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 /

PLAST: parallel local alignment search tool for database comparison

Background: Sequence similarity searching is an important and challenging task in molecular biology and next-generation sequencing should further strengthen the need for faster algorithms to process such vast amounts of data. At the same time, the internal architecture of current microprocessors is tending towards more parallelism, leading to the use of chips with two, four and more cores integrated on the same die. The main purpose of this work was to design an effective algorithm to fit with the parallel capabilities of modern microprocessors. Results: A parallel algorithm for comparing large genomic banks and targeting middle-range computers has been developed and implemented in PLAST software. The algorithm exploits two key parallel features of existing and future microprocessors: the SIMD programming model (SSE instruction set) and the multithreading concept (multicore). Compared to multithreaded BLAST software, tests performed on an 8-processor server have shown speedup ranging from 3 to 6 with a similar level of accuracy. Conclusions: A parallel algorithmic approach driven by the knowledge of the internal microprocessor architecture allows significant speedup to be obtained while preserving standard sensitivity for similarity search problems.

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

A Case Study in Pipeline Processor Farming: Parallelising the H.263 Encoder

This paper describes the parallelisation of the H.263 hybrid video encoder algorithm based upon a pipelines of processor farms (PPF) paradigm. In addition, a data-farming template, which can be very useful for several image coding algorithms, was incorporated in the PPF model. A variety of parallel topologies were implemented in order to obtain the best time performance for an eight processor distributed-memory machine. Results show that, due to communication overheads and algorithm constraints, the speed-up performance is below the value predicted by static analysis. However, the design examples indicated how to modify the PPF methodology in identifying those algorithm components which restrict scaling performance. The paper highlights the problems associated with the parallelisation of sequential algorithms and emphasises the need for generic tools to facilitate such conversion. 1 Introduction H.263 is a new standard for very low bit-rate videocoding (!64kbps) which is ..

Shared-memory, distributed-memory, and mixed-mode parallelisation of a CFD simulation code

This paper presents some different approaches to the parallelisation of a harmonic balance Navier-Stokes solver for unsteady aerodynamics. Such simulation codes can require very large amounts of computational resource for realistic simulations, and therefore can benefit significantly from parallelisation. The simulation code addressed in this paper can undertake different modes of aerodynamic simulation and includes both harmonic balance and time domain solvers. These different modes have performance characteristics which can affect any potential parallelisation, as can the specifics of the problem being simulated. Therefore, three different techniques have been used for the parallelisation, shared-memory, distributed-memory, and a combination of the two—a hybrid or mixed-mode parallelisation. These different techniques attempt to address the different performance requirements associated with the types of simulation the code can be used for and provide the level of computational resources required for significant simulation problems. We discuss the different parallelisations and the performance they exhibit on a range of computational resources