Implementation of the Vectorized Position-Specific Iterated Smith-Waterman Algorithm with Heuristic Filtering Algorithm on Cray Architecture

ABSTRACT: Smith-Waterman is the algorithm for finding local optimal alignments of two amino acid sequences, but is too slow for use in regular large database scanning. We have implemented SW algorithm on Cray X1 architecture, exploiting vectorization and parallelization. In addition we have proposed heuristic database filtering algorithm, which is particularly well suited for the Cray architecture, exploiting possibilities given by the BMM unit

Performance analysis of parallel applications on modern multithreaded processor architectures

In this whitepaper we describe the effort we have made to measure performance of applications and synthetic benchmarks with the use of different simultaneous multithreading (SMT) modes. This specific processor architecture feature is currently available in many petascale HPC systems worldwide. Both IBM Power7 processors available in Power775 (IH) and IBM Power A2 processors available in Blue Gene/Q are built upon 4-way simultaneous multithreaded cores. It should be also mentioned that multithreading is predicted to be one of the leading features of future exascale systems available by the end of next decade [1]

Enabling Large Scale Individual-Based Modelling through High Performance Computing

We present a novel large scale parallel computational model allowing 3-D simulations of cell colonies growing and interacting with variable environment. The cells are modelled as individual objects located in the lattice-free 3-D space. The model incorporates cellular environment described in a continuous manner. Discrete and continuous formulations are eficiently coupled in one model allowing considerations of cell colonies composed of up to 109 individual cells. This large scale computational approach enables simulations to be carried out over realistic spatial scales up to 1cm3 in size i.e. the tissue scale

Parallel Position-Specific Iterated Smith-Waterman Algorithm Implementation

ABSTRACT: We have implemented a parallel version of Position-Specific Iterated Smith-Waterman algorithm using UPC. The core Smith-Waterman implementation is vectorized for Cray X1E, but it is possible to use an FPGA core instead. The quality of results and performance are discussed

Implementation of an Agent-Based Parallel Tissue Modelling Framework for the Intel MIC Architecture

Timothy is a novel large scale modelling framework that allows simulating of biological processes involving different cellular colonies growing and interacting with variable environment. Timothy was designed for execution on massively parallel High Performance Computing (HPC) systems. The high parallel scalability of the implementation allows for simulations of up to 109 individual cells (i.e., simulations at tissue spatial scales of up to 1 cm3 in size). With the recent advancements of the Timothy model, it has become critical to ensure appropriate performance level on emerging HPC architectures. For instance, the introduction of blood vessels supplying nutrients to the tissue is a very important step towards realistic simulations of complex biological processes, but it greatly increased the computational complexity of the model. In this paper, we describe the process of modernization of the application in order to achieve high computational performance on HPC hybrid systems based on modern Intel® MIC architecture. Experimental results on the Intel Xeon Phi™ coprocessor x100 and the Intel Xeon Phi processor x200 are presented

Implementation of an Agent-Based Parallel Tissue Modelling Framework for the Intel MIC Architecture

Timothy is a novel large scale modelling framework that allows simulating of biological processes involving different cellular colonies growing and interacting with variable environment. Timothy was designed for execution on massively parallel High Performance Computing (HPC) systems. The high parallel scalability of the implementation allows for simulations of up to 109 individual cells (i.e., simulations at tissue spatial scales of up to 1 cm3 in size). With the recent advancements of the Timothy model, it has become critical to ensure appropriate performance level on emerging HPC architectures. For instance, the introduction of blood vessels supplying nutrients to the tissue is a very important step towards realistic simulations of complex biological processes, but it greatly increased the computational complexity of the model. In this paper, we describe the process of modernization of the application in order to achieve high computational performance on HPC hybrid systems based on modern Intel® MIC architecture. Experimental results on the Intel Xeon Phi™ coprocessor x100 and the Intel Xeon Phi processor x200 are presented

Computational Modelling of Cancer Development and Growth:Modelling at Multiple Scales and Multiscale Modelling

MAJC and CKM gratefully acknowledge support of EPSRC grant no. EP/N014642/1 (EPSRC Centre for Multiscale Soft Tissue Mechanics – With Application to Heart & Cancer).In this paper, we present two mathematical models related to different aspects and scales of cancer growth. The first model is a stochastic spatiotemporal model of both a synthetic gene regulatory network (the example of a three-gene repressilator is given) and an actual gene regulatory network, the NF- κB pathway. The second model is a force-based individual-based model of the development of a solid avascular tumour with specific application to tumour cords, i.e. a mass of cancer cells growing around a central blood vessel. In each case, we compare our computational simulation results with experimental data. In the final discussion section, we outline how to take the work forward through the development of a multiscale model focussed at the cell level. This would incorporate key intracellular signalling pathways associated with cancer within each cell (e.g. p53–Mdm2, NF- κB) and through the use of high-performance computing be capable of simulating up to 109 cells, i.e. the tissue scale. In this way, mathematical models at multiple scales would be combined to formulate a multiscale computational model.PostprintPeer reviewe