The mapping process can be carried out manually for platforms with a limited number of processing nodes. However, for exascale computing in which hundreds of thousands of processing nodes are applied, the mapping process soon becomes intractable. To assist the parallel computing engineer we provide a model-driven approach to analyze, model, and select feasible mappings. We describe the developed toolset that implements the corresponding approach together with the required metamodels and model transformations. We illustrate our approach for the well-known complete exchange algorithm in parallel computing. © 2013 Springer-Verlag