9,585 research outputs found
Genome-wide inference of ancestral recombination graphs
The complex correlation structure of a collection of orthologous DNA
sequences is uniquely captured by the "ancestral recombination graph" (ARG), a
complete record of coalescence and recombination events in the history of the
sample. However, existing methods for ARG inference are computationally
intensive, highly approximate, or limited to small numbers of sequences, and,
as a consequence, explicit ARG inference is rarely used in applied population
genomics. Here, we introduce a new algorithm for ARG inference that is
efficient enough to apply to dozens of complete mammalian genomes. The key idea
of our approach is to sample an ARG of n chromosomes conditional on an ARG of
n-1 chromosomes, an operation we call "threading." Using techniques based on
hidden Markov models, we can perform this threading operation exactly, up to
the assumptions of the sequentially Markov coalescent and a discretization of
time. An extension allows for threading of subtrees instead of individual
sequences. Repeated application of these threading operations results in highly
efficient Markov chain Monte Carlo samplers for ARGs. We have implemented these
methods in a computer program called ARGweaver. Experiments with simulated data
indicate that ARGweaver converges rapidly to the true posterior distribution
and is effective in recovering various features of the ARG for dozens of
sequences generated under realistic parameters for human populations. In
applications of ARGweaver to 54 human genome sequences from Complete Genomics,
we find clear signatures of natural selection, including regions of unusually
ancient ancestry associated with balancing selection and reductions in allele
age in sites under directional selection. Preliminary results also indicate
that our methods can be used to gain insight into complex features of human
population structure, even with a noninformative prior distribution.Comment: 88 pages, 7 main figures, 22 supplementary figures. This version
contains a substantially expanded genomic data analysi
Fat vs. thin threading approach on GPUs: application to stochastic simulation of chemical reactions
We explore two different threading approaches on a graphics processing unit (GPU) exploiting two different characteristics of the current GPU architecture. The fat thread approach tries to minimise data access time by relying on shared memory and registers potentially sacrificing parallelism. The thin thread approach maximises parallelism and tries to hide access latencies. We apply these two approaches to the parallel stochastic simulation of chemical reaction systems using the stochastic simulation algorithm (SSA) by Gillespie (J. Phys. Chem, Vol. 81, p. 2340-2361, 1977). In these cases, the proposed thin thread approach shows comparable performance while eliminating the limitation of the reaction system’s size
Pipelined genetic propagation
© 2015 IEEE.Genetic Algorithms (GAs) are a class of numerical and combinatorial optimisers which are especially useful for solving complex non-linear and non-convex problems. However, the required execution time often limits their application to small-scale or latency-insensitive problems, so techniques to increase the computational efficiency of GAs are needed. FPGA-based acceleration has significant potential for speeding up genetic algorithms, but existing FPGA GAs are limited by the generational approaches inherited from software GAs. Many parts of the generational approach do not map well to hardware, such as the large shared population memory and intrinsic loop-carried dependency. To address this problem, this paper proposes a new hardware-oriented approach to GAs, called Pipelined Genetic Propagation (PGP), which is intrinsically distributed and pipelined. PGP represents a GA solver as a graph of loosely coupled genetic operators, which allows the solution to be scaled to the available resources, and also to dynamically change topology at run-time to explore different solution strategies. Experiments show that pipelined genetic propagation is effective in solving seven different applications. Our PGP design is 5 times faster than a recent FPGA-based GA system, and 90 times faster than a CPU-based GA system
Fine-Grain Iterative Compilation for WCET Estimation
Compiler optimizations, although reducing the execution times of programs, raise issues in static WCET estimation techniques and tools. Flow facts, such as loop bounds, may not be automatically found by static WCET analysis tools after aggressive code optimizations. In this paper, we explore the use of iterative compilation (WCET-directed program optimization to explore the optimization space), with the objective to (i) allow flow facts to be automatically found and (ii) select optimizations that result in the lowest WCET estimates. We also explore to which extent code outlining helps, by allowing the selection of different optimization options for different code snippets of the application
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